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VERSION:2.0
PRODID:-//wp-events-plugin.com//6.4.7.3//EN
TZID:Asia/Kolkata
X-WR-TIMEZONE:Asia/Kolkata
BEGIN:VEVENT
UID:43@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata;VALUE=DATE:20240531
DTEND;TZID=Asia/Kolkata;VALUE=DATE:20240602
DTSTAMP:20240323T082557Z
URL:https://cds.iisc.ac.in/events/symposium-on-big-data-algorithms-for-bio
logy-bdbio/
SUMMARY:Symposium on Big Data Algorithms for Biology (BDBio)
DESCRIPTION:Join us at the second annual Symposium on Big Data Algorithms f
or Biology (BDBio) on May 31-June 1\, 2024 at the Faculty Hall\, Main Buil
ding\, Indian Institute of Science.\n\nThe 2-day event will feature engagi
ng invited talks\, contributed talks\, poster presentations\, and panel di
scussions on various topics in computational biology. Registration / Abstr
act submission are open until April 20. Check more details on www.bdbio.in
\n\n
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:51@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240425T100000
DTEND;TZID=Asia/Kolkata:20240425T110000
DTSTAMP:20240417T072125Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-seminar-hall-
102-learning-deep-neural-networks-from-limited-and-imperfect-data/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS Seminar Hall # 102 "Learning Deep Ne
ural Networks From Limited and Imperfect Data"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
loquium\n\n\n\nSpeaker : Mr. Harsh Rangwani\n\nS.R. Number : 06-18-01-10-1
2-19-1-17477\n\nTitle :"Learning Deep Neural Networks From Limited and Imp
erfect Data"\nResearch Supervisor: Prof. Venkatesh Babu\nDate &\; Time
: April 25\, 2024 (Thursday) at 10:00 AM\nVenue : # 102 CDS Seminar Hall\n
\n\n\nABSTRACT\n\nDeep Neural Networks have demonstrated orders of magnitu
de improvement in capabilities over the years after AlexNet won the ImageN
et challenge in 2012. One of the major reasons for this success is the ava
ilability of large-scale\, well-curated datasets. These datasets (e.g.\, I
mageNet\, MSCOCO\, etc.) are often manually balanced across categories (cl
asses) to facilitate learning of all the categories. This curation process
is often expensive and requires throwing away precious annotated data to
balance the frequency across classes. This is because the distribution of
data in the world (e.g.\, internet\, etc.) significantly differs from the
well-curated datasets and is often over-populated with samples from common
categories. The algorithms designed for well- curated datasets perform su
boptimally when used to learn from imperfect datasets with long-tailed imb
alances and distribution shifts. For deep models to be widely used\, getti
ng away with the costly curation process by developing robust algorithms t
hat can learn from real-world data distribution is necessary. Toward this
goal\, we develop practical algorithms for Deep Neural Networks that can l
earn from limited and imperfect data present in the real world. This thesi
s is divided into four segments\, each covering a scenario of learning fro
m limited or imperfect data. The first part of the thesis focuses on Learn
ing Generative Models for Long-Tail Data\, where we mitigate the mode-coll
apse for tail (minority) classes and enable diverse aesthetic image genera
tions as head (majority) classes. In the second part\, we enable effective
generalization on tail classes through Inductive Regularization schemes\,
which allow tail classes to generalize as the head classes without enforc
ing explicit generation of images. In the third part\, we develop algorith
ms for Optimizing Relevant Metrics compared to the average accuracy for le
arning from long-tailed data with limited annotation (semi-supervised)\, f
ollowed by the fourth part\, which focuses on the effective domain adaptat
ion of the model to various domains with zero to very few labeled samples.
\n\nGenerative Models for Long-Tail Data. We first evaluate generative mod
els’ performance\, specifically variants of Generative Adversarial Netwo
rks (GANs) on long-tailed datasets. The GAN variants suffer from either mo
de-collapse or miss-class modes during generation. To mitigate this\, we p
ropose Class Balancing GAN with a Classifier in the Loop\, which uses a cl
assifier to asses the modes in generated images and regularizes GAN to pro
duce all classes equally. To alleviate the dependence on the classifier\,
following our observation that spectral norm explosion of Batch Norm param
eters is the major reason for mode collapse. We develop an inexpensive gro
up Spectral Regularizer (gSR) to mitigate the spectral collapse\, signific
antly improving the SotA conditional GANs (SNGAN and BigGAN) performance o
n long-tailed data. However\, we observed that class confusion was present
in the generated images due to norm regularization. In our latest work\,
NoisyTwins\, we factor the latent space as distinct Gaussian by design for
each class\, enforcing class consistency and intra-class diversity using
a contrastive approach (BarlowTwins). This helps to scale high-resolution
StyleGANs for ≥ 1000 class long-tailed datasets of ImageNet-LT and iNatu
ralist2019\, achieving state-of-the-art (SotA) performance.\n\nInducting R
egularization Schemes for Long-Tailed Data. While Data Generation is excit
ing for improving classification models on tail classes\, it often comes w
ith the cost of training an auxiliary GAN model. Hence\, a lightweight tec
hnique like enhancing loss weights (re-weighting) for tail classes while t
raining CNNs is practical to improve minority class performance. However\,
despite this\, the model only attains minima for the head class loss and
converges to saddle point for tail classes. We show that inducing inductiv
e bias of escaping saddles and converging to minima for tail classes\, usi
ng Sharpness Aware Minimization (SAM) significantly improves performance o
n tail classes. Further training Vision Transformer (ViT) for long-tail re
cognition is hard\, as they don’t have inductive biases like locality of
features\, which makes them data hungry. We propose DeiT-LT\, which intro
duces OOD and low-rank distillation from CNN to induce CNN-like robustness
into scalable ViTs for robust performance.\n\nSemi-Supervised Long-Tailed
Learning. The above methods work in supervised long-tail learning\, where
they avoid throwing off the annotated data. However\, the real benefit of
long-tailed methods could be leveraged when they utilize the extensive un
labeled data present (i.e.\, semi-supervised setting). For this\, we intro
duce a paradigm where we measure the performance using relevant metrics li
ke worst-case recall and recall H-mean on a held-out set\, and we use thei
r feedback to learn in a semi-supervised long-tailed setting. We introduce
Cost-Sensitive Self Training (CSST) generalizes self-training (e.g.\, Fix
Match\, etc.) based semi-supervised learning to long-tail settings with st
rong guarantees and empirical performance. The general trend these days is
to use self-supervised pre-training to obtain a robust model and then fin
e-tune it. In this setup\, we introduce SelMix\, an inexpensive fine-tunin
g technique to optimize the relevant metrics using pre-trained models. In
SelMix\, we relax the assumption that unlabeled distribution is similar to
the labeled\, making models robust to distribution shifts.\n\nEfficient D
omain Adaptation. The long-tail learning algorithms focus on limited data
setup and improving in-distribution generalization. Still\, for practical
usage\, the model must learn from imperfect data and perform well across v
arious domains. Toward this goal\, we develop Submodular Subset Selection
for Adversarial Domain Adaptation\, which carefully selects a few samples
to be labeled for maximally improving model performance in the target doma
in. To further improve the efficiency of the Adaptation procedure\, we int
roduce Smooth Domain Adversarial Training (SDAT)\, which converges to gene
ralizable smooth minima. The smooth minimum enables efficient and effectiv
e model adaptation across domains and tasks.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:49@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240418T150000
DTEND;TZID=Asia/Kolkata:20240418T160000
DTSTAMP:20240410T073116Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-18-ap
ril-2024-an-importance-sampling-in-n-sphere-monte-carlo-and-its-performanc
e-analysis-for-high-dimensional-integration/
SUMMARY:M.Tech Research: Thesis Defense: CDS: 18\, April 2024 "An importanc
e sampling in N-Sphere Monte Carlo and its performance analysis for high d
imensional integration"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Oral Examination\n\n\n\nSpeaker : Mr. Jawlekar Abhijeet Rajendra\n\nS.R. N
umber : 06-18-01-10-22-21-1-20128\n\nTitle : "An importance sampling in N-
Sphere Monte Carlo and its performance analysis for high dimensional integ
ration"\n\nThesis examiner : Prof. Manohar C.\, Dept. of Civil Engine
ering\, Indian Institute of Science.\nResearch Supervisor: Prof. Murugesan
Venkatapathi\nDate &\; Time : April 18\, 2024 (Thursday) at 03:00 PM\n
Venue : # 102 CDS Seminar Hall\n\n\n\nABSTRACT\n\nStatistical methods for
estimating integrals are indispensable when the number of dimensions (para
meters) become greater than ~ 10\, where numerical methods are unviable in
general. Well-known statistical methods like Quasi-Monte Carlo converge q
uickly only for problems with a small number of effective dimensions\, and
Markov Chain Monte Carlo (MCMC) methods incur a sharply increasing comput
ing effort with the number of dimensions 'n' that is bounded as O(n^5). Th
is bound on the dimensional scaling of computing effort in multiphase MCMC
is limited to domains with a given convex shape (determined by the limits
of integration). Note that the non-convexity and roughness of the boundar
ies of the domain are factors that adversely affect the convergence of suc
h methods based on a random walk in high dimensions.\n\nA different approa
ch to high-D integration using (1D) line integrals along random directions
coupled with a less-known volume transformation was suggested here at the
Institute by Arun et. al. This method called as N-sphere Monte Carlo (NSM
C) is agnostic to the shape and roughness of the boundary for any given di
stribution of extents of the domain from a reference point. While the dime
nsional scaling of computing in NSMC integration can be bound as O(n^3) fo
r any distribution of relative extents (and not a particular convex shape)
\, a similar bound does not exist for MCMC as any given extent distributio
n can represent numerous geometries where it may not converge. It was show
n earlier that when restricted to convex shapes where the extent density f
unctions become increasingly heavy tailed as 'n' increases\, the naive NSM
C may be more efficient than the multiphase MCMC only when n <\; ~100. T
his thesis has three contributions. 1) It is analytically shown that\, unl
ike MCMC\, the convergence of NSMC in the estimation of n-volume of a doma
in is not a necessary condition for its convergence in any other integrati
on over that domain. 2) A direct numerical comparison of the naive NSMC an
d the multiphase MCMC was performed for estimating n-volumes and different
types of integrands\, establishing this advantage in integration even ove
r typical convex domains when n <\; ~ 100. 3) A method for importance sa
mpling is suggested for NSMC with a demonstration of the improved performa
nce in higher dimensions for domains with heavy tailed extent density func
tions. In identifying and ensuring a local volume of interest is sampled a
dequately\, this method employs efficient sampling in high-D cones with a
target distribution.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:50@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240415T090000
DTEND;TZID=Asia/Kolkata:20240415T100000
DTSTAMP:20240412T113508Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-se
quence-alignment-to-cyclic-pangenome-graphs/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS: "Sequence Alignment to Cy
clic Pangenome Graphs"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n\n\nSpeaker : Ms. Jyotshna Rajput\n\nS.R. Number : 06
-18-01-10-22-21-1-19943\n\nTitle :"Sequence Alignment to Cyclic Pangenome
Graphs"\nResearch Supervisor: Dr. Chirag Jain\nDate &\; Time : April 15
\, 2024 (Monday) at 09:00 AM\n\nVenue : The Thesis Colloquium will be held
on MICROSOFT TEAMS\n\n \;\n\n\n\nABSTRACT\n\nThe growing availability
of genome sequences of several species\, including humans\, has created t
he opportunity to utilize multiple reference genomes for bioinformatics an
alyses and improve the accuracy of genome resequencing workflows. Graph-ba
sed data structures are suitable for compactly representing multiple close
ly related reference genomes. Pangenome graphs use a directed graph format
\, where vertices are labeled with strings\, and the individual reference
genomes are represented as paths in the graph. Aligning sequences (reads)
to pangenome graphs is fundamental for pangenome-based genome resequencing
. The sequence-to-graph alignment problem seeks a walk in the graph that s
pells a sequence with minimum edit distance from the input sequence. Howev
er\, exact algorithms for solving this problem are unlikely to scale due t
o the known hardness of this problem. Co-linear chaining is a well-studied
and commonly used heuristic alternative for quickly aligning reads to a g
raph. However\, the known chaining algorithms are restricted to directed a
cyclic graphs (DAGs) and are not trivially generalizable to cyclic graphs.
This limitation must be addressed because pangenome graphs often contain
cycles due to inversions\, duplications\, or copy number mutations within
the reference genomes.\n\nThis thesis presents the first practical formula
tion and algorithm for co-linear chaining on cyclic pangenome graphs. Our
algorithm builds upon the known chaining algorithms for DAGs. We propose a
novel iterative algorithm to handle cycles and provide rigorous proof of
correctness and runtime complexity. Our algorithm also exploits the domain
-specific small-width property of pangenome graphs. The proposed optimizat
ions enable our algorithm to scale to large human pangenome graphs. We imp
lemented our algorithm in C++ and referred to it as PanAligner (https://gi
thub.com/at-cg/PanAligner). PanAligner is an end-to-end long-read aligner
for pangenome graphs. We evaluated its speed and accuracy by aligning simu
lated long reads to a cyclic human pangenome graph comprising 95 haplotype
s. We achieved superior read mapping accuracy using PanAligner compared to
existing methods.\n\n\n\nALL ARE WELCOME
CATEGORIES:MTech Research Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:47@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240404T160000
DTEND;TZID=Asia/Kolkata:20240404T170000
DTSTAMP:20240327T102422Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-04-ap
ril-2024-intelligent-methods-for-cloud-workload-orchestration/
SUMMARY:M.Tech Research: Thesis Defense: CDS: 04\, April 2024 "Intelligent
Methods for Cloud Workload Orchestration"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nM.Tech Researc
h Thesis Defense\n\n\n\nSpeaker : Mr. Prathamesh Saraf Vinayak\n\nS.R. Num
ber : 06-18-01-10-22-21-1-19717\nTitle : "Intelligent Methods for Cloud Wo
rkload Orchestration."\nResearch Supervisor :Dr. J. Lakshmi\nDate &\; T
ime : April 04\, 2024 (Thursday)\, 04:00 PM\n\nVenue : The Thesis Défense
will be held on MICROSOFT TEAMS\n\nPlease click on the following link to
join the Thesis Defense:\n\nMS Teams link\n\n\n\nABSTRACT\n\nCloud workloa
d orchestration is pivotal in optimizing the performance\, resource utiliz
ation\, and cost-effectiveness of applications in data centers. As modern
businesses and IT operations are migrating their businesses to the cloud\,
understanding the dynamics of cloud data centers has become indispensable
. Often\, two perspectives play a pivotal role in workload orchestration i
n data centers. One is from the cloud provider side\, whose goal is to pro
vision as many applications as possible on the available resources\, bidin
g to SLA constraints and increasing return on investment. Others are from
the side of enterprises and individual customers\, often referred to as en
d users\, whose primary objective is to ensure application performance wit
h a reduced deployment cost. Containerization has gained popularity for de
ploying applications on public clouds\, where large enterprises manage num
erous applications through thousands of containers placed onto Virtual Mac
hines (VMs). While the need for cost-efficient placement in cloud data cen
ters is undeniable\, the complexities involved in achieving this goal cann
ot be understated. This problem is usually modeled as a multi-dimensional
Vector Bin-packing Problem (VBP). Solving VBP optimally is NP-hard and pra
ctical solutions requiring real-time decisions use heuristics. This work e
xplores the landscape of cloud data centers\, emphasizing the significance
of efficient bin packing in achieving optimal cost and resource utilizati
on. Traditional methods\, including heuristics and optimal algorithms\, fa
ce limitations in handling continuous request arrivals and the dynamic nat
ure of cloud workloads. Integer Linear Programming (ILP)\, which can provi
de optimal solutions for small problem sizes with tens of requests\, may t
ake minutes to hours to complete\, even at such scales. Moreover\, optimal
algorithms inherently demand perfect knowledge of all current and future
requests to be placed within the bins\, rendering them unsuitable for the
dynamic and often unpredictable online placement scenarios prevalent in cl
oud setups.\n\nTo address these challenges\, this work introduces a novel
approach to solving VBP through Reinforcement Learning (RL)\, trained on t
he historical container workload trace for an enterprise\, a.k.a CARL (Cos
t-optimized container placement using Adversarial Reinforcement Learning).
The proposed work evaluates the effectiveness of CARL in comparison to tr
aditional methods. CARL leverages historical container workload traces\, l
earning from a semi-optimal VBP solver while optimizing VM costs. The cont
ributions of this research extend beyond traditional methods\, providing i
nsights into the advantages and disadvantages of heuristics\, optimal algo
rithms\, and learning approaches. We trained and evaluated CARL on workloa
ds derived from realistic traces from Google Cloud and Alibaba for placing
10\,000 container requests onto over 8000 VMs. CARL is fast\, making plac
ement decisions for request sets with 124 containers per second within 65m
s onto 1000s of potential VMs. It is also efficient\, achieving up to 13.9
8% lower VM costs than baseline heuristics for larger traces. To push the
boundaries further\, we use the Mixture of Experts (MoE) strategy in CARL\
, wherein we use multiple experts who help CARL learn placement policies o
f various approaches combined. Including an MoE strategy enhances CARL's a
daptability to changes in workload distribution\, ensuring competitive per
formance in scenarios with skewed resource needs or inter-arrival times.\n
\n\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:48@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata;VALUE=DATE:20240404
DTEND;TZID=Asia/Kolkata;VALUE=DATE:20240406
DTSTAMP:20240402T184435Z
URL:https://cds.iisc.ac.in/events/15th-eecs-research-students-symposium/
SUMMARY:15th EECS Research Students Symposium
DESCRIPTION:15th EECS Research Students Symposium will be held on April 4t
h and 5th\, 2024. This annual event has served as an excellent forum for
interaction among IISc's students\, faculty members\, and other top resea
rchers from industry and academia. The symposium covers research topics ra
nging from theoretical computer science to power engineering.\n\nFor more
information on the schedule and speakers\, please visit https://eecs.iisc.
ac.in/EECS2024 or see the flyer below.\n\n[pdf-embedder url="https://cds.i
isc.ac.in/wp-content/uploads/EECS_Symp_2024_Flyer.pdf"].\n\n \;
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:44@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240321T113000
DTEND;TZID=Asia/Kolkata:20240321T123000
DTSTAMP:20240305T044838Z
URL:https://cds.iisc.ac.in/events/cds-research-seminar-development-of-ai-t
ools-in-liver-disease-and-transplantation-current-and-future-prospects/
SUMMARY:CDS Research Seminar: Development of AI tools in Liver Disease an
d Transplantation: Current and Future Prospects
DESCRIPTION:CDS Research Seminar\n\n\n\nTitle: Development of AI tools in
Liver Disease and Transplantation: Current and Future Prospects\nSpeaker:
Dr. Mamatha Bhat\, MD\, MSc\, PhD\nHepatologist &\; Co-Lead of Transpl
ant AI initiative (TAI)\, Ajmera Transplant Centre\nAssociate Professor\,
Division of Gastroenterology &\; Hepatology\nDirector\, Clinician-Scien
tist Training Program (CSTP)\, Department of Medicine\, University of Toro
nto\nPartnerships &\; Engagement Lead\, Temerty Centre for AI in Resear
ch &\; Education in Medicine (T-CAIREM)\nDate &\; Time: March 21\, 2
024 (Thursday) &\; 11:30 AM - 12:30 PM\nRoom No: CDS 102 (CDS Seminar H
all)\n\n\n\nAbstract:\n\nArtificial Intelligence (AI) tools have been incr
easingly applied to clinical questions in transplant medicine in recent ye
ars. As we continue to push the limits of transplantation\, with the older
age and complex comorbidities of candidates\, there are many challenges t
hroughout transplant medicine that must be better addressed. Various facto
rs affect liver transplant pathology and outcomes\, including sex\, ethnic
ity\, genetics\, BMI\, diabetes\, and immunosuppressive regimens. There ex
ist complex\, non-linear patterns in laboratory tests that must be conside
red in conjunction with the complex clinical variables to predict outcome.
Additionally\, electronic health record data\, imaging technologies\, his
tology\, and ‘omics data have continued to expand the types of data avai
lable. These complex data points\, their hidden patterns and interrelation
ships can be uniquely leveraged with the use of AI tools. Longitudinal cha
nges in these variables are also being examined to provide a continuous re
assessment of risk along the timeline. Applications of AI in transplant me
dicine include waitlist prioritization\, donor-recipient matching\, and sh
ort-term/long-term outcome prediction. In this talk\, I will go over these
considerations with respect to application of AI in transplant medicine.
I will additionally discuss integration of ‘omics data\, as well as pers
pectives regarding clinical deployment of AI tools.\n\nShort Biography:\n\
nDr. Mamatha Bhat is a Hepatologist and leads the Transplant AI initiative
at the University Health Network's Ajmera Transplant Centre\, and Associa
te Professor of Medicine at the University of Toronto. Dr. Bhat completed
her medical school and residency training at McGill University. She then c
ompleted a Transplant Hepatology fellowship at the Mayo Clinic in Rocheste
r\, Minnesota\, followed by a Canadian Institutes of Health Research (CIHR
) Fellowship for Health Professionals\, through which she completed a PhD.
\n\nThe goal of Dr. Bhat’s research program is to improve long-term outc
omes of liver transplantation through a precision medicine approach. Her p
rogram is unique in using tools of Artificial Intelligence with bioinforma
tics to personalize the care of liver transplant recipients based on an im
proved biological understanding of the liver and disease after transplant.
Her interdisciplinary program has been supported by the Canadian Institut
es for Health Research (CIHR)\, Canadian Donation and Transplant Research
Program (CDTRP)\, Natural Sciences and Engineering Research Council (NSERC
)\, Canadian Liver Foundation (CLF)\, among others. Dr. Bhat is Partnershi
p &\; Engagement Lead for the Temerty Centre for AI Research and Educat
ion in Medicine (T-CAIREM)\, Chair of the International Liver Transplant S
ociety Basic and Translational Science Research committee\; on the Executi
ve committee of the CDTRP\, and an Associate Editor for the American Journ
al of Transplantation. Dr. Bhat has also been the recipient of recognition
s such as the 2022 CIHR-INMD-CASL Early Career Researcher prize\, CASL Res
earch Excellence award and the 2021 American Society of Transplantation Ba
sic Science Career Development Award.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:46@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240311T100000
DTEND;TZID=Asia/Kolkata:20240311T110000
DTSTAMP:20240310T165024Z
URL:https://cds.iisc.ac.in/events/cds-seminar10211th-march-non-equilibrium
-plasma-and-its-use-in-combustion-a-modeling-perspec6ve/
SUMMARY:CDS Seminar@102:11th March: Non-Equilibrium Plasma and its Use in C
ombustion: A Modeling Perspec6ve
DESCRIPTION:Department of computational and Data Sciences\nDepartment semin
ar\n\n\n\nTitle: Non-Equilibrium Plasma and its Use in Combustion: A Mode
ling Perspec6ve\nSpeaker: Taaresh S. Taneja\, Department of Mechanical Eng
ineering\, University of Minnesota\, USA\nDate and time: 11 March 2024 (M
onday)\, 10:00 AM\nVenue: CDS 102\n\n\n\nAbstract:\n\n\nNon-equilibrium or
Low Temperature Plasma is a state of a gas which is characterized by a di
fference in the energies of the electrons and other heavy species of the
gas. Such a plasma typically constitutes of gas molecules\, a relatively
lower density of ions and rotational\, vibrational\, and electronic excite
d states\, neutral radicals\, along with free electrons. Non-equilibrium
plasmas can exist at various gas pressures ranging from 0.1 – 106 Pa an
d gas temperatures ranging from 100 – 10000 K. Across these wide range
of conditions\, the physical and chemical properties of the plasma can var
y substantially – which make them extremely useful in diverse technolog
ical areas such as semiconductor manufacturing\, water treatment\, medica
l equipment sterilization\, nanomaterial synthesis\, chemical reforming\,
combustion assistance\, etc. Between 104 - 106 Pa and 300 – 5000 K\, th
ese non-equilibrium plasma discharges can be used for assisting combustio
n. This assistance is provided majorly through two channels – gas heati
ng and chemical radical production. Both these channels can be used to ign
ite renewable and carbon-free fuels such as ammonia (NH3)\, which is very
difficult to burn\, and stabilize flames in challenging conditions such
as gas turbines\, scramjet\, and rocket combustors. Moreover\, the chemic
al pathways introduced by the non-equilibrium of the gas\, can also help t
o lower\, or completely prevent emissions from combustion\, such as unbur
ned hydrocarbons\, soot\, CO2\, CO and NOx. High fidelity computational s
imulations (DNS / LES) of non-equilibrium “plasma assisted combustion
” (PAC) face various constraints due to the wide-ranging temporal (10-15
– 10-2 s) and spatial (10-6 – 10-1 m) scales of this problem\, which
make the system of governing equations very stiff. Furthermore\, the hig
hly coupled interaction of electrostatics\, plasma chemistry\, combustion
chemistry\, and turbulent flow renders PAC its multi-physics nature. Thi
s talk will provide an overview of the governing physics\, the mathematic
al formulation of different models\, and a few technological applications
of non- equilibrium plasma assisted combustion.\n\nBiography:\n\nTaaresh
Taneja is a 5th year PhD candidate at the University of Minnesota (UMN)\,
Twin Cities\, who is currently focused on modeling non-equilibrium plasma
assisted combustion. He works with Prof. Suo Yang\, in the Computational R
eactive Flow and Energy Laboratory (CRFEL) at UMN. For his research\, he h
as received the UMII MNDrive Fellowship\, NSF INTERN Supplemental Funding
Opportunity and the UMN Doctoral Dissertation Fellowship\, along with othe
r travel grants for presenting his work at conferences. Taaresh has also i
nterned at the National Renewable Energy Laboratory\, Colorado and at the
Sandia National Laboratory\, California during his PhD. Before joining UMN
for his PhD in 2019\, Taaresh worked as a CFD engineer at Johnson Control
s India Pvt. Ltd.\, Pune (2017 - 2019). He received his B.E. (Hons) in Mec
hanical Engineering from BITS Pilani\, Goa Campus in 2017.\n\n\n\nHost Fac
ulty: Dr. Konduri Aditya\n\n\n\nAll are welcome
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:45@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240311T090000
DTEND;TZID=Asia/Kolkata:20240311T100000
DTSTAMP:20240306T051313Z
URL:https://cds.iisc.ac.in/events/seminar-cds-online-11th-march-computatio
nal-profiling-of-microbes-in-environments-pitfalls-controversies-and-solut
ions/
SUMMARY:{Seminar} @ CDS: Online : 11th March: "Computational profiling of m
icrobes in environments: pitfalls\, controversies\, and solutions"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSPEAKER : Dr. Jim Shaw\, Department of Mathematics at the
University of Toronto\nTITLE : "Computational profiling
of microbes in environments: pitfalls\, controversies\, and solutions"\nDa
te &\; Time : March 11\, 2024\, 09:00 AM\n\nVenue
: The Seminar will be held on MICROSOFT TEAMS. Please click on the fol
lowing link to join the Seminar:\n\nhttps://teams.microsoft.com/l/meetup-j
oin/19%3ameeting_NGZkMjVhNGYtNzZkMS00OGE3LTljNzItOTdhZTM1ZDMxNTdm%40thread
.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c
%22Oid%22%3a%220df9a69b-76ed-46bd-a19d-e01995f351a6%22%7d\n\n\n\nABSTRACT\
n\nDNA sequencing technology now allows researchers to detect the microorg
anisms in your microbiomes\, such as your gut\, skin\, and even blood. Com
putational methods for analyzing this data have enabled insights into the
relationship between our microbiomes and human health. DNA sequencing data
\, however\, is notoriously messy\; computational methods are also imperfe
ct.\n\nIn this talk\, I will first discuss how extremely subtle algorithmi
c issues have led to controversy in the biological literature\, including
suspicion about a landmark paper in the study of cancer microbiomes. After
giving an overview of the computational issues surrounding this field\, I
will present our new computational method for profiling microbiomes calle
d sylph (Shaw and Yu\, 2023\, bioRxiv). While sylph has high accuracy and
is >\; 30 times faster than previous methods\, I will focus on why our
new method works. More generally\, I will discuss the process of designing
practical algorithms for complicated biological data\, which I believe is
a treacherous but extremely rewarding process.\n\nBIOGRAPHY\n\nJim Shaw i
s a PhD candidate in the Department of Mathematics at the University of To
ronto under the supervision of Prof. Yun William Yu. His research centers
on developing efficient software and methods for computational biology fro
m a mathematical point of view\, with a heavy emphasis on bridging the gap
between theory and biological discovery. Jim is supported by an NSERC CGS
-D\, and prior to his PhD\, he received a BASc in Engineering Physics and
Mathematics from the University of British Columbia.\n\nHost Faculty: Dr.
Chirag Jain\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:42@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240306T103000
DTEND;TZID=Asia/Kolkata:20240306T113000
DTSTAMP:20240227T061300Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-06-ma
rch-2024-a-co-kurtosis-tensor-based-featurization-of-chemistry-for-scalabl
e-combustion-simulations/
SUMMARY:M.Tech Research: Thesis Defense: CDS: 06\, March 2024 "A co-kurtosi
s tensor based featurization of chemistry for scalable combustion simulati
ons"
DESCRIPTION:\n\nDEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nM.Tech Res
earch Thesis Defense\n\n\n\nSpeaker : Mr. Dibya Jyoti Naya
k\nS.R. Number : 06-18-01-10-22-21-1-19747.\nTitle
: "A co-kurtosis tensor based featurization of chemistry for scalable c
ombustion simulations."\nResearch Supervisor : Dr. Konduri Aditya\nDate &a
mp\; Time : March 06\, 2024 (Wednesday)\, 10:30 am\nVenue
: Room No. 102 (CDS Seminar Hall)\n\n\n\n\nABSTRACT\nIdentifying l
ow-dimensional representations of the thermo-chemical state space for turb
ulent reacting flow systems is vitally important\, primarily to significan
tly reduce the computational cost of device-scale combustion simulations.
Moreover\, these simulations are often performed to gain fundamental insig
hts into the inception of extreme/anomalous events such as flashbacks\, fl
ame extinction\, blow-offs\, thermoacoustic instabilities\, etc.\, which c
an have detrimental effects on combustion efficiency and engine performanc
e. With the scale of scientific investigations ever increasing\, the need
for robust anomaly detection methods becomes increasingly critical for jud
icious steering of these simulations and also aiding smooth operations of
practical engines. Recent studies have shown that the fourth-order joint s
tatistical moment tensor\, i.e.\, co-kurtosis\, effectively captures anoma
lies/outliers in scientific data. Accordingly\, the primary objective of t
his work centers around leveraging the unique properties of the co-kurtosi
s tensor to drive low-cost and scalable combustion simulations and build r
obust algorithms for extreme event detection. Particularly\, the first par
t of this work develops tools for dimensionality reduction for chemistry\,
while the second part focuses on employing a co-kurtosis based detection
algorithm for capturing extreme events such as flame instabilities in hydr
ogen-fired reheat burners relevant to sequential gas turbine engines.\n\nT
o obtain low-dimensional manifolds (LDMs) that describe the original therm
o-chemical state\, principal component analysis (PCA) and its variants are
widely employed. An alternative dimensionality reduction technique that f
ocuses on higher order statistics\, co-kurtosis PCA (CoK-PCA)\, has been s
hown to provide an optimal LDM for effectively capturing the stiff chemica
l dynamics associated with spatiotemporally localized reaction zones. Whil
e its effectiveness has only been demonstrated based on a priori analyses
with linear reconstruction\, in this work\, we employ nonlinear techniques
to reconstruct the full thermo-chemical state and evaluate the efficacy o
f CoK-PCA compared to PCA. Specifically\, we combine a CoK-PCA-/PCA-based
dimensionality reduction (encoding) with an artificial neural network (ANN
) based reconstruction (decoding) and examine\, a priori\, the reconstruct
ion errors of the thermo-chemical state. We employ three combustion test c
ases representing varying degrees of complexity in the geometrical domain\
, combustion regimes\, ignition kinetics\, etc.\, to assess CoK-PCA/PCA co
upled with ANN-based reconstruction. Results from the analyses demonstrate
the robustness of the CoK-PCA based LDM with ANN reconstruction in accura
tely capturing the data\, specifically from the reaction zones.\n\nHydroge
n's highly reactive and diffusive nature towards decarbonization is prone
to flashbacks\, flame instabilities\, and thermoacoustic instabilities. Fo
r example\, in the case of reheat burners of hydrogen-fired sequential gas
turbine engines\, intermittent temperature and pressure fluctuations resu
lt in flame instabilities\, such as intermittent autoignition events at of
f-design locations that can adversely impact the engine's performance. To
address this issue\, we develop an unsupervised learning methodology based
on the co-kurtosis tensor to detect the early onset of spontaneous igniti
on kernels in lean premixed hydrogen combustion at vitiated conditions. Th
e accuracy of the model is evaluated for various ignition test cases.\n\n\
n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:40@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240301T150000
DTEND;TZID=Asia/Kolkata:20240301T160000
DTSTAMP:20240221T085040Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-sc
alable-read-alignment-algorithm-for-cyclic-pangenome-graphsscalable-read-a
lignment-algorithm-for-cyclic-pangenome-graphs/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : "Scalable Video Data Man
agement and Visual Querying for Autonomous Camera Networks"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n\n\nSpeaker : Ms. Bharati Khanijo\n\nS.R. Number : 06
-18-02-10-12-19-1-17219\n\nTitle :"Scalable Video Data Management and Visu
al Querying for Autonomous Camera Networks"\nResearch Supervisor: Prof. Yo
gesh Simmhan\nDate &\; Time : March 01\, 2024 (Friday) at 03:00 PM\nVen
ue : # 102 CDS Seminar Hall\n\n\n\nABSTRACT\n\nVideo data has been histori
cally known for its unstructured nature and rich semantic content but also
for scalability issues in terms of storage and analytics. Mobile aerial p
latforms like drones capture such videos across space and time. Advances i
n computer vision and deep learning enable automatic extraction of rich se
mantic information from video data\, leading to applications where the sto
red video data can be used to study and analyze the world retrospectively
and automatically. However\, recent research has highlighted the compute-i
ntensive nature of such Deep Neural Network (DNN) models\, e.g.\, for accu
rate object detection\, leading to high computing costs that limits their
applicability for brute-force analysis of all historical videos. Also\, an
efficient design of such applications often requires co-analysis of video
data along with associated geospatial and temporal metadata\, which is a
challenge.\n\nWe propose a geospatial-temporal video query system with sup
port for semantic queries for drone videos\, extending an existing spatial
-temporal database and contemporary object detection models. We develop a
heuristic to enable better reuse of semantic object detections obtained fr
om different configurations (object detection model and its input resoluti
on) . The system further motivates the need for optimizations for retrospe
ctive semantic analysis and storage for drone videos\, which is addressed
by our novel DDownscale method and the associated ingest pipeline.\n\nPrio
r optimizations on semantic querying over video data focus on static camer
as from city-scale traffic/surveillance camera networks\, often leveraging
the spatial and temporal characteristics of associated videos\, which are
absent in videos recorded by mobile drone cameras. We specifically focus
on two such characteristics of drone videos. One is that drone videos have
shorter durations\, unlike those captured by static cameras. Another is t
hat there can be large variations in the level of detail of information ca
ptured across a fleet of drone cameras due to differences in the resolutio
n of the camera\, the altitude\, and the orientation from which the videos
were captured.\n\nSpecifically\, we address the need to intelligently sca
le-down the spatial resolution of videos to reduce the video storage costs
and semantic query/inferencing time. However\, conventional methods of ma
nual or profiling-based estimation of the ideal scaling ratio are compute-
intensive and/or time consuming for such heterogeneous feeds. We propose D
Downscale\, a novel method to dynamically select the downscale factor for
a video by utilizing the information on the object size in the video. We m
odel the downscale factor and associated drop in relative recall due to do
wnscaling as a function of object size in the downscaled video and demonst
rated that for a given DNN model and class of interest\, DDownscale genera
lizes well to the evaluated datasets. A DDownscale inequality between the
relative recall drop and the hyper-parameters of the method is derived. Th
is satisfies 98% of the dynamically downscaled videos across datasets\, ob
jects of interest and parameters. The algorithm achieve over 19% reduction
in total object detection time and 24% reduction in storage on average co
mpared to the baseline of storing/inferencing at the original resolution \
, for different user-specified target reduction in recall values ranging f
rom 1--30%\, and 96% of the downscaled videos are within the target recall
drop.\n\nA simpler specification at the time of ingest of target level of
detail (average ground spatial distance) captured in the video and the ha
rmonic mean of relative recall drop for the class of smallest object of in
terest and selected object detection model was derived using the above mod
eling to aid in the selection of a target level of detail. Additionally\,
we develop an ingest pipeline that reduces the time to ingest drone videos
using this dynamically downscaling method over heterogeneous edge acceler
ators\, and reduce the average turnaround time to ingest data from multipl
e clients by ~ 66%\, despite the downscaling time overhead\, compared to u
ploading original resolution video without downscaling.\n\n\n\nALL ARE WEL
COME
CATEGORIES:Events,MTech Research Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:39@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240227T140000
DTEND;TZID=Asia/Kolkata:20240227T150000
DTSTAMP:20240216T092452Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-27th-february-towards-se
cure-interpretable-and-scalable-machine-learning-applications-in-cyber-phy
sical-systems/
SUMMARY:{Seminar} @ CDS: #102 :27th February: "Towards Secure\, Interpretab
le\, and Scalable Machine Learning Applications in Cyber-Physical Systems"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Dr. Shailja Thakur\, Postdoctoral fellow\, New York\
n\nTitle : "Towards Secure\, Interpretable\, and Scalable Machine Learning
Applications in Cyber-Physical Systems"\n\nDate &\; Time : February 27
\, 2024\, 02:00 PM\n\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\n
In a world increasingly reliant on Cyber-Physical Systems (CPS)\, there ar
e critical challenges associated with the integration of complex software
and hardware. The enormous and diverse nature of data\, alongside pressing
security and privacy concerns\, demands innovative solutions. My work aim
s to enhance the intelligence of CPS through AI\, aiming for systems that
are not only self-aware but also capable of adapting in real-time to chang
ing environments. To that end\, my work has spanned the automotive\, energ
y\, and hardware sectors\, delivering practical solutions engineered along
side industry partners. I have made significant strides in enhancing secur
ity in automotive systems and have pioneered tools for deciphering the dec
ision-making processes of machine learning models. In the realm of hardwar
e design\, I am exploring the potentials of Large Language Models (LLMs) t
o automate and optimize the process\, reducing human error and increasing
efficiency. In the future\, I want to expand upon the challenges and scope
of applying generative AI in CPS for developing time-efficient\, scalable
\, safe and transparent real-world applications.\n\nBIOGRAPHY\n\nI am a po
stdoctoral fellow currently at New York University in the Tandon School of
Engineering within the Department of Electrical and Computer Engineering
and the Center of Cyber Security with Professors Ramesh Karri and Professo
r Siddharth Garg. My research interests span the field of cybersecurity\,
with a particular focus on the application of language modelling in embedd
ed systems\, LLM attributions\, safety\, and privacy. Shailja received her
Ph.D from the University of Waterloo and a masters in Computer Science an
d Engineering from IIIT Delhi.\n\nHost Faculty: Dr. Danish Pruthi\n\n\n\nA
LL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:41@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240226T113000
DTEND;TZID=Asia/Kolkata:20240226T123000
DTSTAMP:20240226T055854Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-26th-february-domain-ada
ptation-for-fair-and-robust-computer-vision/
SUMMARY:{Seminar} @ CDS: #102 :26th February: "Domain Adaptation for Fair a
nd Robust Computer Vision."
DESCRIPTION:Department of Computational and Data Sciences\n\nCDS / KIAC Sem
inar\n\n\n\nSpeaker : Mr. Tarun Kalluri\, University of California San Die
go.\n\nTitle : "Domain Adaptation for Fair and Robust Computer Vision."\n\
nDate &\; Time : February 26\, 2024\, 11:30 AM\n\nVenue : # 102\, CDS S
eminar Hall\n\n\n\nABSTRACT\n\nWhile recent progress significantly advance
s the state of the art in computer vision across several tasks\, the poor
ability of these models to generalize to domains and categories under-repr
esented in the training set remains a problem\, posing a direct challenge
to fair and inclusive computer vision. In my talk\, I will talk about my r
ecent efforts towards improving generalizability and robustness in compute
r vision using domain adaptation. First\, I will talk about our work on sc
aling domain adaptation to large scale datasets using metric learning. Nex
t\, I will introduce our new dataset effort called GeoNet aimed at benchma
rking and developing novel algorithms towards geographical robustness in v
arious vision tasks. Finally\, I will talk about the latest research study
ing the role of language supervision to improve adaptation of visual model
s to new domains.\n\nBIOGRAPHY\n\nTarun Kalluri is a fifth year PhD studen
t at UC San Diego in the Visual Computing Group. Prior to that\, he gradua
ted with a bachelors from Indian Institute of Technology\, Guwahati and wo
rked as a data scientist in Oracle. His research interests lie in label an
d data efficient learning from images and videos\, domain adaptation and i
mproving fairness in AI. He is a recipient of IPE PhD fellowship.\n\nHost
Faculty: Prof. Venkatesh Babu\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:36@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240215T143000
DTEND;TZID=Asia/Kolkata:20240215T154500
DTSTAMP:20240209T155828Z
URL:https://cds.iisc.ac.in/events/cds-seminar-systemsai-research-microsoft
/
SUMMARY:[CDS Seminar] Systems+AI Research @ Microsoft
DESCRIPTION:Department of Computational and Data Sciences (CDS) Seminar\n\n
\n\nTitle: Systems + AI Research @ Microsoft\n\nSpeakers: Saravan Rajmohan
\, Chetan Bansal\, Anjaly Parayil and Mayukh Das\, Microsoft\n\nDate/Time:
Thu Feb 15\, 230-345PM\n\nVenue: SERC Auditorium 4th Floor\n\n\n\nAbstrac
t: At Microsoft\, we operate one of the largest productivity clouds and we
need to keep pace with paradigm shifts such as the massive growth in AI w
orkloads\, sustainability push\, the need for self-managing cloud environm
ents and the complex challenges that arise out of its sheer scale. To solv
e these challenges\, at M365 Research group in Microsoft\, we have built a
cross-domain research team focusing on applied research on “ML for Syst
ems” to bring a step function improvement in Cloud Efficiency and Reliab
ility. The group comprises of 30+ PhD Researchers\, Research Fellows and I
nterns in Bangalore (co-located with MSR India)\, Redmond and Beijing. We
aim to partner with top research institutions to drive innovation and leve
rage the immense scientific knowledge and expertise to bring new ideas int
o practice. Our goal is to explore all the dimensions from collaborative r
esearch with faculty members\, to establishing knowledge sharing seminars
and building a long-term talent pipeline. You can read more about our rese
arch group here: https://aka.ms/systems-innovation.\n\nBios:\n\n Saravan
Rajmohan is a Partner Director at Microsoft\, leading the M365 Research g
roup focused on AI and Applied Research. He oversees diverse teams in the
US\, Latin America\, UK\, India\, and China\, conducting groundbreaking re
search in Systems Innovation and Privacy Preserving Machine Learning. The
group develops advanced algorithms and hardware innovation to enhance M365
infrastructure and services\, collaborating closely with Microsoft Resear
ch labs. They drive AIOps Cloud Intelligence research\, improving efficien
cy and reliability across M365 services. His Efficient AI team optimizes g
enerative AI scenarios through cross-layer optimization and collaboration\
, while privacy-preserving research ensures confidentiality in ML systems\
, playing a pivotal role in AI privacy policies.\n Chetan Bansal is a Pri
ncipal Research Manager at Microsoft leading the AIOps and Cloud Intellige
nce research. His research is focused on improving Cloud Efficiency and Re
liability through AI and data-driven methods. His research has had a signi
ficant impact on Cloud Efficiency and Developer Productivity at Microsoft
with millions of dollars of savings. He has published 30+ papers in top in
ternational conferences like ICSE\, FSE\, NSDI\, KDD and filed 15+ patents
. His research has been recognized with best paper awards and nominations
in conferences such as SoCC\, FMCAD and ICSE. Prior to Microsoft\, his wor
k on Formal verification of Web Protocols was recognized by awards from Fa
cebook\, Mozilla and Google.\n Anjaly Parayil is a Senior Researcher at M
365 Research leading applied research at the intersection of efficiency an
d reliability of cloud services. In particular\, she works on data drive
n optimizations to ensure continuous availability of cloud services as wel
l as workload aware techniques for improving the efficiency of Cloud infra
structure. Before joining Microsoft\, Anjaly was a Post Doc with the Compu
tational and Information Sciences Directorate at the US Army Research Labo
ratory where she focused on Reinforcement Learning and Bayesian Inferencin
g. Anjaly completed her graduate studies at the Department of Aerospace En
gineering at IISc with her thesis focusing on uncertain systems and multi-
agent control\, for which she received Prof. A. K. Rao Medal for the best
Ph.D. Thesis. Anjaly has authored 25+ publications in Artificial intellige
nce\, control systems and optimization.\n Mayukh Das is a Senior Research
er at Microsoft driving applied AI research for Cloud Efficiency. In par
ticular\, he works on varied decision-making problems for configuration tu
ning for performance optimization of cloud services\, for capacity provisi
oning\, for power and energy optimization\, and\, operational efficiency o
f ML workloads. He completed his PhD from UT Dallas and his thesis work wa
s focused on Guided Reinforcement Learning and Probabilistic Modeling in N
oisy domains. Prior to Microsoft he was at Samsung Research solving Edge-A
I problems. He serves on the program committee of various conferences incl
uding AAAI\, ICML\, NeurIPS\, SDM etc. and has served as a track chair at
CODS-COMAD ‘24. Mayukh has authored 25+ publications in AI/ML and holds
7+ patents.\n\n\n\n\nHost: Yogesh Simmhan\, CDS\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:37@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240213T140000
DTEND;TZID=Asia/Kolkata:20240213T150000
DTSTAMP:20240209T160037Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-13th-february-on-using-m
achine-learning-techniques-for-the-numerical-solution-of-convection-diffus
ion-problems/
SUMMARY:{Seminar} @ CDS: #102 :13th February: "On Using Machine Learning Te
chniques for the Numerical Solution of Convection-Diffusion Problems"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Prof. Dr. Volker John\, Berlin Germany\n\nTitle : "O
n Using Machine Learning Techniques for the Numerical Solution of Convecti
on-Diffusion Problems"\n\nDate &\; Time : February 13\, 2024\, 02:00 PM
\n\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\nIn the convection-
dominated regime\, solutions of convection-diffusion problems usually poss
esses layers\, which are regions where the solution has a steep gradient.
It is well known that many classical numerical discretization techniques f
ace difficulties when approximating the solution to these problems.\n\nMan
y stabilized discretizations of steady-state convection-diffusion equation
s lead to numerical solutions with notable spurious oscillations in a vici
nity of layers. Using discontinuous Galerkin methods offers the possibilit
y to reduce such oscillations effectively with post-processing techniques\
, so-called slope limiters. In the first part of the talk\, several of the
se techniques from the literature and improvements proposed in [1\, 2] wil
l be discussed and numerical assessments will be presented. Then the quest
ion will be studied whether a feed forward neural network (multilayer perc
eptrons)\, which is trained on the basis of these techniques\, is likewise
able to limit spurious oscillations\, see [3].\n\nIn recent years\, physi
cs-informed neural networks (PINNs) for approximating the solution to (ini
tial-)boundary value problems received a lot of interest. In the second pa
rt of this talk\, various loss functionals for PINNs are studied that are
novel in the context of PINNs and are especially designed for convectiondo
minated convection-diffusion problems. They are numerically compared to th
e vanilla and a hp-variational loss functional from the literature based o
n two benchmark problems whose solutions possess different types of layers
. It is observed in [4] that the best novel loss functionals reduce the L
2 (Ω) error by 17.3% for the first and 5.5% for the second problem compa
red to the methods from the literature.\n\nThis talk presents joint work w
ith Derk Frerichs–Mihov and Linus Henning\n\nReferences\n\n[1] D. Freric
hs\, V. John\, On reducing spurious oscillations in discontinuous Galerkin
(DG) methods for steady-state convection–diffusion equations\, Journal
of Computational and Applied Mathematics 393 (2021) 113487. doi:10.1016/j.
cam.2021.113487.\n\n[2] D. Frerichs-Mihov\, V. John\, On a technique for r
educing spurious oscillations in DG solutions of convection–diffusion eq
uations\, Applied Mathematics Letters 129 (2022) 107969. doi:10.1016/j.aml
.2022.107969.\n\n[3] D. Frerichs-Mihov\, L. Henning\, V. John\, Using deep
neural networks for detecting spurious oscillations in discontinuous Gale
rkin solutions of convection-dominated convection-diffusion equations\, J.
Sci. Comp. 97 (2023)\, Article 36. doi.org/10.1007/s10915-023-02335-x.\n\
n[4] D. Frerichs-Mihov\, L. Henning\, V. John\, On loss functionals for ph
ysicsinformed neural networks for convection-dominated convection-diffusio
n problems\, WIAS Preprint 3063\, submitted\, 2023 doi.org/10.20347/ WIAS.
PREPRINT.3063.\n\nBIOGRAPHY\nUniv.-Prof. Dr. Volker John - Head of Researc
h Group Numerical Mathematics and Scientific Computing Weierstrass Institu
te for Applied Analysis and Stochastics Mohrenstr. 39 10117 Berlin\, Germa
ny\nMore details about Prof. Volker John is available at https://www.wias-
berlin.de/people/john/\n\nHost Faculty: Prof. Sashikumaar Ganesan\n\n\n\nA
LL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:38@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240213T113000
DTEND;TZID=Asia/Kolkata:20240213T123000
DTSTAMP:20240209T160253Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-13th-february-large-scal
e-real-space-finite-difference-based-electronic-structure-calculations/
SUMMARY:{Seminar} @ CDS: #102 :13th February: "Large-scale real-space finit
e-difference based electronic structure calculations"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Dr. Abhiraj Sharma\, Postdoctoral Researcher\, LBNL\
, California\, U.S.A.\n\nTitle : "Large-scale real-space finite-difference
based electronic structure calculations"\n\nDate &\; Time : February 1
3\, 2024\, 11:30 AM\n\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\
nElectronic structure methods are widely employed for understanding the qu
antum mechanics driven behavior of material systems including but not limi
ted to flexo electricity\, spintronics\, piezoelectricity\, and thermal co
nductivity. Among these methods\, Kohn-Sham density functional theory (DFT
) has emerged as the cornerstone of material's research owing to its high
accuracy-to-cost ratio in comparison to other such ab initio methods. Desp
ite significant advancements\, Kohn-Sham calculations are still very expen
sive which limits the length and time scales accessible to first-principle
s investigation.\n\nIn this talk\, I will present on the recent developmen
ts in real-space finite-difference DFT resulting in a significant reductio
n in the computational cost and wall times associated with Kohn-Sham calcu
lations\, opening avenues for studying material systems considered intract
able before. In particular\, we have achieved more than an order of magnit
ude speedup in comparison to state-of-the-art DFT implementations by utili
zing the computational locality in finite-difference method\, concepts of
symmetry\, and high performance hybrid computing architectures. I will als
o discuss our recent efforts on accelerating the quantum molecular dynamic
s (QMD) simulations by incorporating a kernel method based machine-learned
force field (MLFF) scheme trained in an online manner\, resulting in more
than two orders of magnitude speedups. Finally\, I will discuss the compu
tational bottlenecks associated with calculating higher order properties o
f material systems including superconductivity and thermal conductivity an
d our ongoing work on developing a large-scale real-space formulation of d
ensity functional perturbation theory (DFPT) to overcome it\, opening aven
ues for a plethora of new applications with technological implications.\n\
nBIOGRAPHY\n\nAbhiraj Sharma received his B.Tech degree in Civil Engineeri
ng from IIT Roorkee in 2016. Afterwards\, he completed his M.S. and PhD fr
om Georgia Institute of Technology in 2022. Currently\, he is working as a
Postdoctoral Researcher in the Physical and Life Sciences division at Law
rence Livermore National Lab. His research is in the broad area of materia
l physics and mechanics with the focus on the development of mathematical
and computational tools to enable the first-principles study of mechanics
in material which can potentially lead to the discovery of materials with
fascinating properties.\n\nHost Faculty: Dr. Phani Motamarri\n\n\n\nALL AR
E WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:35@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240207T100000
DTEND;TZID=Asia/Kolkata:20240207T110000
DTSTAMP:20240207T172508Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-a-scalable-as
ynchronous-discontinuous-galerkin-method-for-massively-parallel-pde-solver
s/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS : "A scalable asynchronous discontin
uous-Galerkin method for massively parallel PDE solvers."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
loquium\n\n\n\n\n\n\n\n\n\n\n\nSpeaker : Mr. Shubham Kumar Goswami\n\nS.
R. Number : 06-18-00-10-12-19 -1-17224\n\nTitle : "A scalable asynchro
nous discontinuous-Galerkin method for massively parallel PDE solvers "\n\
nResearch Supervisor: Dr. Konduri Aditya\nDate &\; Time : February
07\, 2024 (Wednesday) at 10:00 AM\nVenue : The Thesis Colloquium will b
e held on HYBRID Mode\n# 102 CDS Seminar Hall /MICROSOFT TEAMS.\n\n\n\n\nP
lease click on the following link to join the Thesis Colloquium:\n\nMS T
eams link\n\n\n\n\n\n\nABSTRACT\n\n\n\n\n\n\n\n\n\nAccurate simulations of
turbulent flows in computational fluid dynamics (CFD) are crucial for com
prehending numerous complex phenomena in engineered systems and natural pr
ocesses. These flows are governed by nonlinear partial differential equati
ons (PDEs)\, which are approximated as algebraic equations and solved usin
g PDE solvers. However\, the complexity of turbulence makes these simulati
ons computationally expensive\, necessitating the use of massively paralle
l supercomputers. While advancements such as hardware-aware computing\, fa
ult tolerance\, and overlapping computation and communication have improve
d solver scalability\, achieving efficient performance at extreme scales r
emains a challenge owing to the communication and synchronization overhead
. To address this issue\, an asynchronous computing approach was introduce
d that relaxed communication and synchronization at a mathematical level\,
allowing PEs to operate independently regardless of the status of message
s\, potentially decreasing communication overhead and enhancing scalabilit
y. This approach has been developed specifically for finite difference sch
emes\, which are widely used but not ideal for complex geometries and unst
ructured meshes. The objective of this work is to develop an asynchronous
discontinuous-Galerkin method that can provide high-order accurate solutio
ns for various flow problems on unstructured meshes and demonstrate its sc
alability.\n\n\n\n\nBased on the asynchronous computing approach\, several
PDE solvers have been developed that use high-order asynchrony-tolerant f
inite difference schemes for spatial discretization to simulate reacting a
nd non-reacting turbulent flows\, achieving significant improvements in sc
alability. However\, for time integration\, most of them used either multi
-step Adams-Bashforth schemes\, which possess poor stability\, or multi-st
age Runge-Kutta (RK) schemes with an over-decomposed domain that necessita
tes larger message sizes for communication and redundant computations. In
this work\, we propose a novel method to couple asynchrony-tolerant and lo
w-storage explicit RK (LSERK) schemes to solve time-dependent PDEs with re
duced communication efforts. We developed new asynchrony-tolerant schemes
for ghost or buffer point updates that are necessary to maintain the desir
ed order of accuracy. The accuracy of this method has been investigated bo
th theoretically and numerically using simple one-dimensional linear model
equations. Thereafter\, we demonstrate the scalability of the proposed nu
merical method through three-dimensional simulations of decaying Burgers
’ turbulence performed using two different asynchronous algorithms: comm
unication-avoiding and synchronization-avoiding algorithms. Scalability st
udies up to 27\,000 cores yielded a speed-up of up to 6× compared to a ba
seline synchronous algorithm.\n\n\n\n\nIn recent years\, the discontinuous
Galerkin (DG) method has received broad interest in developing PDE solver
s\, particularly for nonlinear hyperbolic problems\, due to its ability to
provide high-order accurate solutions in complex geometries\, capture dis
continuities\, and exhibit high arithmetic intensity. However\, the scalab
ility of DG-based solvers is hindered by communication bottlenecks that ar
ise at extreme scales. In this work\, we introduce the asynchronous DG (AD
G) method\, which combines the benefits of the DG method with asynchronous
computing by relaxing the need for data communication and synchronization
at the mathematical level to overcome communication bottlenecks. The prop
osed ADG method ensures flux conservation and effectively addresses challe
nges arising from asynchrony. To assess its stability\, we employ Fourier-
mode analysis to examine the dissipation and dispersion behavior of fully-
discrete DG and ADG schemes with the Runge-Kutta (RK) time integration sch
emes across the entire range of wavenumbers. Furthermore\, we present an e
rror analysis within a statistical framework\, which demonstrates that the
ADG method with standard numerical fluxes achieves at most first-order ac
curacy. To recover accuracy\, we derived asynchrony-tolerant (AT) fluxes t
hat utilize data from multiple time levels. Finally\, extensive numerical
experiments are conducted to validate the performance and accuracy of the
ADG-AT scheme for both linear and nonlinear problems.\n\n\n\n\nWith the de
velopment of the asynchronous discontinuous-Galerkin (ADG) method\, we fin
ally put our focus on implementing and evaluating its performance in solvi
ng hyperbolic equations with shocks/discontinuities. To achieve this\, we
chose a highly scalable DG solver for compressible Euler equations from de
al.II\, which is one of the widely used open-source finite element librari
es. The solver uses low-storage explicit Runge-Kutta schemes for the time
integration. We implemented the ADG method in deal.II\, incorporating the
communication-avoiding algorithm (CAA)\, and performed validation and benc
hmarking\, showcasing the accuracy limitations of standard ADG schemes and
the effectiveness of newly developed asynchrony-tolerant (AT) fluxes. Str
ong scaling results are provided for both synchronous and asynchronous DG
solvers\, demonstrating a speedup of up to 80%. Since these AT fluxes are
also compatible with the finite volume (FV) method\, the overall work high
lights the potential benefits of the asynchronous approach for the develop
ment of accurate and scalable DG and FV-based PDE solvers\, paving the way
for simulations of complex physical systems on massively parallel superco
mputers.\n\n\n\n\n\n\n\nALL ARE WELCOME\n\n
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:34@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240131T140000
DTEND;TZID=Asia/Kolkata:20240131T150000
DTSTAMP:20240126T064354Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-sc
alable-read-alignment-algorithm-for-cyclic-pangenome-graphs/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : "Scalable read alignment
algorithm for cyclic pangenome graphs"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n\n\nSpeaker : Ms. Jyotshna Rajput\n\nS.R. Number : 06
-18-01-10-22-21-1-19943\n\nTitle :"Scalable read alignment algorithm for c
yclic pangenome graphs"\nResearch Supervisor: Dr. Chirag Jain\nDate &\;
Time : January 31\, 2024 (Wednesday) at 02:00 PM\nVenue : # 102 CDS Semin
ar Hall\n\n\n\nABSTRACT\n\nThe growing availability of genome sequences of
several species\, including humans\, has created the opportunity to utili
ze multiple reference genomes for bioinformatics analyses and improve the
accuracy of genome resequencing workflows. Graph-based data structures are
suitable for compactly representing multiple closely related reference ge
nomes. Pangenome graphs use a directed graph format\, where vertices are l
abeled with strings\, and the individual reference genomes are represented
as paths in the graph. Aligning sequences (reads) to pangenome graphs is
fundamental for pangenome-based genome resequencing. The sequence-to-graph
alignment problem seeks a walk in the graph that spells a sequence with m
inimum edit distance from the input sequence. However\, exact algorithms f
or solving this problem are unlikely to scale due to the known hardness of
this problem. Co-linear chaining is a well-studied and commonly used heur
istic alternative for quickly aligning reads to a graph. However\, the kno
wn chaining algorithms are restricted to directed acyclic graphs (DAGs) an
d are not trivially generalizable to cyclic graphs. This limitation must b
e addressed because pangenome graphs often contain cycles due to inversion
s\, duplications\, or copy number mutations within the reference genomes.\
n\nThis thesis presents the first practical formulation and algorithm for
co-linear chaining on cyclic pangenome graphs. Our algorithm builds upon t
he known chaining algorithms for DAGs. We propose a novel iterative algori
thm to handle cycles and provide rigorous proof of correctness and runtime
complexity. Our algorithm also exploits the domain-specific small-width p
roperty of pangenome graphs. The proposed optimizations enable our algorit
hm to scale to large human pangenome graphs. We implemented our algorithm
in C++ and referred to it as PanAligner (https://github.com/at-cg/PanAlign
er). PanAligner is an end-to-end long-read aligner for pangenome graphs. W
e evaluated its speed and accuracy by aligning simulated long reads to a c
yclic human pangenome graph comprising 95 haplotypes. We achieved superior
read mapping accuracy using PanAligner compared to existing methods.\n\n\
n\nALL ARE WELCOME
CATEGORIES:Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:33@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240131T140000
DTEND;TZID=Asia/Kolkata:20240131T150000
DTSTAMP:20240126T064012Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-online-mode-cds-31-j
anuary-2024-sparsification-of-reaction-diffusion-dynamical-systems-on-comp
lex-networks/
SUMMARY:Ph.D: Thesis Defense: Online Mode: CDS: 31\, January 2024 "Sparsifi
cation of Reaction-Diffusion Dynamical Systems on > Complex Networks"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nPh.D. Thesis D
efense\n\n\n\nSpeaker : Mr.Abhishek Ajayakumar\n\nS.R. Number : 06-18-01-1
0-12-18-1-16176\n\nTitle : "Sparsification of Reaction-Diffusion Dynamical
Systems on Complex Networks"\n\nResearch Supervisor : Prof. Soumyendu Rah
a\n\nDate &\; Time : January 31\, 2024 (Wednesday)\, 02:00 PM\nVenue :
The Thesis Défense will be held on MICROSOFT TEAMS\n\nPlease click on the
following link to join the Thesis Defense:\n\nMS Teams link\n\n\n\nABSTRA
CT\n\nGraph sparsification is an area of interest in computer science and
applied mathematics. Sparsification of a graph\, in general\, aims to redu
ce the number of edges in the network while preserving specific properties
of the graph\, like cuts and subgraph counts. Modern deep learning framew
orks\, which utilize recurrent neural network decoders and convolutional n
eural networks\, are characterized by a significant number of parameters.
Pruning redundant edges in such networks and rescaling the weights can be
useful. Computing the sparsest cuts of a graph is known to be NP-hard\, an
d sparsification routines exist for generating linear-sized sparsifiers in
almost quadratic running time.The complexity of this task varies\, closel
y linked to the desired level of sparsity to achieve. The thesis introduce
s the concept of sparsification to the realm of reaction-diffusion complex
systems. The aim is to address the challenge of reducing the number of ed
ges in the network while preserving the underlying flow dynamics. Sparsifi
cation of such complex networks is approached as an inverse problem guided
by data representing flows in the network\, where a relaxed approach is a
dopted considering only a subset of trajectories. The network sparsificati
on problem is mapped to a data assimilation problem on a reduced order mod
el (ROM) space with constraints targeted at preserving the eigenmodes of t
he Laplacian matrix under perturbations. The Laplacian matrix is the diffe
rence between the diagonal matrix of degrees and the graph’s adjacency m
atrix. Approximations are propose to the eigenvalues and eigenvectors of t
he Laplacian matrix subject to perturbations for computational feasibility
\, and a custom function is included based on these approximations as a co
nstraint on the data assimilation framework. Extensive empirical testing c
overed a range of graphs\, while its application to multiple instances led
to the creation of sparse graphs. In the latter phase of the thesis\, a f
ramework is presented to enhance proper orthogonal decomposition (POD)-bas
ed model reduction techniques in reaction-diffusion complex systems. This
framework incorporates techniques from stochasticfiltering theory and patt
ern recognition (PR). Obtaining optimal state estimates from a noisy model
and noisy measurements forms the core of the filtering problem. By integr
ating the particle filtering technique\, the reaction-diffusion state vect
ors are generated at various time steps\, utilizing the ROM states as meas
urements. To ensure the framework’s effectiveness\, intermittent updates
to the system variables are made during the particle filtering step\,empl
oying the carefully crafted sparse graph. The framework is utilized for ex
perimentation\, and results are presented on random graphs\, considering t
he diffusion equation on the graph and the chemical Brusselator model as t
he reaction-diffusion system embedded in the graph. Limitations of the met
hod are discussed\, and the proposed framework is evaluated by comparing i
ts performance against the Neural Ordinary Differential Equation or neural
ODE-based approach\, which serves as a compelling reference due to its de
monstrated robustness in specific applications.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:32@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240129T093000
DTEND;TZID=Asia/Kolkata:20240129T103000
DTSTAMP:20240119T062759Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-hybridcds-29-january
-2024end-to-end-resiliency-analysis-framework-for-cloud-storage-services/
SUMMARY:Ph.D: Thesis Defense: HYBRID:CDS: 29\, January 2024 "End-to-end Res
iliency Analysis Framework for Cloud Storage Services."
DESCRIPTION:\nDEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\n\nPh.D. Thes
is Defense \n\n\n\nSpeaker : Ms. Archita Ghosh\nS.R. Numbe
r : 06-18-02-10-12-17-1-14492\nTitle : "End-to-e
nd Resiliency Analysis Framework for Cloud Storage Services."\nResearch Su
pervisor :Dr Lakshmi Jagarlamudi\nDate &\; Time : January 29\, 2024
(Monday)\, 09:30 AM\nVenue : The Thesis Défense w
ill be held on HYBRID Mode\n\n# 102 CDS Seminar Hall /MICROSOFT TEAMS\n
\nPlease click on the following link to join the Thesis Defense:\nMS Teams
link\n\n\n\n\n\nABSTRACT\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\
n\n\n\n\n\n\nCloud storage service brought the idea of a global scale stor
age system available on-demand and accessible from anywhere. Despite the b
enefits\, resiliency remains one of the key issues that hinder the wide ad
aptation of storage services. The data is hosted on cloud data centers con
taining hundreds of thousands of commodity-grade hardware with layers of c
omplex software. Failures due to system crashes\, natural disasters\, cybe
r-attacks\, etc.\, are common and frequent in such environments. To keep t
he service unaffected by such events\, resiliency is essential for cloud s
ystems. For storage services\, resiliency is far more critical because los
ing access to data or\, more importantly\, a complete data loss can have a
catastrophic impact on the client.\n\n\n\n\n\n\n\n\nThe existing works on
storage resiliency focus on maintaining sufficient user data redundancy i
n the system to maintain a reliable service. However\, providing a global-
scale storage solution requires various functional and management layers t
o ensure the service is accessible and all the stored items are durable. T
he first part of our work proves that resiliency at the stored data level
does not guarantee service level reliability. A generic cloud storage syst
em model is designed to analytically show that the reliability achieved at
the service level drastically differs from the reliability ensured by sto
red data redundancy. This motivates us to bring the entire system into pur
view to understand cloud storage resiliency.\n\nDue to the complexity and
variation of large-scale storage architectures\, assessing end-to-end stor
age resiliency is a challenging task. To achieve this\, the second part of
the work proposes a generic resiliency evaluation method for cloud storag
e services. The method identifies the essential functional layers for stor
age service and the components constituting the layers. It then performs a
n in-depth behavior analysis during all possible failures of each componen
t. The method is used to assess the resiliency of two diverse and real-wor
ld cloud storage services\, OpenStack Swift and CephFS. The analysis ident
ifies various resiliency weak points in the service architectures and depi
cts the effectiveness of different resiliency methods used at various laye
rs.\n\nThe third part of the work extends the resiliency evaluation method
to understand the correlation of resiliency with the service usage patter
n. A storage service can be used for different use cases resulting in the
variation of request interarrival time\, read and write ratio\, accessed d
ata and metadata\, etc. Hence\, the components involved in access sequence
s may differ\, and so can their failure impact. Using the improved resilie
ncy evaluation method and access patterns identified from real traces\, we
show that resiliency can be selective and dynamically adjusted based on w
orkloads without affecting service reliability.\n\nFinally\, the work defi
nes an end-to-end resiliency analysis framework for cloud storage services
that enables quantification\, comparison\, and optimization of cloud stor
age resiliency. The framework allows effective modeling of cloud storage r
esilience by combining the resiliency of each component participating in s
ervice reliability maintenance for specific workloads. The framework succe
ssfully models the resiliency of OpenStack Swift and CephFS as Stochastic
Petri Nets (SPNs). The models are used to quantify and compare the resilie
ncy of the above two service architectures and demonstrate how to optimize
resiliency while achieving expected service reliability.\n\n\n\nALL ARE W
ELCOME\n\n\n\n\n\n
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:23@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata;VALUE=DATE:20240124
DTEND;TZID=Asia/Kolkata;VALUE=DATE:20240126
DTSTAMP:20231222T135636Z
URL:https://cds.iisc.ac.in/events/workshop-on-artificial-intelligence-in-p
recision-medicine/
SUMMARY:Workshop on Artificial Intelligence in Precision Medicine
DESCRIPTION:Date: 24-25 January\, 2024\n\nLocation: #102-Seminar Hall\, Co
mputational &\; Data Sciences Department\, IISc\n\n\n\nThe workshop aim
s to foster a deeper understanding of ongoing research in both Industry an
d Academia by bringing together leaders from both. We hope to provide a co
mprehensive overview of recent advancements in precision medicine\, and al
so explore its wide-ranging applications.\n\nFor more information\, please
visit our website: website\n\nMandatory Poster Submission\n\nIn line with
our commitment to fostering interactive discussions and knowledge exchang
e\, we require all attendees to submit posters presenting their research c
ontributions relevant to the workshop themes. Poster submissions are manda
tory for participation in the workshop.\n\nYou can submit your poster and
register yourself here: form\n\nNote: All the participants are requested t
o kindly carry your own laptops for the hands-on session.\n\n \;\n\n\n
\n\n\n \;
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:31@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240119T141500
DTEND;TZID=Asia/Kolkata:20240119T151500
DTSTAMP:20240118T150345Z
URL:https://cds.iisc.ac.in/events/cds-seminar-intl-collaborative-opportuni
ties-with-cardiff-university-on-cybersecurity-and-related-areas/
SUMMARY:[CDS SEMINAR] Intl Collaborative Opportunities with Cardiff Univers
ity on Cybersecurity and Related Areas
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nDEPARTMENT SEM
INAR\n\n\n\nSpeaker: Dr. A. Javed\, Cardiff University\, UK\n\nTitle: In
ternational Collaborative Opportunities with Cardiff University on Cyberse
curity and Related Areas\n\nDate and Time: Fri Jan 19\, 2:15pm\n\nLocation
: CDS 102 Seminar Hall\n\n\n\nABSTRACT: \n\nThis talk will provide a com
prehensive overview of recent advancements and cutting-edge research we do
in the cybersecurity group at Cardiff University. Our researchers have be
en at the forefront of addressing contemporary challenges in cybersecurity
\, ranging from understanding human and machine behaviour behind cyber-att
acks to developing innovative models for threat detection and prediction.
The discussion will encompass a spectrum of topics\, including exploring n
ovel methods in community organisation within online social networks\, the
correlation between emotions and malware propagation\, and the developmen
t of effective countermeasures against evolving cyber threats. The talk ai
ms to outline our work and identify areas where we can collaborate and sub
mit joint grant applications.\n\nBIO: \n\nDr. A. Javed is a Cardiff Unive
rsity lecturer focusing on machine learning to identify cybercriminal beha
viour. His research includes understanding human and machine behaviour pat
terns\, emphasising online social network (OSN) security. Dr. Javed collab
orates with other schools\, resulting in joint publications on predicting
drive-by download attacks and factors in malware propagation. He pioneers
predictive defence strategies in OSN security. He also develops models for
securing Internet of Things (IoT) devices on the edge and addressing adve
rsarial attacks. His research extends to quantifying organisational cyber
risks for continuous monitoring\, aiding in resource allocation. Dr. Javed
's work enhances his teaching and contributes to the fields of cyber secur
ity and risk management.\n\nThis talk is supported by the Global Wales-IIS
c Joint Research Partnership Fund\n\n\n\nHOST: Yogesh Simmhan\n\nALL ARE W
ELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:29@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240112T160000
DTEND;TZID=Asia/Kolkata:20240112T170000
DTSTAMP:20240108T064154Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-an
-importance-sampling-in-n-sphere-monte-carlo-nsmc-and-its-performance-anal
ysis-for-high-dimensional-integration/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : "An importance sampling
in N-Sphere Monte Carlo (NSMC) and its performance analysis for high dimen
sional integration."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n\n\nSpeaker : Mr. Jawlekar Abhijeet Rajendra\n\nS.R.
Number : 06-18-01-10-22-21-1-20128\n\nTitle :"An importance sampling in N-
Sphere Monte Carlo (NSMC) and its performance analysis for high dimensiona
l integration"\n\nResearch Supervisor: Prof. Murugesan Venkatapathi\n\nDat
e &\; Time : January 12\, 2024 (Friday) at 04:00 PM\n\nVenue : # 102 CD
S Seminar Hall\n\n\n\nABSTRACT\n\nStatistical methods for estimating integ
rals are indispensable when the number of dimensions (parameters) become g
reater than ~ 10\, where numerical methods are unviable in general. Well
-known statistical methods like Quasi-Monte Carlo converge quickly only fo
r problems with a small number of effective dimensions\, and Markov Chain
Monte Carlo (MCMC) methods incur a sharply increasing computing effort wit
h the number of dimensions 'n' that is bounded as O(n^5). This bound on
the dimensional scaling of computing effort in multiphase MCMC is limited
to domains with a given convex shape (determined by the limits of integrat
ion). Note that the non-convexity and roughness of the boundaries of the
domain are factors that adversely affect the convergence of such methods
based on a random walk\, as the n-volume concentrates near the boundary in
high dimensions with increasing 'n'.\n\nA different approach to high-D in
tegration using (1D) line integrals along random directions coupled with a
less-known volume transformation was suggested here at the Institute by A
run et. al. This method called as N-sphere Monte Carlo (NSMC) is agnostic
to the shape and roughness of the boundary for any given distribution of e
xtents of the domain from a reference point. While the dimensional scaling
of computing in NSMC integration can be bound as O(n^3) for any distribut
ion of relative extents (and not a particular convex shape)\, a similar bo
und does not exist for MCMC as any given extent distribution can represent
numerous geometries where it may not converge. It was shown earlier tha
t when restricted to convex shapes where the extent density functions beco
me increasingly heavy tailed as 'n' increases\, the naive NSMC may be more
efficient than the multiphase MCMC only when n <\; ~100. This thesis
has three contributions. 1) It is analytically shown that\, unlike MCMC\,
the convergence of NSMC in the estimation of n-volume of a domain is not a
necessary condition for its convergence in any other integration over tha
t domain. 2) A direct numerical comparison of the naive NSMC and the mul
tiphase MCMC was performed for estimating n-volumes and different types of
integrands\, establishing this advantage in integration even over typical
convex domains when n <\; ~ 100. 3) A method for importance sampling is
suggested for NSMC with a demonstration of the improved performance in hi
gher dimensions for domains with heavy tailed extent density functions. In
identifying and ensuring a local volume of interest is sampled adequately
\, this method employs efficient sampling in high-D cones with a target di
stribution.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:30@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240111T110000
DTEND;TZID=Asia/Kolkata:20240111T120000
DTSTAMP:20240108T080126Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-hybrid-cds-11-januar
y-2024-an-arbitrary-lagrangian-eulerian-volume-of-fluid-method-for-free-su
rface-and-floating-body-dynamics-simulation/
SUMMARY:Ph.D: Thesis Defense: HYBRID: CDS: 11\, January 2024 "An Arbitrary
Lagrangian Eulerian Volume of fluid method for free surface and floating b
ody dynamics simulation."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nPh.D. Thesis D
efense (HYBRID)\n\n\n\nSpeaker : Mr. Bhatta Bhanu Teja\n\nS.R. Number :06-
02-00-10-12-12-1-09588\nTitle : "An Arbitrary Lagrangian Eulerian Volume o
f fluid method for free surface and floating body dynamics simulation."\nR
esearch Supervisor :Prof. Sashikumaar Ganesan\nDate &\; Time : January
11\, 2024 (Thursday)\, 11:00 AM\n\nVenue : The Thesis Défense will be hel
d on HYBRID Mode\n\n# 102 CDS Seminar Hall /MICROSOFT TEAMS\n\n\n\nPlease
click on the following link to join the Thesis Defense:\n\nMS Teams link\n
\nhttps://teams.microsoft.com/l/meetup-join/19%3ameeting_OGE0ZDhlZTMtNTJhY
y00NDg1LWJiY2EtNjk2ZWRjOWY1OWE3%40thread.v2/0?context=%7b%22Tid%22%3a%226f
15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2283157d9d-c640-4c35-
856e-b22b4ab79e4e%22%7d\n\nABSTRACT\nThe floating body dynamics is treated
as a Fluid-Structure Interaction (FSI) problem. A FSI problem is where th
e forces from the fluid move/deform the interacting structure\, and the mo
vement of the structure\, in turn\, influences the dynamics of fluid flow
resulting in a coupled set of partial differential equations. The problem
is especially challenging owing to the time changing nature of the domain
and the presence of multiple interacting phases. These kind of time changi
ng domain and multi physics problems are primarily dealt with moving domai
n or fixed grid techniques. In the moving domain technique\, the governing
equations are posed in the so called Arbitrary Lagrangian Eulerian (ALE)
formulation. In ALE formulation\, the interfaces are resolved by the mesh
and thus leads to very good mass conservation properties. But the method f
ails when there are large topological changes\, such as mixing and splitti
ng. This problem can be partly handled by fixed grid techniques where a sp
ecial function is used to represent various phases\, but the method has it
s drawbacks\, one of which is the interfaces cannot be represented precise
ly and smears with time which leads to mass conservation problems and ofte
n much finer mesh is needed to localize the interface. Also\, because of t
he pure convection nature of the phase transport equation\, a naive/standa
rd discretization results in undershoots and overshoots of the solution. S
pecial stabilization schemes have to be used to suppress the oscillations.
The aim of the thesis is to treat the floating structure as a rigid body
to estimate its overall stability in free surface flows.\n\nFirst\, the pr
oblem is posed in moving mesh or Arbitrary Lagrangian Eulerian framework.
The motion of the free surface was captured. But the method failed to capt
ure the dynamics of the floating structure when it is introduced. Fine tun
ing the mesh yielded only incremental result. So the research focus was sh
ifted to fixed grid techniques\, particularly the 'Volume of Fluid' (VoF)
method. For the present problem\, we took a hybrid approach. The interface
between the floating structure and surrounding fluid/s is treated in a La
grangian way\, thus necessitating mesh movement\, and the fluid-fluid inte
rface (in our case\, it can be considered as water-air) is captured by the
VoF equation. As there is mesh movement\, the VoF equation was also posed
in ALE form.\n\nAs the VoF equation is a pure convection equation\, a nai
ve Galerkin discretization results in undershoots and overshoots in the so
lution. The Streamlined Upwind Petrov Galerkin (SUPG) stabilization is use
d to stabilize the VoF equation. The scheme is shown to give stable result
s. The Finite element method is used to discretize the coupled set of part
ial differential equations. The method is extensively discussed in a movin
g mesh setting with various boundary conditions. The partitioned time step
ping is used to march in time across phases\, and a fully implicit scheme
is employed within each phase.\n\nFinally\, this hybrid ALE-NSE-VoF with S
UPG stabilization scheme is shown to give stable results for extended time
steps. The numerical results with both the formulations(ALE and VoF) are
discussed. The simulations are carried out in distributed setting with Mes
sage passing interface(MPI)\, and the speedup results are discussed as wel
l.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:27@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240105T150000
DTEND;TZID=Asia/Kolkata:20240105T160000
DTSTAMP:20240103T050523Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-05th-january-2024-multim
odality-for-multilingual-nlp-the-need-an-application-and-open-questions/
SUMMARY:{Seminar} @ CDS: #102 : 05th January\, 2024 : "Multimodality for Mu
ltilingual NLP: The need\, an application\, and open questions"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Ms. Simran Khanuja\nTitle : "Multimodality for Multi
lingual NLP: The need\, an application\, and open questions "\nDate &\;
Time : January 05\, 2024\, 03:00 PM\n\nVenue : # 102\, CDS Seminar Hall\n
\n\n\nABSTRACT\n\nOur world is multimodal and multilingual\, demanding NLP
technology to be reflective of the same. From a cognitive perspective\, m
ultimodality is intricately linked with multilingualism in the brain. From
an application perspective\, this would make models capable of interpreti
ng and generating human-like responses. In this talk\, we will first prese
nt a bird's eye view on the benefits of multimodal modeling for multilingu
al NLP (the need). We will specifically focus on the visual and text modal
ity\, and discuss how real-world tasks would benefit from such models. Nex
t\, we will narrow down on our focus to one such application\, that of tra
nslating images across cultures (an application). Specifically\, we will d
iscuss our recent work on assessing the state of generative AI to do the t
ask\, and highlight existing gaps and biases of these models. Finally\, we
will broaden the discussion to explore open research questions in this ex
citing field\, and consider potential strategies for addressing these issu
es (open questions).\n\nBIOGRAPHY\n\nSimran Khanuja is a second-year PhD s
tudent at Carnegie Mellon University\, working with Prof. Graham Neubig. H
er research interests lie in multilingual\, multimodal modeling\, and down
stream applications that would benefit from such models. Prior to this\, s
he worked at Google and Microsoft Research in India for three years\, cont
ributing to the development of models and benchmarks for Indian languages.
\n\nHost Faculty: Dr. Danish Pruthi\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:28@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240105T110000
DTEND;TZID=Asia/Kolkata:20240105T120000
DTSTAMP:20240103T050807Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-05th-january-2024-solvin
g-pdes-with-neural-operators/
SUMMARY:{Seminar} @ CDS: #102 : 05th January\, 2024 : "Solving PDEs with Ne
ural Operators"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Prof. Siddhartha Mishra\, Chair Professor at ETH Zur
ich\, Switzerland\nTitle : "Solving PDEs with Neural Operators "\nDate &am
p\; Time : January 05\, 2024\, 11:00 AM\n\nVenue : # 102\, CDS Seminar Hal
l\n\n\n\nABSTRACT\n\nPartial differential equations (PDEs) are ubiquitous
as mathematical models in Science and Engineering. Solutions to both forwa
rd and inverse problems for PDEs can be encapsulated in terms of the so-ca
lled solution operators\, i.e\, mapping between infinite-dimensional funct
ion spaces\, that map inputs such as coefficients\, sources\, initial and
boundary conditions to the PDE solution. Learning the solution operator fr
om data falls under the rubric of "operator learning"\, a rapidly evolving
field within machine learning. In contrast to standard deep learning\, th
e inputs and outputs in operator learning are infinite-dimensional. Hence\
, special attention needs to be paid to the correspondence between the con
tinuous operator and its discrete realizations. By expanding on notions of
continuous-discrete equivalence in signal processing and harmonic analysi
s\, we introduce Representation equivalent Neural Operators (ReNOs) and sh
ow how they are a suitable framework of structure preserving operator lear
ning. Moreover\, a concrete instantiation of ReNOs\, the convolutional neu
ral operator (CNO) is presented and demonstrated as the state of the art m
achine learning surrogate for a wide variety of PDE benchmarks. If time pe
rmits\, we will discuss further applications such as learning PDE inverse
problems with Neural Inverse Operators.\n\nBIOGRAPHY\n\nSiddhartha Mishra
is a Chair Professor at ETH Zurich\, Switzerland\, where he heads the Comp
utational and Applied Mathematics Laboratory (CamLab)\, within the Seminar
for Applied Mathematics at the Department of Mathematics. He is also the
director of Computational Science Zurich and a core faculty member of the
ETH AI center. Mishra's research interests are in numerical analysis of PD
Es\, scientific computing and machine learning and in applications to flui
d dynamics\, geophysics\, astrophysics\, climate science and engineering.
For his contributions to research\, Mishra has received many awards and ho
nors\, including the Collatz Prize of ICIAM (2019)\, the Germund Dahlquist
Prize of SIAM (2021)\, the Rossler Prize of ETH (2023) and the Infosys Pr
ize (2019). Mishra has also been a keynote speaker at leading internationa
l conferences such as the International Conference of Mathematicians (ICM)
in 2018.\n\nHost Faculty: Prof. Sashikumaar Ganesan\n\n\n\nALL ARE WELCOM
E
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:26@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240104T150000
DTEND;TZID=Asia/Kolkata:20240104T160000
DTSTAMP:20240102T055638Z
URL:https://cds.iisc.ac.in/events/cloud-seminar-pattern-aware-graph-mining
-abstractions-performance/
SUMMARY:[CLOUD SEMINAR] Pattern-Aware Graph Mining: Abstractions & Performa
nce
DESCRIPTION:\n\nCLOUD COMPUTING SEMINAR SERIES\n\n\n\nTITLE: Pattern-Aware
Graph Mining: Abstractions &\; Performance\n\nSPEAKER: Prof. Keval Vora
\, School of Computing Science\, Simon Fraser University\, Canada\n\nDATE/
TIME: Thu Jan 4\, 3PM\n\nVENUE: CDS Seminar Room #102\n\n\n\nABSTRACT\n\nM
odern graph mining applications like Motif Counting and Frequent Subgraph
Mining analyze the structural properties of graphs (i.e.\, rely on the sub
graph isomorphism problem). These applications are not only computationall
y expensive\, but are also difficult to express due to the complexities in
volved in the nuanced structural constraints to satisfy the application re
quirements.\n\nIn this talk\, I will give a flavor of the challenges invol
ved and our recent efforts in developing scalable graph mining systems. I
will present our pattern-aware processing philosophy that enables easier e
xpression of complex graph mining use cases\, enabling efficient pattern m
atching strategies for fast subgraph exploration. Specifically\, I will di
scuss the Anti-Vertex construct to easily express neighborhood constraints
in subgraph queries. Then\, I will present Subgraph Morphing\, a general
technique that exploits structural similarities across different patterns
to accelerate graph mining systems.\n\nBIO\n\nKeval Vora is an Associate P
rofessor at the School of Computing Science at Simon Fraser University. He
received his Ph.D. from the Department of Computer Science and Engineerin
g at the University of California\, Riverside where he was advised by Prof
. Rajiv Gupta. He was also a visiting researcher at the University of Cali
fornia\, Irvine where he worked with Prof. Harry Xu. His research addresse
s challenges in building scalable modern data analytics systems\, with a f
ocus on graph data processing and management. His work lies at the interse
ction of runtime systems (often touching various parts of the technology s
tack) and algorithmic semantics (to build smarter solutions). He specializ
es in developing efficient techniques with provable guarantees for large-s
cale graph systems.\n\nHost: Yogesh Simmhan\n\nAbout: The IBM-IISc Hybrid
Cloud Lab (IIHCL) hosted at IISc is curating the Cloud Computing Seminar s
eries with guest speakers from Industry and Academia speaking about the la
test technologies and research on Cloud and edge computing\, distributed c
omputing systems\, and AI/ML/Big Data platforms.\n\n\n\nALL ARE WELCOME
CATEGORIES:Talks
END:VEVENT
BEGIN:VEVENT
UID:25@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231222T110000
DTEND;TZID=Asia/Kolkata:20231222T120000
DTSTAMP:20231222T073909Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-22nd-december-matchings-
in-big-graphs-approximation-and-streaming-algorithms/
SUMMARY:{Seminar} @ CDS: #102 : 22nd December: "Matchings in Big Graphs: Ap
proximation and Streaming Algorithms"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Prof. Alex Pothen\, Purdue University\n\nTitle : "Ma
tchings in Big Graphs: Approximation and Streaming Algorithms"\n\nDate &am
p\; Time : December 22\, 2023\, 11:00 AM\n\nVenue : # 102\, CDS Seminar Ha
ll\n\n\n\nABSTRACT\n\nMatchings in graphs are classical problems in combin
atorial optimization and computer science\, significant due to their theor
etical importance and relevance to applications. Polynomial time algorithm
s for several variant matching problems with linear objective functions ha
ve been known for fifty years. However\, these algorithms fail to compute
matchings in big graphs with billions of edges. They are also not concurre
nt and thus practical parallel algorithms are not known.\n\nThis has led t
o work in the last twenty years on designing approximation algorithms for
variant matching problems with near-linear time complexity in the size of
the graphs. Approximation has thus become a useful paradigm for designing
parallel matching algorithms. In this talk I will report on fast approxima
tion algorithms and streaming algorithms for the maximization versions of
edge-weighted matching\, edge-weighted b-matching\, and the maximum k-disj
oint weighted matching problems. We will also describe applications to air
craft design\, traffic routing in data centers and load balancing in quant
um chemistry.\n\nBIOGRAPHY\n\nAlex Pothen is a professor of computer scien
ce at Purdue University. His research interests are in combinatorial scien
tific computing\, graph algorithms and parallel computing. He received the
George Polya prize in applied combinatorics from the Society for Industri
al and Applied Mathematics (SIAM) in 2021 for his work on graph coloring a
lgorithms to enable Jacobian and Hessian matrix computations for optimizat
ion. He is a Fellow of SIAM\, ACM and AMS.\n\nHost Faculty: Prof. Sashikum
aar Ganesan\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:24@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231221T110000
DTEND;TZID=Asia/Kolkata:20231221T120000
DTSTAMP:20231215T045612Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-in
telligent-methods-for-cloud-workload-orchestration/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : "Intelligent Methods for
Cloud Workload Orchestration."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n\n\nSpeaker : Mr. Prathamesh Saraf Vinayak\n\nS.R. Nu
mber : 06-18-01-10-22-21-1-19717\n\nTitle :"Intelligent Methods for Cloud
Workload Orchestration"\nResearch Supervisor: Dr. Lakshmi J\nDate &\; T
ime : December 21\, 2023 (Thursday) at 11:00 AM\nVenue : # 102 CDS Seminar
Hall\n\n\n\nABSTRACT\n\nCloud workload orchestration is pivotal in optimi
zing the performance\, resource utilization\, and cost-effectiveness of ap
plications in data centers. As modern businesses and IT operations are mig
rating their businesses to the cloud\, understanding the dynamics of cloud
data centers has become indispensable. Often\, two perspectives play a pi
votal role in workload orchestration in data centers. One is from the clou
d provider side\, whose goal is to provision as many applications as possi
ble on the available resources\, biding to SLA constraints and increasing
return on investment. Others are from the side of enterprises and individu
al customers\, often referred to as end users\, whose primary objective is
to ensure application performance with a reduced deployment cost. Contain
erization has gained popularity for deploying applications on public cloud
s\, where large enterprises manage numerous applications through thousands
of containers placed onto Virtual Machines (VMs). While the need for cost
-efficient placement in cloud data centers is undeniable\, the complexitie
s involved in achieving this goal cannot be understated. This problem is u
sually modeled as a multi-dimensional Vector Bin-packing Problem (VBP). So
lving VBP optimally is NP-hard and practical solutions requiring real-time
decisions use heuristics. This work explores the landscape of cloud data
centers\, emphasizing the significance of efficient bin packing in achievi
ng optimal cost and resource utilization. Traditional methods\, including
heuristics and optimal algorithms\, face limitations in handling continuou
s request arrivals and the dynamic nature of cloud workloads. Integer Line
ar Programming (ILP)\, which can provide optimal solutions for small probl
em sizes with tens of requests\, may take minutes to hours to complete\, e
ven at such scales. Moreover\, optimal algorithms inherently demand perfec
t knowledge of all current and future requests to be placed within the bin
s\, rendering them unsuitable for the dynamic and often unpredictable onli
ne placement scenarios prevalent in cloud setups. To address these challen
ges\, this work introduces a novel approach to solving VBP through Reinfor
cement Learning (RL)\, trained on the historical container workload trace
for an enterprise\, a.k.a CARL (Cost-optimized container placement using A
dversarial Reinforcement Learning). The proposed work evaluates the effect
iveness of CARL in comparison to traditional methods. CARL leverages histo
rical container workload traces\, learning from a semi-optimal VBP solver
while optimizing VM costs. The contributions of this research extend beyon
d traditional methods\, providing insights into the advantages and disadva
ntages of heuristics\, optimal algorithms\, and learning approaches. We tr
ained and evaluated CARL on workloads derived from realistic traces from G
oogle Cloud and Alibaba for placing 10\,000 container requests onto over 8
000 VMs. CARL is fast\, making placement decisions for request sets with 1
24 containers per second within 65ms onto 1000s of potential VMs. It is al
so efficient\, achieving up to 13.98% lower VM costs than baseline heurist
ics for larger traces. To push the boundaries further\, we use the Mixture
of Experts (MoE) strategy in CARL\, wherein we use multiple experts who h
elp CARL learn placement policies of various approaches combined. Includin
g an MoE strategy enhances CARL's adaptability to changes in workload dist
ribution\, ensuring competitive performance in scenarios with skewed resou
rce needs or inter-arrival times.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:22@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231220T100000
DTEND;TZID=Asia/Kolkata:20231220T110000
DTSTAMP:20231211T125647Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-fast-and-scal
able-algorithms-for-intelligent-routing-of-autonomous-marine-vehicles/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS : "Fast and Scalable Algorithms for
Intelligent Routing of Autonomous Marine Vehicles."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
loquium\n\n\n\nSpeaker : Mr. Rohit Chowdhury\n\nS.R. Number : 06-18-01-10-
12-18-1-16320\n\nTitle :"Fast and Scalable Algorithms for Intelligent Rout
ing of Autonomous Marine Vehicles"\nResearch Supervisor: Dr. Deepak Subram
ani\nDate &\; Time : December 20\, 2023 (Wednesday) at 10:00 AM\nVenue
: # 102 CDS Seminar Hall\n\n\n\nABSTRACT\n\nAutonomous marine agents play
a pivotal role in diverse ocean applications. These agents serve as indisp
ensable instruments for acquiring crucial environmental information. They
are used to explore and monitor of harsh environments\, e.g.\, to map ocea
n topography\, study coral reefs\, search and rescue\, structural monitori
ng of oil and gas installations etc. In naval security\, these agents are
used for surveillance and strategic monitoring of maritime activities. Bui
lding intelligence to optimally use these agents is essential for reducing
operational costs.\n\nThe path planning problem is as follows. An autonom
ous marine agent must optimally traverse from a given start location to a
given target location in a stochastic dynamic velocity field like ocean cu
rrents while avoiding obstacles or restricted regions in the flow. A key c
hallenge is that the agent is heavily advected by the flow. The optimality
may refer to minimising expected travel time or energy consumption\, data
collection or risk of failure. While there are multiple methods of solvin
g path planning problems\, each with its challenges\, we develop and use a
fast and scalable MDP-based offline planning software that computes optim
al policies\, and a novel sequence-modelling-based deep learning approach
for onboard routing and dynamic planning\, where the objective is to learn
optimal action sequences for the agent. The goal of this thesis is to dev
elop efficient\, fast and scalable Artificial intelligence algorithms for
optimal planning and on-board routing algorithms for autonomous marine age
nts in stochastic dynamic environments.\n\nThe thesis comprises five works
organised into two parts based on the solution approach. In the first par
t\, we model the path planning problem as a Markov Decision Process (MDP)
and aim to compute an optimal policy. However\, the key challenge here is
that solving an MDP can be prohibitively expensive for large state and act
ion spaces. To overcome this challenge\, we either approximate the optimal
policy or accelerate the computation using GPUs.\n\n Physics-driven Q-le
arning for onboard routing: First\, the distribution of exact time-optimal
paths predicted by stochastic Dynamically Orthogonal (DO) Hamilton-Jacobi
level set partial differential equations (HJLS PDEs) are utilised to lear
n an initial action-value function that approximates the optimal policy. T
he flow data collected by onboard sensors are utilised to get a posterior
estimate of the environment. The approximated optimal policy is refined in
-mission by performing epsilon greedy Q-learning in simulated posterior en
vironments. We showcase the computational advantage of the approach at the
cost of approximating the optimal policy.\n GPU-accelerated path plannin
g: We compute an exact optimal policy by solving the path planning problem
modelled as an MDP. To solve large-scale real-time problems\, which can o
therwise be computationally expensive\, we introduce an efficient end-to-e
nd GPU accelerated algorithm that builds the MDP model (computing transiti
on probabilities and expected one-step rewards) and solves the MDP to comp
ute an optimal policy. We develop methodical and algorithmic solutions to
overcome the limited global memory of GPUs by using a dynamic reduced-orde
r representation of the ocean flows\, leveraging the sparse nature of the
state transition probability matrix and introducing a neighbouring subgrid
concept to save memory and reduce the computational effort. We achieve si
gnificant speedups compared to conventional sequential computation.\n Mul
ti-objective GPU-accelerated path planning: The end-to-end GPU accelerated
MDP solver is extended to a multi-objective path planner to solve multi-o
bjective optimisation problems in path planning\, like minimising both the
expected mission completion time and energy consumption. MDPs are modelle
d with scalarised rewards for multiple objectives. The solver is used to s
olve numerous instances of complex scenarios with other sources of uncerta
inty in the environment\, enabling us to compute optimal operating curves
in a fraction of the time compared to traditional solvers.\n\nIn the secon
d part\, we convert the optimal path planning problem into a supervised le
arning problem via sequence modelling. This approach involves learning opt
imal action sequences based on the available environment information and e
xpert trajectories. It also avoids the issue of re-computing optimal polic
ies for onboard routing.\n Intelligent onboard routing using decision tra
nsformers: We develop a novel\, deep learning method based on the decision
transformer (decoder-only model) for onboard routing of autonomous marine
agents. Training data is obtained from aforementioned HJLS PDE or MDP sol
vers\, which is further processed to sequences of states\, actions and ret
urns. The model is autoregressively trained on these sequences and then te
sted in different environment settings. We demonstrate that (i) a trained
agent learns to infer the surrounding flow and perform optimal onboard rou
ting when the agent's state estimation is accurate\,(ii) specifying the ta
rget locations (in case of multiple targets) as a part of the state enable
s a trained agent to route itself to the correct destination\, and (iii) a
trained agent is robust to limited noise in state transitions and is capa
ble of reaching target locations in completely new flow scenarios. We exte
nsively showcase end-to-end planning and onboard routing in various canoni
cal and idealised ocean flow scenarios.\n Path planning with environment
encoders and action decoders: We propose a novel combination of dynamicall
y orthogonal flow representation with uncertainty and a transformer model
(encoder-decoder) for the path planning task. We model the problem as a se
quence-to-sequence translation task where the source sequence is the agent
's knowledge representation of the uncertain environmental flow. The targe
t sequence is the optimal sequence of actions the agent must execute. We d
emonstrate that a trained transformer model can predict near-optimal paths
for unseen flow realisations and obstacle configurations in a fraction of
the time required by traditional planners. Validation is performed to sho
w generalisation in unseen obstacle configurations. We also analyse the pr
edictions of both transformer models\, viz\, decoder only and encoder-deco
der and explain the inner mechanics of learning through a novel visualisat
ion of self-attention of actions and states on the trajectories.\n\n\n\n\n
ALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:21@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231212T103000
DTEND;TZID=Asia/Kolkata:20231212T113000
DTSTAMP:20231207T052739Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-12th-december-compressib
le-turbulence-and-mixing-from-homogeneous-isotropic-to-convective-turbulen
ce/
SUMMARY:{Seminar} @ CDS: #102 : 12th December: "Compressible Turbulence and
Mixing: From Homogeneous Isotropic to Convective Turbulence"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Dr. John. P. John\nTitle : "Compressible Turbulence
and Mixing: From Homogeneous Isotropic to Convective Turbulence"\nDate &am
p\; Time : December 12\, 2023\, 10:30 AM\n\nVenue : # 102\, CDS Seminar Ha
ll\n\n\n\nABSTRACT\n\nCompressible turbulence and turbulent mixing play a
critical role in diverse systems ranging from engineering devices to astro
physics. Attempts to make progress using traditional governing parameters\
, namely the Taylor Reynolds number (Rλ) and the turbulent Mach number (M
t) have been marred with inconsistencies and conflicting results in the li
terature. Combining a massive DNS database and a novel asymptotic theoreti
cal approach\, we identify a new nondimensional scaling parameter\, δ\, t
he ratio of compressible to vortical strength. This parameter along with t
raditional parameters is used to unravel universal behaviour and scaling l
aws resolving several major issues. Results include division of the δ −
Mt phase into different physical regimes\, scaling of spectra\, dissipati
ve anomaly and passive scalar mixing in compressible turbulence. Finally s
ome exciting new results on fully compressible convection going beyond the
commonly used Boussinesq and anelastic approximations will be reported.\n
\nBIOGRAPHY\n\nDr John Panickacheril John is currently a Alexander von Hum
boldt postdoctoral fellow at the Institute of Thermodynamics and Fluid Mec
hanics\, Technische Universit¨at\, Ilmenau. His research interests are in
compressible turbulence\, convection and dispersed compressible multiphas
e turbulence using a combination of theory and high-fidelity direct numeri
cal simulations. He completed his undergraduate studies in mechanical engi
neering from National Institute of Technology\, Calicut. He did his master
s and Ph.D in aerospace engineering from Purdue University and Texas A &am
p\; M university respectively.\n\nHost Faculty: Dr. Konduri Aditya\n\n\n\n
ALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:20@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231208T150000
DTEND;TZID=Asia/Kolkata:20231208T160000
DTSTAMP:20231205T090746Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-08th-december-algorithms
-to-study-micro-evolutionary-systems/
SUMMARY:{Seminar} @ CDS: #102 : 08th December: "Algorithms to study micro-e
volutionary systems"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Dr. Palash Sashittal\nTitle : "Algorithms to study m
icro-evolutionary systems"\nDate &\; Time : December 08\, 2023\, 03:00
PM\n\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\nRapid advancemen
ts in sequencing technologies are revolutionizing the fields of modern med
icine and public health management. In recent years\, several groundbreaki
ng techniques such as CRISPR-Cas9 genome editing and barcoding of biomolec
ules from individual cells have emerged. These breakthroughs\, coupled wit
h the decreasing costs of genomic sequencing\, have resulted in the develo
pment of various “*-Seq” protocols for measuring DNA\, RNA\, and prote
ins at unprecedented throughput and resolution.\n\nIn many biological appl
ications\, the bottleneck is not in the generation of sequence data\, but
rather in the computational analysis and interpretation of this data. Spec
ifically\, the diverse characteristics of these sequencing methods have cr
eated a pressing need for specialized algorithms capable of effectively in
terpreting the vast amounts of sequencing data.\n\nIn this talk\, I will i
ntroduce two such algorithms designed to analyse data from recently develo
ped single-cell sequencing technologies. First\, I will present ConDoR\, a
n algorithm to infer the evolutionary history of a cancer tumor using targ
eted single-cell DNA sequencing (scDNA-seq) data. Underlying ConDoR is a n
ew evolutionary model\, the Constrained k-Dollo model\, which generalizes
existing models used for cancer evolution. I will show that ConDoR outperf
orms existing methods for tumor phylogeny inference methods on simulated a
nd real targeted scDNA-seq data. Second\, I will present Startle\, an algo
rithm to infer cell lineage trees from CRISPR-Cas9-based lineage tracing d
ata. Startle uses a new model\, the star homoplasy model\, which captures
the unique characteristics of mutations induced by CRISPR-Cas9. I will dem
onstrate that Startle infers more accurate phylogenies on simulated lineag
e tracing data compared to existing methods\, and finds parsimonious phylo
genies with fewer metastatic migrations on lineage tracing data from mouse
metastatic lung adenocarcinoma.\n\nBIOGRAPHY\n\nDr. Palash Sashittal is a
Postdoctoral Research Associate with Prof. Ben Raphael in the Computer Sc
ience Department at Princeton University. His research focuses on the desi
gn of combinatorial and statistical algorithms to analyze and interpret se
quencing data. Recent areas of emphasis include infectious disease evoluti
on and transmission\, cancer genome evolution\, and cell fate mapping in d
evelopmental systems.\n\nHe received a Ph.D. in Aerospace Engineering and
M.S. in Computer Science from the University of Illinois Urbana-Champaig
n (UIUC)\, and B.Tech. in Aerospace Engineering from Indian Institute of T
echnology Bombay (IIT Bombay). Palash’s work has been recognized by mult
iple awards and honors\, including Best Paper Award at RECOMB CCB\, Mistle
toe Research Fellowship\, Cornell Future Faculty Fellowship and Mavis Futu
re Faculty Fellowship (UIUC). Palash is firmly committed to enhancing dive
rsity\, equity\, and inclusion in STEM through mentoring\, outreach\, and
service activities.\n\nHost Faculty: Dr. Chirag Jain\n\n\n\nALL ARE WELCOM
E
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:19@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231207T110000
DTEND;TZID=Asia/Kolkata:20231207T120000
DTSTAMP:20231205T090803Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-07th-december-production
-scale-ai-for-contact-centers-at-asapp/
SUMMARY:{Seminar} @ CDS: #102 : 07th December: "Production-scale AI for con
tact centers at ASAPP"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n\n\nSpeaker : Dr. Nirmal Mukhi\nTitle : "Production-scale AI for c
ontact centers at ASAPP"\nDate &\; Time : December 07\, 2023\, 11:00 AM
\n\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\nASAPP builds AI sy
stems that help drive transformational outcomes for contact centers in a d
iverse set of industries. We do this using a deep understanding of the pro
blem\, bringing research and engineering expertise to bear to solve it\, a
nd a rigorous process to optimize the solution.\n\nIn this talk\, Nirmal M
ukhi will focus on a particular example of this approach\, describing how
we support agents in responding to customers efficiently through a combina
tion of retrieval-augmented generation\, predictive text and hallucination
detection models\, and discuss the research and engineering challenges in
delivering and optimizing this for 10\,000s of concurrent conversations.\
n\nBIOGRAPHY\n\nNirmal Mukhi leads the AI Engineering team at ASAPP\, and
has responsibility for multiple AI products. Prior to joining ASAPP\, Nirm
al held leadership positions in engineering and research at IBM\, where he
was R&\;D lead for Watson Education\, and has also served as CTO at fa
st-growing EdTech startup. He has over 30 publications (with 4500+ citatio
ns) in distributed systems\, BPM\, and AI. He has been awarded 15 patents\
, has held the title of Master Inventor at IBM\, and has appeared on a Dis
covery channel documentary about AI.\nHost Faculty: Dr. Danish Pruthi\n\n\
n\nALL ARE WELCOME
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:18@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231205T160000
DTEND;TZID=Asia/Kolkata:20231205T170000
DTSTAMP:20231205T090818Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-05th-december-advancing-
visual-intelligence-innovations-across-images-videos-and-point-clouds/
SUMMARY:{Seminar} @ CDS: #102 : 05th December: "Advancing Visual Intelligen
ce: Innovations Across Images\, Videos\, and Point Clouds"
DESCRIPTION:We welcome you to CDS-KIAC talk on 05 December 2023 (Tuesday).
The details are as below:\n\n\n\nSpeaker: Dr. Mrigank Rochan\n\nTitle:
Advancing Visual Intelligence: Innovations Across Images\, Videos\, and Po
int Clouds\n\nDate and time: 05 December 2023\; 4 PM\n\nVenue: CDS #102\
, Department of Computational and Data Sciences.\n\n\n\nAbstract:\n\nAs th
e demand for advanced computer vision applications continues to grow\, the
re is a pressing need to improve the understanding and interpretation of v
isual data. In this talk\, I will present our efforts to push the boundari
es of visual intelligence across multiple modalities\, including images\,
videos\, and point clouds\, enabling more accurate and efficient analysis
of diverse visual content. Firstly\, I will introduce our method that can
automatically localize the object in an image associated with a user-gener
ated textual tag. Secondly\, I will describe our work towards the automati
c creation of a short visual summary or highlight of a long input video\,
allowing users to easily preview\, search\, and edit ever-growing video da
ta. Thirdly\, I will discuss our research on robust visual perception syst
ems in autonomous driving\, focusing specifically on LiDAR point cloud sem
antic segmentation. Finally\, I will conclude with some interesting future
directions.\n\nBio of Speaker: \n\nMrigank Rochan is an Assistant Profes
sor in the Department of Computer Science at the University of Saskatchewa
n\, Canada. He earned his PhD in Computer Science from the University of M
anitoba in 2020. His research interests lie in computer vision and machine
learning. He has published papers in top-tier computer vision and robotic
s conferences and journals\, including CVPR\, ICCV\, ECCV\, ICRA\, and TPA
MI. He received the 2020 Canadian Image Processing and Pattern Recognition
Society (CIPPRS) John Barron Doctoral Dissertation Award for his doctoral
dissertation on deep learning models for video abstraction\, a prestigiou
s national award given annually to the top PhD thesis defended in computer
/robot vision at a Canadian university.\n\n\n\nALL ARE WELCOME
CATEGORIES:Talks
END:VEVENT
BEGIN:VEVENT
UID:17@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231129T113000
DTEND;TZID=Asia/Kolkata:20231129T123000
DTSTAMP:20231205T090919Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-29th-november-computatio
nal-and-data-sciences-applications-in-oil-gas-industry/
SUMMARY:{Seminar} @ CDS: #102 : 29th November: "Computational and Data Scie
nces Applications in Oil & Gas Industry"
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Se
minar\n\n\n\nSPEAKER : Xiaojun Huang\, [Chief Engineer\, Modeling
\, Optimization &\; Data Science\, ExxonMobil]\n\nTITLE
: "Computational and Data Sciences Applications in Oil &\; Gas Indu
stry"\n\nDate &\; Time : November 29\, 2023\, 11:30 AM\n\nVenue
: # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\nIn th
is talk\, a brief overview of the oil and gas business and the importance
of computational\, AI and Machine learning skillsets to solve various busi
ness problems at ExxonMobil will be established. Further\, we will share t
he recent digital solutions created by computational\, data and optimizati
on scientists at ExxonMobil.\n\nBIOGRAPHY\n\nWork/School History\n\n Upst
ream Research Co\, (Senior) Research Scientist (2003 – 2006)\n Explora
tion Co / Development Co\, Senior Geophysical Associate (2006-2008)\n Up
stream Research Co\, Technical Team Lead\n\nProduction Monitoring / Reserv
oir Characterization (2008-2009)\n\n Upstream Research Co\, Seismology Su
pervisor (2009-2011)\n Exploration Company\, Global Geophysical Applicati
ons Manager (2011-2012)\n Exploration Company\, New Opportunity Manager\,
Middle East &\; China (2012-2014)\n Upstream Research Company\n\nGeop
hysics Function Manager / XTO Geoscience Contact Executive (2014 – 2017)
\n\n Production Co / Upstream Integrated Solution Co\n\nSubsurface Digita
l Executive / Upstream Digital Advisor (2017 – 2022)\n\n EMTEC\, MODS C
hief (2022 – present)\n B.S and M.S. in Physics &\; Electrical Engin
eering\, Nanjing University\n Ph.D. in Geophysics\, Massachusetts Institu
te of Technology\n\nHigh performance / distributed computing\n\nLarge-scal
e PDE constrained optimization\n\nInformation theory\n\nEarth systems\, fi
nance and microeconomics\n\nHost Faculty: Prof. Sashikumaar Ganesan\n\n\n\
nALL ARE WELCOME
CATEGORIES:Talks
END:VEVENT
BEGIN:VEVENT
UID:16@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata;VALUE=DATE:20231117
DTEND;TZID=Asia/Kolkata;VALUE=DATE:20231119
DTSTAMP:20231102T051946Z
URL:https://cds.iisc.ac.in/events/workshop-on-tensor-computation-and-machi
ne-learning-tcml-november-17-18-2023/
SUMMARY:Workshop on Tensor Computation and Machine Learning (TCML)\, Novemb
er 17-18\, 2023.
DESCRIPTION:Click here for program details.
CATEGORIES:Events
END:VEVENT
BEGIN:VEVENT
UID:15@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231102T140000
DTEND;TZID=Asia/Kolkata:20231102T150000
DTSTAMP:20231205T090948Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-02-no
vember-2023%e2%80%b3semi-analytical-solution-for-eigenvalue-problems-of-la
ttice-models-with-boundary-conditions/
SUMMARY:M.Tech Research: Thesis Defense: CDS: 02\, November 2023″Semi-ana
lytical solution for eigenvalue problems of lattice models with boundary c
onditions”.
DESCRIPTION:02 Nov @ 2:00 PM -- 3:00 PM\n\nDEPARTMENT OF COMPUTATIONAL AND
DATA SCIENCES\nM.Tech Research Thesis Defense\n\n\n\nSpeaker : Ms. Athira
Gopal\n\nS.R. Number : 06-18-00-10-12-19-2-17782\n\nTitle : “Semi-analyt
ical solution for eigenvalue problems of lattice models with boundary cond
itions”\n\nResearch Supervisor: Prof. Murugesan Venkatapathi\n\nDate &am
p\; Time : November 02\, 2023 (Thursday)\, 02:00 PM\n\nVenue : Room No. 10
2 (CDS Seminar Hall)\n\n\n\nAbstract\nClosed-form relations for limiting e
igenvalues of an infinite k-periodic spatial lattice in any number of dime
nsions ‘d’\, and its semi-analytical extensions for any given size ‘
n’ of the lattice with free-free boundary conditions\, are known. These
are based on the eigenvalues of tridiagonal k-Toeplitz matrices (represent
ing chains and d=1)\, and their tensor products or sums. These semi-analyt
ical methods for eigenvalues incur drastically lower computing costs than
the direct numerical methods i.e. O(n) vs. O(n^2) for the latter\, and fur
ther they are more accurate for sufficiently large lattices approaching th
e limiting case (n >\; 100). This advantage in computing cost\, accuracy
\, and numerical stability results as the original eigenvalue problem of n
k in size is reduced to n eigenvalue problems each k in size\, further mak
ing this approach very amenable to parallel computation when required. In
this work\, their errors in eigenvalues are compared with the errors of th
e direct numerical methods using special examples with high condition numb
ers. Secondly\, in the absence of such analytical methods\, one also resor
ts to periodic boundary conditions to limit the size of the numerical mode
l representing a very large system. The convergence of numerical models wi
th periodic boundary conditions to the limiting eigenvalues is highlighted
\, to emphasize the utility of the closed-form solution for the limiting e
igenvalues. Thirdly\, the fixed-fixed boundary conditions on a finite chai
n and their counterpart for periodic spatial lattices in higher dimensions
(d>\;1) are addressed using perturbations to tridiagonal k-Toeplitz mat
rices on their main diagonal. Extensions of the semi-analytical methods fo
r these cases by applying numerical methods to update only the few perturb
ed eigenvalues is proposed. An efficient extension for evaluating the eige
nvectors in the case of real eigenvalues as required in most physical syst
ems\, is also presented.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:14@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230920T100000
DTEND;TZID=Asia/Kolkata:20230920T110000
DTSTAMP:20231102T051640Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-20th-september-applicati
ons-of-hahn-banach-theorem-to-sequence-spaces-and-variational-inequality/
SUMMARY:{Seminar} @ CDS: #102 : 20th September: “Applications of Hahn- Ba
nach Theorem to Sequence Spaces and Variational Inequality.”
DESCRIPTION:20 Sep @ 10:00 AM -- 11:00 AM\n\nDepartment of Computational an
d Data Sciences\n\nDepartment Seminar\n\n \;\n\nSPEAKER : Sudarsan Nan
da (Retd. Professor- IIT Kharagpur) is at present the Professor of Eminenc
e and Research Chair at KIIIT\, Deemed to be University\, Bhubaneswar.\n\n
TITLE : “Applications of Hahn- Banach Theorem to Sequence Spaces and Var
iational Inequality.”\n\nDate &\; Time : September 20\, 2023\, 10:00
AM\n\nVenue : # 102\, CDS Seminar Hall\n\n================================
===========================================================\nAbstract\nApp
lication of the Hahn-Banach Theorem to the space of bounded sequences with
a specific sublinear functional p defined on it gives rise to linear func
tionals\, which are dominated by p and are extensions of limits of converg
ent sequences. These are called Banach Limits\, which were studied by Bana
ch (1932)\, and their uniqueness is called almost convergence and was char
acterized by Lonentz [1948].\n\nIn the present lecture\, we will discuss a
bout the absolute analog of almost convergence\, which generalizes LP spac
es. The two concepts Variational Inequality and complementarily problems a
re essentially the same concepts which are studied by two different groups
of mathematicians: applied mathematics on one hand and operations researc
hers on the other hand. The proof existence of the variational inequality
problem uses Hanh- Banach Theorem or Fixed-Point theorems. In this lecture
\, we will discuss about the existence of solutions of the complementary p
roblem\, under the most general conditions on the operator and the cone.\n
Biography\nSudarsan Nanda (Retd. Professor- IIT Kharagpur) is at present t
he Professor of Eminence and Research Chair at KIIIT\, Deemed to be Univer
sity\, Bhubaneswar. He is a Ph.D. and D.Sc. in Mathematics and was a Profe
ssor at IIT Kharagpur. He was Vice-Chancellor\, North Orissa University. P
rofessor Nanda was a Visiting Professor at the Universities of Guelph\, Ca
nada\, Pisa\, and Milan in Italy\, University of Central Florida\, USA and
Chinese University of Hongkong. He was an Associate member of ICTP\, Trie
ste\, Italy\, visited University of Kaiserslautern\, Germany under exchang
e programme and several Universities of Europe and USA. S. Nanda is a Fell
ow of Institute of Mathematics and Applications\, U.K. (FIMA)\, Forum D’
e Analysts\, Member of the Editorial Board of the Journal of Fuzzy Mathema
tics and several other Journals on Mathematics. He is a reviewer of Mathem
atical reviews and several journals of international repute. Prof. Nanda r
eceived the distinguished teacher award during 2002 and received Fulbright
and AMS travel grant for visiting USA\, during 2000. He has received dist
inguished senior scientist award from Odisha Bigyan Academy\, Bhubaneswar
Chapter of Indian Science Congress association\, Odisha environment societ
y . S. Nanda has published around 235 research papers in the journals of I
nternational repute\, guided 22 Ph. D students and authored and co-authore
d 20 books and many popular articles relating to Mathematics and Higher ed
ucation.\n\nTaking an active interest in the modernization of the Mathemat
ics syllabus\, established research culture and popularization of mathemat
ics\, which has a great impact on society S. Nanda’s research work has b
een widely cited and he has been a member in many academic bodies. He has
done social work by establishing a school for mentally handicapped childre
n at IIT Kharagpur Campus. As Vice Chancellor of North Orissa University i
ntroduced Santali language as MIL for Under Graduate students.\n\nHost Fac
ulty: Dr. Ratikanta Behera\n==============================================
====================================\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:13@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230913T100000
DTEND;TZID=Asia/Kolkata:20230913T110000
DTSTAMP:20231102T051511Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-13th-september-simulatio
n-data-6-steps-from-theory-to-impact-through-disasters-engineering-heritag
e/
SUMMARY:{Seminar} @ CDS: #102 : 13th September: “Simulation & Data: 6 Ste
ps from Theory to Impact through Disasters\, Engineering & Heritage.”
DESCRIPTION:Department of Computational and Data Sciences\n\nDepartment Sem
inar\n\n \;\n\nSPEAKER : Dr. Phil Weir\, Founder and Director of flax
&\; Teal Ltd\, UK.\n\nTITLE : “Simulation &\; Data: 6 Steps from T
heory to Impact through Disasters\, Engineering &\; Heritage.”\n\nDat
e &\; Time : September 13\, 2023\, 10:00 AM\n\nVenue : # 102\, CDS Semi
nar Hall\n\n==============================================================
=============================\nAbstract\nGeospatial data\, open data\, IoT
\, computational physics and streaming infrastructure metrics – working
across varied areas\, we see the simplicity of standard approaches struggl
ing to translate to complex\, messy\, multi-domain problems. In industry\,
theoretical challenges combine in nonlinear ways and success metrics are
rarely neatly defined. The focus on accessible\, reusable\, scalable data
solutions in short timescales can be a huge hurdle to newcomers used to re
fined\, well-understood patterns. Moreover\, as the capability of software
tooling and hardware surges\, a static skillset quickly becomes irrelevan
t. This presentation will outline six key insights about industrial mathem
atics\, to help surf that wave.\nBiography\nDr. Phil has his Bachelor’s
degree and Master’s Degree in Pure Mathematics from the University of Ca
mbridge. He completed his PhD in Computational Mathematics from the Univer
sity of Otago\, New Zealand in 2012. He also worked as Web Developer at va
rious companies in the UK. He worked as a Software Development Engineer at
NUMA Engineering Services Ltd\, Dundalk\, Ireland from 2013-2016. He is a
Board Member of OpenUK. He is the Founding Director and Executive Directo
r of Flax &\; Teal Limited\, Belfast\, UK. His areas of expertise inclu
de Data Science\, Scientific Computing\, Numerical Analysis\, Web-based Si
mulation\, and On-Demand Data Analysis. He also has work experience with g
eospatial data. Please visit his company profile here: https://flaxandteal
.co.uk/pages/team/ and his profile here https://www.linkedin.com/in/phil-w
eir-033b5a62/ His company works on Scientific Computing and Data Science.\
n\nHost Faculty: Dr. Ratikanta Behera\n===================================
===============================================\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:12@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230911T100000
DTEND;TZID=Asia/Kolkata:20230911T110000
DTSTAMP:20231102T051329Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-unsupervised-
test-time-adaptation-for-patient-specific-deep-learning-models-in-medical-
imaging/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS : “Unsupervised test-time adaptati
on for patient-specific deep learning models in medical imaging.”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
loquium\n\n_______________________________________________________________
___________________________\n\nSpeaker : Mr. Hariharan Ravishankar\n\nS.R.
Number : 06-18-00-11-12-20-1-18917\n\nTitle : “Unsupervised test-time a
daptation for patient-specific deep learning models in medical imaging”\
n\nResearch Supervisor: Prof. Phaneendra Kumar Yalavarthy\n\nDate &\; T
ime : September 11\, 2023 (Monday) at 10:00 AM\n\nVenue : # 102 CDS Semina
r Hall\n\n________________________________________________________________
__________________________\nAbstract\nDeep learning (DL) models have achie
ved state-of-the-art results in multiple medical imaging applications\, re
sulting in the widespread adoption of artificial intelligence (AI) models
for radiological workflows. Despite their success\, they exhibit two major
weaknesses in real-world applications: a) poor generalization and b) lack
of patient optimality. The potential “distribution shift” in unseen m
edical imaging data\, owing to changes in demography\, acquisition hardwar
e\, etc.\, often leads to a reduction in performance. Despite the high ave
rage performance\, they are prone to failures on “individual” cases wi
th minor input modulations than the training data. Solving this problem is
critical in medical image analysis because this variance in performance a
cross cases and abject failures on select subjects will increase the burde
n on care-giving experts and reduce trust in AI-based applications.\n\nThe
aim of this thesis work is to develop methods for patient-specific test-t
ime adaptation of pre-trained DL models on individual subject data. While
generalization is often treated as a training time goal\, test-time adapta
tion (TTA) modifies the weights during inference time in an unsupervised m
anner specific to each subject data to jointly address generalization and
patient optimality. While sustaining performance in individual subjects is
seen as a challenge for current DL models\, this line of research also ha
s an opportunity for next-generation clinical decision support systems. AI
methods that would result in patient-specific DL models can be heralded a
s the next big step in medical imaging.\n\nInformation Geometric Test Time
Adaptation (IGTTA) for semantic segmentation: The test-time adaptation (T
TA) of deep-learning-based semantic segmentation models\, specific to indi
vidual patient data\, was addressed in this part of thesis work. Existing
TTA methods in medical imaging are often unconstrained and require prior a
natomical information or additional neural networks built during the train
ing phase\, making them less practical and prone to performance deteriorat
ion. In this part of thesis work\, a novel framework based on information
geometric principles was proposed to achieve generic\, off-the-shelf\, reg
ularized patient-specific adaptation of models during test-time. By consid
ering the pre-trained model and adapted models as part of statistical neur
omanifolds\, test-time adaptation was treated as constrained functional re
gularization using information geometric measures\, leading to improved ge
neralization and patient optimality. The efficacy of the proposed approach
was shown on three challenging problems: a) improving the generalization
of state-of-the-art models for segmenting COVID-19 anomalies in Computed T
omography (CT) images\, b) cross-institutional brain tumor segmentation fr
om magnetic resonance (MR) images\, and c) segmentation of retinal layers
in Optical Coherence Tomography (OCT) images. Furthermore\, it was demonst
rated that robust patient-specific adaptation can be achieved without addi
ng a significant computational burden\, making it the first of its kind ba
sed on information geometric principles.\n\nTest-time adaptation via domai
n transforms (TTADT): OCT imaging has emerged as the modality of choice fo
r the diagnosis of retinal diseases. The popular\, cost-effective variant
of OCT – Spectral-Domain Optical Coherence Tomography (SDOCT) often suff
ers in image quality owing to speckle noise\, making expert or automated a
nalysis of these images challenging. This part of thesis work proposes for
the first-time inference time adaptation of deep learning models for a gi
ven test sample to achieve improved diagnostic performance. The adaptation
is driven by domain transforms – a framework in which domain-specific a
ugmentation is employed for a test sample\, and the deep learning model we
ights are updated in a constrained and regularized manner. Performance of
the proposed approach was evaluated on both simulated and real-world noisy
B-mode OCT images affected by varying degrees of speckle noise from four
benchmarking SD-OCT datasets. Systematic studies in this part of thesis wo
rk clearly demonstrate the utility of the proposed approach in building ge
neralized and robust deep learning models for automated retinal disease di
agnosis using OCT images.\n\nUncertainty-aware test-time adaptation for in
verse problems (UATTA): Deep learning models for inverse problems (both on
e-pass and unrolled versions): have these limitations 1) inability to prov
ide pixel-wise uncertainty quantification 2) sensitivity to variation in f
orward model and 3) sub-optimal regularization over different iterations.
In this part of thesis work\, uncertainty aware test-time adaptation of of
f-the-shelf models is proposed for improved image reconstruction performan
ce. Different pixel-wise uncertainty quantification methods were explored
for utility in test-time adaptation\, along with data fidelity constraints
. The results were studied for two relevant problems:1) accelerated MRI ac
quisition and 2) quantitative susceptibility mapping for quantifying tissu
e magnetic susceptibility. The proposed framework provides a reliable and
accurate reconstruction of these images.\n\nTest-time adaptation with foun
dational models as neural surrogates (TTANS): Recently\, foundational mode
ls have demonstrated impressive promptable segmentation of natural images.
However\, these models often perform poorly with medical imaging data. In
this part of the thesis\, a two-way synergic approach for jointly utilizi
ng foundational models while adapting pretrained medical imaging segmentat
ion models is explored. The results are demonstrated on various medical im
aging semantic segmentation tasks to show the ability of foundational mode
ls as neural surrogates\, thus demonstrating test-time adaptation achieved
in real time. As these foundational models are capable of handling high l
evels of semantics\, using them as surrogates helped reduce reliance on co
stly annotated data needed to achieve state-of-the-art results.\n\nIn summ
ary\, this thesis work developed methods to improve the generalization of
typical deep-learning models utilized in medical imaging by personalizing
them to individual patients during testing in an unsupervised and automate
d manner. The developed methods demonstrated the applicability and utility
in different medical imaging tasks across various medical imaging modalit
ies. This thesis work is the first of its kind in medical imaging to showc
ase the utility of test-time adaptation methods\, paving the way for furth
er research towards the goal of precision medicine.\n\n===================
============================================================\n\nALL ARE WE
LCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:11@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230830T110000
DTEND;TZID=Asia/Kolkata:20230830T120000
DTSTAMP:20231102T051101Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-30th-august-recon-all-cl
inical-cortical-analysis-of-heterogeneous-clinical-brain-mri-scans/
SUMMARY:{Seminar} @ CDS: #102 : 30th August: “Recon-all-clinical Cortical
analysis of heterogeneous clinical brain MRI scans”
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
ar\n\nSPEAKER : Karthik Gopinath is a Postdoctoral Research Fellow at Athi
noula A.\n\nTITLE : “Recon-all-clinical Cortical analysis of heterogeneo
us clinical brain MRI scans“\n\nDate &\; Time : August 30\, 2023\, 11
:00 AM\n\nVenue : # 102\, CDS Seminar Hall\n\n============================
===============================================================\nAbstract\
nSurface analysis of the cortex is ubiquitous in human neuroimaging with M
RI\, e.g.\, for cortical registration\, parcellation\, or thickness estima
tion. The convoluted cortical geometry requires isotropic scans (e.g.\, 1m
m MPRAGEs) and good gray-white matter contrast for 3D reconstruction. This
precludes the analysis of most brain MRI scans ac- quired for clinical pu
rposes. Analyzing such scans would enable neuroimaging studies with sample
sizes that cannot be achieved with current research datasets\, particular
ly for underrepresented populations and rare diseases. Here we present the
first method for cortical reconstruction\, registration\, parcellation\,
and thickness estimation for clinical brain MRI scans of any resolution an
d pulse sequence. The methods have a learning component and a classical op
timization module. The former uses domain randomization to train a CNN tha
t predicts an implicit representation of the white matter and pial surface
s (a signed distance function) at 1mm isotropic resolution\, independently
of the pulse sequence and resolution of the input. The latter uses geomet
ry processing to place the surfaces while accurately satisfying topologica
l and geometric constraints\, thus enabling subsequent parcellation and th
ickness estimation with existing methods. We present results on 5mm axial
FLAIR scans from ADNI and on a highly heterogeneous clinical dataset with
5\,000 scans.\nBiography\nKarthik Gopinath is a Postdoctoral Research Fell
ow at Athinoula A. Martinos Center for Biomedical Imaging\, Massachusetts
General Hospital\, and Harvard Medical School. His work focuses on Brain s
urface analysis of clinically acquired MR images. Previously he completed
his Ph.D. from ETS Montreal\, where his work focused on Geometric learning
for brain surface analysis\, for which he received the Governor-general a
cademic gold medal and Best Thesis award. He completed his MS by Research
from IIIT-Hyderabad. You can find more information about his work on his w
ebpage: https://sites.google.com/site/karthikharitz/\n\nHost Faculty: Dr.
Vaanathi Sundaresan\n=====================================================
=============================\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:10@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230824T123000
DTEND;TZID=Asia/Kolkata:20230824T133000
DTSTAMP:20231102T050810Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-24th-august-qameleon-mul
tilingual-qa-with-only-5-examples/
SUMMARY:{Seminar} @ CDS: #102 : 24th August: “QAmeleon: Multilingual QA w
ith Only 5 Examples”
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
ar\n\nSPEAKER : Priyanka Agrawal (Google Deepmind)\n\nTITLE : “QAmeleon:
Multilingual QA with Only 5 Examples“\n\nDate &\; Time : August 24\,
2023\, 12:30 PM\n\nVenue : # 102\, CDS Seminar Hall\n\n==================
=========================================================================\
nAbstract\nThe availability of large\, high-quality datasets has been one
of the main drivers of recent progress in question answering (QA). Such an
notated datasets however are difficult and costly to collect\, and rarely
exist in languages other than English\, rendering QA technology inaccessib
le to underrepresented languages. An alternative to building large monolin
gual training datasets is to leverage pre-trained language models (PLMs) u
nder a few-shot learning setting. Our approach\, QAmeleon\, uses a PLM to
automatically generate multilingual data upon which QA models are trained\
, thus avoiding costly annotation. Prompt tuning the PLM for data synthesi
s with only five examples per language delivers accuracy superior to trans
lation-based baselines\, bridges nearly 60% of the gap between an English-
only baseline and a fully supervised upper bound trained on almost 50\,000
hand labeled examples\, and always leads to substantial improvements comp
ared to fine-tuning a QA model directly on labeled examples in low resourc
e settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show that
few-shot prompt tuning for data synthesis scales across languages and is a
viable alternative to large-scale annotation.\nBiography\nPriyanka Agrawa
l is a Research Scientist at the Google Deepmind in London\, formally part
of Google Brain\, and is focused on building responsible Generative AI mo
dels and scaling them to underrepresented languages. Prior to that she was
a Senior Researcher and Lead at Booking.com and IBM Research Labs\, where
she was driving work in cross-domain transfer and representation learning
. She is also an alumni from IISc from the CSA department. Her work is pub
lished at top tier ML and NLP conferences like NeurIPS\, ACL and she holds
25+ US Patents. Priyanka also serves as Area Chair and PC member at these
conferences and has been an invited panelist and speaker at various ML/NL
P and diversity forums.\n\nHost Faculty: Dr. Danish Pruthi\n==============
====================================================================\n\nAL
L ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:9@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230824T110000
DTEND;TZID=Asia/Kolkata:20230824T120000
DTSTAMP:20231102T050821Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-24th-august-an-introduct
ion-to-artificial-intelligence-in-pharmaceutical-industry-beyond-drug-disc
overy/
SUMMARY:{Seminar} @ CDS: #102 : 24th August: “An introduction to Artifici
al Intelligence in pharmaceutical industry beyond drug discovery”
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
ar\n\nSPEAKER : Dr Arijit Patra is a Senior Principal Scientist at UCB Bio
pharma UK\n\nTITLE : “An introduction to Artificial Intelligence in phar
maceutical industry beyond drug discovery”\n\nDate &\; Time : August
24\, 2023\, 11:00 AM\n\nVenue : # 102\, CDS Seminar Hall\n\n==============
==========================================================================
===\nAbstract\nThe development of machine learning algorithms geared towar
ds drug discovery and chemoinformatics has seen unprecedented progress in
recent years. Much of this revolution has derived from progress in deep le
arning and generative modelling. It is worth noting\, however\, that the
‘Ideas to Patient’ journey in a pharmaceutical setting is much more ex
pansive and data-rich even beyond the discovery phases. There is a possibi
lity of deriving significant value using AI algorithms in the phases of dr
ug safety and efficacy evaluations\, patient analytics and post-market sur
veillance as these stages involve the analysis of large amounts of multimo
dal information. In this talk\, we shall discuss the potential for using m
achine learning approaches in these parts of the drug development pipeline
\, with a particular focus on machine learning for toxicologic pathology.\
nBiography\nDr Arijit Patra is a Senior Principal Scientist at UCB Biophar
ma UK. He holds a PhD in machine learning for healthcare imaging from the
University of Oxford\, where he was a Rhodes Scholar (India &\; Exeter\
, 2016). Prior to that\, he completed a dual degree in Mechanical Engineer
ing from the Indian Institute of Technology (IIT) at Kharagpur\, India. He
has also been associated with AstraZeneca\, Shell\, Microsoft Research an
d CSIR-South Africa at various points in his career and has been actively
involved in the AI4SG (AI for Social Good) community. He has authored seve
ral publications around machine learning and medical imaging and is a revi
ewer for multiple peer reviewed venues such as NeurIPS\, ICML\, MICCAI and
several journals.\n\nHost Faculty: Dr. Vaanathi Sundaresan\n=============
=====================================================================\nALL
ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:8@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230822T090000
DTEND;TZID=Asia/Kolkata:20230822T100000
DTSTAMP:20231102T050523Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-cds-22-august-2023%e
2%80%b3abstractions-and-optimizations-for-data-driven-applications-across-
edge-and-cloud/
SUMMARY:Ph.D: Thesis Defense: CDS: 22\, August 2023″Abstractions and Opti
mizations for Data-driven Applications Across Edge and Cloud.”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Def
ense\n\n__________________________________________________________________
________________________\n\nSpeaker : Mr. Aakash Khochare\n\nS.R. Number :
06-18-02-13-12-16-1-14050\n\nTitle : “Abstractions and Optimizations fo
r Data-driven Applications Across Edge and Cloud.”\n\nResearch Superviso
r: Prof. Yogesh Simmhan\n\nDate &\; Time : August 22\, 2023 (Tuesday)\,
09:00 AM\n\nVenue : Room No. 102 (CDS Seminar Hall)\n____________________
______________________________________________________________________\nAb
stract\nModern data driven applications have a novel set of requirements.
Advances in deep neural networks (DNN) and computer vision (CV) algorithms
have made it feasible to extract meaningful insights from large-scale dep
loyments of urban cameras and drone video feeds. These data driven applica
tions\, usually composed as workflows\, tend to have high bandwidth and lo
w latency requirements in order to extract timely results from large data
sources. Other applications may necessitate the use of multiple geographic
ally distributed resources. Such requirements may be driven by data privac
y regulations such as the General Data Protection Regulation (GDPR) of the
European Union\, need for specialized hardware\, or as a means of avoidin
g vendor lock-ins.\n\nTo support these modern applications\, a diverse com
puting landscape has emerged over the last decade. We have witnessed incre
asingly powerful Edge computing resources be available in network proximit
y to the data sources for these applications. The number of Cloud Service
Providers (CSPs) has increased along with the regions in which they operat
e. And finally\, the CSPs have supplemented Infrastructure as a Service (I
aaS) offerings with modern serverless compute offerings which promise cost
benefits as well as lower operational overheads.\n\nThe availability of c
hoices in compute resources makes it challenging for application developer
s to manage the lifecycle of their applications — from programming the a
pplication\, to optimizing it for performance\, and finally deploying it.
Typically\, developers rely on platforms that promise ease of programmabil
ity coupled with scalability with minimal developer effort. However\, the
combination of application requirements and compute resource characteristi
cs makes it challenging for platform designers to make design choices that
optimizes the application for programmability and performance. A thorough
revisit of existing platforms\, abstractions\, and optimizations is essen
tial for addressing these challenges.\n\nIn this thesis\, we tackle these
challenges with three distinct but related research contributions on scala
ble platforms\, distributed algorithms and system optimizations: (1) We pr
opose Anveshak\, a platform that provides a domain specific programming mo
del and a distributed runtime for efficiently tracking entities in a multi
-camera network\; (2) We design algorithms and heuristics to solve MSP\, w
hich co-schedules the flight routes of a drone fleet to visit and record v
ideo at waypoints\, and perform subsequent on-board Edge analytics\; and (
3) We develop XFaaS\, a platform that allows “zero touch” deployment o
f functions and workflows across multiple clouds and Edges by automaticall
y generating code wrappers\, Cloud queues\, and coordinating with the nati
ve FaaS engine of a CSP.\n\nThese platforms\, abstractions and optimizatio
ns solve different combinations of the problem dimensions\, are motivated
through real-world applications\, and the solutions are validated through
detailed experiments on distributed systems. Taken together\, this suite o
f contributions addresses the key gaps highlighted in this dissertation an
d help bridge the gap between modern computing resource characteristics an
d modern application requirements.\n\n====================================
============================\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
END:VEVENT
BEGIN:VEVENT
UID:7@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230821T100000
DTEND;TZID=Asia/Kolkata:20230821T110000
DTSTAMP:20231102T045936Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-a-
co-kurtosis-tensor-based-featurization-for-scalable-combustion-simulations
/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : “A co-kurtosis tensor
based featurization for scalable combustion simulations.”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research
Thesis Colloquium\n\n_____________________________________________________
_____________________________________\n\nSpeaker: Mr. Dibya Jyoti Nayak\n\
nS.R. Number: 06-18-01-10-22-21-1-19747\n\nTitle: “A co-kurtosis tensor
based featurization for scalable combustion simulations.“\n\nResearch Su
pervisor: Dr. Konduri Aditya\n\nDate &\; Time: August 21\, 2023 (Monday
) at 10:00 AM\n\nVenue: # 102 CDS Seminar Hall\n__________________________
________________________________________________________________\nAbstract
\nIdentifying low-dimensional representations of the thermo-chemical state
space for turbulent reacting flow systems is vitally important\, primaril
y to significantly reduce the computational cost of device-scale combustio
n simulations. Moreover\, these simulations are often performed to gain fu
ndamental insights into the inception of extreme/anomalous events such as
flashbacks\, flame extinction\, blow-offs\, thermoacoustic instabilities\,
etc.\, which can have detrimental effects on combustion efficiency and en
gine performance. With the scale of scientific investigations ever increas
ing\, the need for robust anomaly detection methods becomes increasingly c
ritical for judicious steering of these simulations and also aiding smooth
operations of practical engines. Recent studies have shown that the fourt
h-order joint statistical moment tensor\, i.e.\, co-kurtosis\, effectively
captures anomalies/outliers in scientific data. Accordingly\, the primary
objective of this work centers around leveraging the unique properties of
the co-kurtosis tensor to drive low-cost and scalable combustion simulati
ons and build robust algorithms for extreme event detection. Particularly\
, the first part of this work develops tools for dimensionality reduction
for chemistry\, while the second part focuses on employing a co-kurtosis b
ased detection algorithm for capturing extreme events such as flame instab
ilities in hydrogen-fired reheat burners relevant to sequential gas turbin
e engines.\n\nTo obtain low-dimensional manifolds (LDMs) that describe the
original thermo-chemical state\, principal component analysis (PCA) and i
ts variants are widely employed. An alternative dimensionality reduction t
echnique that focuses on higher order statistics\, co-kurtosis PCA (CoK-PC
A)\, has been shown to provide an optimal LDM for effectively capturing th
e stiff chemical dynamics associated with spatiotemporally localized react
ion zones. While its effectiveness has only been demonstrated based on a p
riori analyses with linear reconstruction\, in this work\, we employ nonli
near techniques to reconstruct the full thermo-chemical state and evaluate
the efficacy of CoK-PCA compared to PCA. Specifically\, we combine a CoK-
PCA-/PCA-based dimensionality reduction (encoding) with an artificial neur
al network (ANN) based reconstruction (decoding) and examine\, a priori\,
the reconstruction errors of the thermo-chemical state. We employ three co
mbustion test cases representing varying degrees of complexity in the geom
etrical domain\, combustion regimes\, ignition kinetics\, etc.\, to assess
CoK-PCA/PCA coupled with ANN-based reconstruction. Results from the analy
ses demonstrate the robustness of the CoK-PCA based LDM with ANN reconstru
ction in accurately capturing the data\, specifically from the reaction zo
nes.\n\nHydrogen’s highly reactive and diffusive nature towards decarbon
ization is prone to flashbacks\, flame instabilities\, and thermoacoustic
instabilities. For example\, in the case of reheat burners of hydrogen-fir
ed sequential gas turbine engines\, intermittent temperature and pressure
fluctuations result in flame instabilities\, such as intermittent autoigni
tion events at off-design locations that can adversely impact the engine
’s performance. To address this issue\, we develop an unsupervised learn
ing methodology based on the co-kurtosis tensor to detect the early onset
of spontaneous ignition kernels in lean premixed hydrogen combustion at vi
tiated conditions. The accuracy of the model is evaluated for various igni
tion test cases.\n\n======================================================
=========================\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:6@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230818T120000
DTEND;TZID=Asia/Kolkata:20230818T130000
DTSTAMP:20231102T050841Z
URL:https://cds.iisc.ac.in/events/cloud-seminar-a-carbon-first-approach-fo
r-decarbonizing-computing-prashant-shenoy-u-mass-amherst-fri-18-aug-12pm-c
ds-102/
SUMMARY:[CLOUD SEMINAR] A Carbon First Approach for Decarbonizing Computing
\, Prashant Shenoy\, U Mass\, Amherst\, Fri 18 Aug\, 12PM\, CDS 102
DESCRIPTION:========================================\nCLOUD COMPUTING SEMIN
AR SERIES\n========================================\n\nSPEAKER : Prof. Pra
shant Shenoy\, University of Massachusetts Amherst\, USA\n\nTITLE : A Carb
on First Approach for Decarbonizing Computing\n\nDate &\; Time : Friday
Aug 18\, 2023\, 12PM\n\nVenue : CDS Seminar Hall (102)\n\n===============
==========================================================================
==\nAbstract\nThe exponential growth of cloud computing has been a definin
g trend of our time\, fueled by rapidly growing demands from data-intensiv
e and machine-learning workloads. Despite the end of Dennard scaling\, the
cloud’s energy demand grew more slowly than expected over the past deca
de due to the aggressive implementation of energy-efficiency optimizations
. Unfortunately\, there are few significant remaining optimization opportu
nities using traditional methods\, and moving forward\, the cloud’s cont
inued exponential growth will translate into rising energy demand\, which\
, if left unchecked\, will translate to increasing carbon emissions.\n\nIn
this talk\, I will argue for a Carbon First approach to designing cloud c
omputing platforms and systems by making carbon efficiency a first-class d
esign metric\, similar to traditional metrics of performance and reliabili
ty. I will explain how today’s systems can be made first carbon-aware by
exposing energy and carbon usage information to software platforms and th
en made carbon-efficient by providing control over the system’s carbon u
sage. I will present several systems mechanisms to enable such carbon awar
eness and management and present several application case studies on how m
odern cloud applications can employ these mechanisms to reduce their carbo
n footprint. I will end with some open research challenges in this emergin
g field of sustainable computing.\nBiography\nPrashant Shenoy is currently
a Distinguished Professor and Associate Dean in the College of Informatio
n and Computer Sciences at the University of Massachusetts Amherst. He rec
eived the B.Tech degree in Computer Science and Engineering from the India
n Institute of Technology\, Bombay and the M.S and Ph.D degrees in Compute
r Science from the University of Texas\, Austin. His research interests li
e in distributed systems and networking\, with a recent emphasis on cloud
and green computing. He has been the recipient of several best paper award
s at leading conferences\, including a Sigmetrics Test of Time Award. He s
erves on editorial boards of the several journals and has served as the pr
ogram chair of over a dozen ACM and IEEE conferences. He is a fellow of th
e ACM\, the IEEE\, and the AAAS.\n\nHost Faculty: Yogesh Simmhan\, CDS\n\n
—————————————————————–\n\nAbou
t: The IBM-IISc Hybrid Cloud Lab (IIHCL) hosted at IISc is curating the Cl
oud Computing Seminar series with guest speakers from Industry and Academi
a speaking about the latest technologies and research on Cloud and edge co
mputing\, distributed computing systems\, and AI/ML/Big Data platforms. Mo
re details at: http://iihcl.iisc.ac.in\n\n================================
==================================================\nALL ARE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:5@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230811T110000
DTEND;TZID=Asia/Kolkata:20230811T120000
DTSTAMP:20231102T045245Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-modeling-phys
iological-transport-at-scales-connecting-cells-to-organs/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS : “Modeling physiological transpor
t at scales: connecting cells to organs.”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
loquium\n\n_______________________________________________________________
___________________________\n\nSpeaker : Ms. Deepa Maheshvare\n\nS.R. Numb
er : 06-18-01-10-12-16-1-14025\n\nTitle : “Modeling physiological transp
ort at scales: connecting cells to organs.”\n\nResearch Supervisor: Prof
. Debnath Pal\n\nDate &\; Time : August 11\, 2023 (Friday) at 11:00 AM\
n\nVenue : Room No. 102 (CDS Seminar Hall)\n\n____________________________
______________________________________________________________\nAbstract\n
The physiological system is a complex network in which each organ forms a
subsystem\, and different subsystems interact to maintain the body’s ove
rall homeostasis. Within each subsystem\, functional networks exist at dif
ferent levels of complexity. The vessels that perfuse the cells within a t
issue mediate short- and long-distance communication. The ability to simul
taneously capture local and global dynamics by hierarchically bridging com
munication networks at different scales is a key challenge in holistic phy
siology modeling.\n\nWe present a scalable hierarchical framework that all
ows us to bridge diverse scales to model biochemicals’ production\, cons
umption\, and distribution in a tissue microenvironment. We developed a di
screte modeling framework to simulate the gradient-driven advection–disp
ersion-reaction physics of multispecies transport in multiscale systems. B
iochemical drift in the capillary network\, transcapillary exchange\, and
cellular reactions were modeled by translating physical space into a metam
odel with functional units. We define graph operators on the finite connec
ted network representation of the discrete functional units embedded in th
e metamodel. The governing differential equations are modeled to study the
inter-compartment dynamics of the well-mixed nodal volumes by formulating
the transport dynamics in the vascular domain and the transcapillary exch
ange as a ‘tank-in-series’ model. This allows our framework to scale t
o large networks and provides the flexibility to fuse multiscale models by
encoding imaging data of vascular topology and omics data of cellular rea
ctions to enhance systems-level understanding. Our framework is suitable f
or reducing the computational cost of spatially discretizing large tissue
volumes and for probing the effect of flow topology on biochemical transpo
rt to study structure-function relationships in tissues.\n\nNext\, we deve
loped a comprehensive and standardized data-driven modeling workflow to bu
ild kinetic models of cellular metabolism. There are significant challenge
s in developing computational models at the cellular level due to the subs
tantial variation in data across different experimental systems\, animal s
pecies\, and cell lines. We have created open\, free\, and FAIR (findable\
, accessible\, interoperable\, and reusable) assets that can be used to st
udy pancreatic physiology and glucose-stimulated insulin secretion (GSIS).
The data curation\, integration\, normalization and data fitting workflow
\, and a large database of metabolic data of the pancreatic beta-cell base
d on experimental and clinical data from 39 studies spanning 50 years of p
ancreatic\, islet\, and beta-cell research in humans\, rats\, mice\, and c
ell lines were used to construct a novel data-driven kinetic SBML (Systems
Biology Markup Language) model of GSIS in the pancreatic beta-cell. The m
odel consists of detailed glycolysis and phenomenological equations for bi
phasic insulin secretion coupled to cellular energy state\, ATP dynamics\,
and (ATP/ADP ratio). The predicted glucose-dependent response of glycolyt
ic intermediates and biphasic insulin secretion are in good agreement with
experimental measurements\, and our model predicts that the factors affec
ting ATP consumption\, ATP formation\, hexokinase\, phosphofructokinase\,
and ATP/ADP-dependent insulin secretion have a major effect on GSIS.\n\nFi
nally\, we present KiPhyNet\, an online network simulation tool connecting
cellular kinetics and physiological transport. The tool allows users to s
imulate and interactively visualize pressure\, velocity\, and concentratio
n fields for applications such as flow distribution\, glucose transport\,
and glucose-lactate exchange in microvascular networks.\n\nWhen extended f
or translational purposes in clinical settings\, the framework and pipelin
e developed in this work can advance the simulation of whole-body models a
nd are expected to have major applications in personalized medicine and dr
ug discovery.\n\n=========================================================
======================\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:1@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata;VALUE=DATE:20230807
DTEND;TZID=Asia/Kolkata;VALUE=DATE:20230809
DTSTAMP:20231007T192521Z
URL:https://cds.iisc.ac.in/events/cds-research-expo-2023/
SUMMARY:CDS Research Expo 2023
DESCRIPTION:We are pleased to inform you that the Department of Computation
al and Data Sciences\, Indian Institute of Science Bangalore is organizing
the CDS Research Expo on 7th and 8th August 2023. The two-day expo is jam
-packed with engaging sessions\, including plenary talks and presentations
by senior Ph.D. students\, poster sessions\, and industry-connect events.
The CDS Expo brings together researchers and faculty members of the CDS d
epartment\, where this expo serves as the best platform to research studen
ts to present their work to a larger audience\, share their ideas\, and ob
tain constructive feedback.\n\nWe cordially invite you to join us at this
event for interaction among students\, faculty members\, and top-notch res
earchers from academia and industry. Your presence would greatly contribut
e to the success of this event.\n\n\n\n\n\n\n\n\n\n\nPoster Presentation S
chedule\n\n\n\n\n\n\nOrganising Committee Members\nVaanathi Sundaresan\nRa
tikanta Behera\nChirag Jain\n\nVolunteers\nRaji Susan Mathew\nAjeya B S\nM
ohit Singhal\nTarun Nayak\nRamanujam Narayanan\nArchakam S Anudeep
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VEVENT
UID:4@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230731T113000
DTEND;TZID=Asia/Kolkata:20230731T123000
DTSTAMP:20231007T192554Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-cds-sparsificatio
n-of-reaction-diffusion-dynamical-systems-in-complex-networks/
SUMMARY:Ph.D. Thesis {Colloquium}: CDS : “Sparsification of Reaction-Diff
usion Dynamical Systems in Complex Networks.”
DESCRIPTION:Speaker : Mr. Abhishek Ajayakumar\n\nS.R. Number : 06-18-01-10-
12-18-1-16176\n\nTitle : “Sparsification of Reaction-Diffusion Dynamical
Systems in Complex Networks.”\n\nResearch Supervisor: Prof. Soumyendu R
aha\n\nDate &\; Time : July 31\, 2023 (Monday) at 11:30 AM\n\nVenue : T
he Thesis Colloquium will be held on HYBRID Mode # 102 CDS Seminar Hall /M
ICROSOFT TEAMS.\n\nPlease click on the following link to join the Thesis C
olloquium:\n\nMS Teams link:\n\nhttps://teams.microsoft.com/l/meetup-join/
19%3ameeting_MDFmNDdlYWYtOTI0ZC00Njg5LWEwZjUtNzZhZjNiY2I0MDhm%40thread.v2/
0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22O
id%22%3a%22f67fa1e8-f789-4048-bb53-c2880088cc5d%22%7d\n\n_________________
_________________________________________________________________________\
nAbstract\nGraph sparsification is an area of interest in computer science
and applied mathematics. Sparsification of a graph\, in general\, aims to
reduce the number of edges in the network while preserving specific prope
rties of the graph\, like cuts and subgraph counts. Modern deep learning f
rameworks\, which utilize recurrent neural network decoders and convolutio
nal neural networks\, are characterized by a significant number of paramet
ers. Pruning redundant edges in such networks and rescaling the weights ca
n be useful. Computing the sparsest cuts of a graph is known to be NP-hard
\, and sparsification routines exist for generating linear sized sparsifie
rs in almost quadratic running time. The complexity of this task varies\,
and it is closely linked to the level of sparsity we desire to achieve. In
our study\, we extend the concept of sparsification to the realm of react
ion-diffusion complex systems. We aim to address the challenge of reducing
the number of edges in the network while preserving the underlying flow d
ynamics.\n\nSparsification of such complex networks is approached as an in
verse problem guided by data representing flows in the network\, where we
adopt a relaxed approach considering only a subset of trajectories. We map
the network sparsification problem to a data assimilation problem on a Re
duced Order Model (ROM) space with constraints targeted at preserving the
eigenmodes of the Laplacian matrix under perturbations. The Laplacian matr
ix is the difference between the diagonal matrix of degrees and the graph
’s adjacency matrix. We propose approximations to the eigenvalues and ei
genvectors of the Laplacian matrix subject to perturbations for computatio
nal feasibility and include a custom function based on these approximation
s as a constraint on the data assimilation framework.\n\nIn the latter pha
se of the study\, we developed a framework to enhance POD-based model redu
ction techniques inreaction-diffusion complex systems. This framework inco
rporates techniques from stochastic filtering theory and pattern recogniti
on.\n\nGetting optimal state estimates from a noisy model and noisy measur
ements forms the core of the filtering problem. By integrating the particl
e filtering technique\, we generate the reaction-diffusion state vector at
various time steps\, utilizing noisy measurements obtained from ROM. To e
nsure the framework’s effectiveness\, we make intermittent updates to th
e system variables during the particle filtering step\, employing the care
fully crafted sparse graph. The framework is utilized for experimentation\
, and results are presented on random graphs\, considering the diffusion e
quation on the graph and the chemical Brusselator model as the dynamical s
ystems embedded in the graph. We discuss the method’s limitations\, and
the proposed framework is evaluated by comparing its performance against t
he Neural ODE-based approach\, which serves as a compelling reference due
to its demonstrated robustness in specific applications.\n\n==============
=================================================================\n\nALL A
RE WELCOME
CATEGORIES:Ph.D. Thesis Colloquium
END:VEVENT
BEGIN:VEVENT
UID:3@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230705T110000
DTEND;TZID=Asia/Kolkata:20230705T120000
DTSTAMP:20231007T192549Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-hybrid-cds-05-july-2
023%e2%80%b3constrained-stochastic-differential-equations-on-smooth-manifo
lds/
SUMMARY:Ph.D: Thesis Defense: HYBRID: CDS: ″Constrained Stochastic Differ
ential Equations on Smooth Manifolds.”
DESCRIPTION:\n\n\n\nSpeaker : Mr. Sumit Suthar\n\
n\nS.R. Number : 06-18-01-10-12-17-1-14862\n\n\n\n\nTitle
: “Constrained Stochastic Differential E
quations on Smooth Manifolds.“\n\nResearch Supervisor: Prof. Soumyendu R
aha\n\n\n\n\n\nDate &\; Time : July 05\, 2023 (Wednesday)\
, 11:00 AM\n\n\n\n\nVenue : The Thesis D
éfense will be held on HYBRID Mode # 102 CDS Seminar Hall /MICROSO
FT TEAMS\n\nPlease click on the following link to join the Thesis Defens
e:\n\nMS Teams link\n\nhttps://teams.microsoft.com/l/meetup-join/19%3ameet
ing_YjhkNjhjMzktMjQ2OC00YzYwLTkxZWQtNDJiMWViZDUzYTc0%40thread.v2/0?context
=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%
22f67fa1e8-f789-4048-bb53-c2880088cc5d%22%7d\n\n\n\n______________________
____________________________________________________________________\nAbst
ract\n\n\n\n\n\n\n\n\n\n\n\nDynamical systems with uncertain fluctuations
are usually modelled using Stochastic Differential Equations (SDEs). Due
to operation and performance related conditions\, these equations may als
o need to satisfy the constraint equations. Often the constraint equations
are “algebraic”. Such constraint equations along with the given SDE
form a system of Stochastic Differential-Algebraic Equations (SDAEs).\nT
he main objective of this thesis is to consider these equations on smooth
manifolds. However\, we first consider SDAEs on Euclidean spaces to unde
rstand these equations locally. A sufficient condition for the existence
and uniqueness of the solution is obtained for SDAEs on Euclidean spaces.
We also give necessary condition for the existence of the solution. Base
d on the necessary condition\, there exists a class of SDAEs for which th
ere is no solution. Since all SDAEs are not solvable\, we present methods
and algorithms to find approximate solution of the given SDAE. \nIn o
rder to extend this work to smooth manifolds\, we consider second order s
tochastic differential geometry to construct Schwartz morphism to represe
nt SDEs with drift that are driven by p-dimensional Wiener process. We sh
ow that it is possible to construct such Schwartz morphisms using what we
call as diffusion generators. We demonstrate that diffusion generator ca
n be constructed using flow of second order differential equations\, in p
articular using regular Lagrangians. The results obtained for SDAEs on Eu
clidean spaces are extended to SDAEs on smooth manifolds using the framew
ork of diffusion generators. We show that the results obtained for SDAEs
on Euclidean spaces translate to the manifold setting with minimal modifi
cations. We have derived Ito-Wentzell’s formula on manifolds in the fra
mework of diffusion generators to obtain approximate bounded solution wit
h unit probability. Another type of approximate solution is bounded soluti
on such that the probability of explosion is bounded by α <\; 1. We pr
esent algorithms to compute approximate solutions of both type. This has
been demonstrated with an example of SDAE on a sphere.\n=================
==============================================================\n\n\n\n\nAL
L ARE WELCOME\n\n\n\n\n\n
CATEGORIES:Thesis Defense
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