Professor.
Computational Scientist.
ML Engineer.
Founder, Zenteiq.
I'm a
Department of Computational and Data Sciences, IISc Bangalore.
About MeAbout Me
Know Me More
Dr. rer. nat. Sashikumaar Ganesan
Professor and Chair, Department of Computational and Data Sciences (CDS), Indian Institute of Science (IISc), Bangalore, India
Founder, Zenteiq Edtech Private Limited. a deep tech start-up incubated at FSID, IISc.
About Me
I am Sashikumaar Ganesan, a faculty member in CDS at IISc, Bangalore. My journey in academia and research spans over two decades, with a steadfast focus on advancing the frontiers of computational sciences.
Administrative Roles
- Chair, CDS, IISc: Steering the department since February 2018, with a vision to integrate computational and data sciences for groundbreaking research and education.
- Founder, Zenteiq Edtech Private Limited: Revolutionizing Higher Education through Advanced AI - A deep tech startup dedicated to transforming the learning experience in higher education with cutting-edge artificial intelligence.
- Co-Convenor (2022-23), Kotak IISc AI-ML Centre: A joint initiative of IISc and Kotak Mahindra Bank to promote research and education in AI and ML.
- Founding Chair & member, PCC, M.Tech. Data Science & Business Analytics:, aligning it with industry needs and technological advancements.
- Programme Director, Advanced Program in Computational Data Science and AI and AI & MLOps: Leading these innovative upskilling programs since July 2020 and October 2022, respectively, to cultivate expertise in cutting-edge areas of technology.
- Faculty Associate, IMSc, IISc: A joint initiative of IISc and IMSc to offer interdisciplinary research program.
I joined IISc in 2011 as an Assistant Professor. Before joining the institute, I served as a Research Associate at Dept. of Aeronautics, Imperial College London and as an Alexander von Humboldt Fellow at Weierstrass Institute Berlin. I received my Ph.D. (Dr. rer. nat) in 2006 from Institute of Analysis und Numerics, Otto-von-Guericke University, Germany.
- Name: Prof. Sashikumaar Ganesan
- Email: sashi@iisc.ac.in
- Phone: (+91) 802 293 2902
- Address:
Department of Comptational & Data Sciences,
Indian Institute of Science, Bangalore.
Karnataka, India - 560012
15+
Years of Experiance
25+
Students Mentored
60++
Published Works
1000+
Professionals Upskilled in AI/ML
Research
Interests
My Research group, Scientific Machine Learning and Operations (STARS),is at the forefront of integrating scientific computing with machine learning. Our group's mission is to harmonize the precision of scientific computing with the agility and adaptability of machine learning, paving the way for breakthroughs in both theoretical and applied domains. We delve into:
Artificial Intelligence (AI)
Focused on developing AI-based recommender systems for combat aircraft, with a special emphasis on emitter classification and multi-sensor data fusion. Additionally, there's a key focus on constructing platform engineering frameworks for the integration of diverse ML models. In the educational sector, the approach includes creating AI-driven predictive models based on learners' activities and developing tools for skill and performance assessments.
Scientific Machine Learning (SciML)
Our research is centered on integrating advanced parallel computing techniques, especially high-performance (hp) Physics-Informed Neural Networks (PINNs) and GPU acceleration, to enhance computational efficiency and accuracy. By infusing traditional numerical schemes with data-driven approaches, we align our work with the forefront of SciML trends, ensuring a harmonious blend of scientific precision and technological innovation.
Machine Learning Operations (MLOps)
Crafting scalable ML algorithms suited for diverse computational environments. Innovating in distributed training techniques and cloud computing applications. Implementing rigorous ML model and data version control systems. Establishing efficient CI/CD pipelines for ML deployment and operations.
Computational Science
Specializing in finite element analysis. Advanced modeling of multiphase flows, fluid-structure interactions, turbulent flows, and epidemiological modeling. Developing hybrid CPU-GPU parallel algorithms and hardware-aware scalable parallel implementations.
Research
Publications
Book
|
S. Ganesan & L. Tobiska :
Finite Elements: Theory and Algorithms.
Cambridge University Press, 2017, ISBN: 9781108415705.
|
Journal Articles
Book Chapters
|
S. Ganesan, A. Hahn, K. Simon, L. Tobiska:
Finite Element Computations for Dynamic Liquid-Fluid Interfaces.
In: M. T. Rahni, M. Karbaschi and R. Miller, editors, Computational Methods for Complex Liquid-Fluid Interfaces, CRC Press, Taylor and Francis Group, (2015) Ch.16, Pages: 331--351, ISBN: 9781498722087 (online) |
|
S. Ganesan, B. Pal, S.Srivastava:
Simulation of two-phase flows with surfactants.
In: N. K. Gupta, A. V. Manzhirov, and R. Velmurugan, editor, Topical problems in theoretical and applied mechanics, Elite Publishing House Pvt. Ltd., (2013) Pages: 418 - 425, ISBN: 978-81-88901-55-5 |
|
S. Ganesan, Bhanu Teja:
A multi-level finite element discretization for efficient solution of multidimensional population balance system.
In: S. Sundar, editor, Advances in PDE Modeling and Computation, Ane Books Pvt. Ltd., (2013), Pages: 105 - 118 (online) |
|
S. Ganesan, S. Rajasekaran, L. Tobiska:
An ALE-based finite element method for the simulation of an impinging droplet on a hot surface.
In: S. Sundar, editor, Advances in PDE Modeling and Computation, Ane Books Pvt. Ltd., (2013), Pages: 35 - 53 (online) |
|
S. Ganesan, L. Tobiska:
Finite Element Simulation of an Impinging Liquid Droplet.
In: Albrecht Bertram and Jürgen Tomas, editor, Micro-Macro-
Interactions In Structured Media and Particle Systems, Springer,
(2008) (online) |
|
S. Ganesan, L. Tobiska:
A Finite Element Method for the Simulation of a Liquid Droplet Impinging on a Solid Surface.
In: Palle Jorgensen, Xiaoping Shen, Chi-Wang Shu and Ningning Yan, editor, RECENT ADVANCES IN COMPUTATIONAL SCIENCES, World Scientific, (2008)
(online) |
Teaching
Courses
AI & Machine Learning with Python: Jan-Apr 2023 Online CCE course
AI/ML is a CCE-PROFICIENCE semester long course aimed to upskill students, researchers & working professionals on Computational Thinking with applications to ML/AI and Data Science in a "Learn-by-Doing" approach. Live lectures will be conducted in the evenings so that regular students from other institutes/colleges and working professionals can participate without affecting their normal schedule.
Admissions open for Jan - Apr 2023 Term. Apply online!
Introduction to Computing for AI & Machine Learning, Jan - Apr 2023 Credits-3:1, DA 203o
This four-credit course will be offered every year in the Jan-April term as a core course to M.Tech. (Online) programme. This course is aimed at building the foundation of computational thinking with applications to Artificial Intelligence and Machine learning (AI & ML). Besides, how to build a neural network and how to train, evaluate and optimize it with TensorFlow will also be covered in this course.
Introduction to Computing for AI & Machine Learning, Aug 2022 (2:1), DS 226
"
.”
HPCDS:: High-Performance Computing and Data Science (Online CCE course) Aug 21
HPCDS is a CCE-PROFICIENCE semester long course aimed at students, researchers, and professionals working on computational modeling or Data science applications who wish to upskill. This course will be organized during evening hours so that regular students from other institutes/colleges and working professionals can participate without affecting their normal schedule.
NMSC::Numerical Methods (DS 288) / Introduction to Scientific Computing (UE 201) Oct 2020
Finite Elements: Theory and Algorithms(DS 291) Oct 2020
Consultancy
Projects
Shell India
Large Scale Simultion augmented by AI. This shell industrial project aims to develop a real-time environment-aware simulation of a wind turbine farm through combination of techniques in Deep Learning, Model Order Reduction and High-Performance Computing.
ITC Limited
Confidential.
Indo-German Partnership Program
The purpose of this four-year program (2020 - 2024) is to foster hardware-aware numerical schemes and scalable algorithms to harness future multi-node, multi-GPU supercomputers. The key focus is not only developing hardware-aware algorithms but to train young researchers in scientific computing through joint supervision, workshops, summer/winter schools.
Confidential.
Social
Impacts
