[CDS Seminar] Systems+AI Research @ Microsoft

Department of Computational and Data Sciences (CDS) Seminar

Title: Systems + AI Research @ Microsoft

Speakers: Saravan Rajmohan, Chetan Bansal, Anjaly Parayil and Mayukh Das, Microsoft

Date/Time: Thu Feb 15, 230-345PM

Venue: SERC Auditorium 4th Floor

Abstract: 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 workloads, sustainability push, the need for self-managing cloud environments and the complex challenges that arise out of its sheer scale. To solve these challenges, at M365 Research group in Microsoft, we have built a cross-domain research team focusing on applied research on “ML for Systems” to bring a step function improvement in Cloud Efficiency and Reliability. The group comprises of 30+ PhD Researchers, Research Fellows and Interns in Bangalore (co-located with MSR India), Redmond and Beijing. We aim to partner with top research institutions to drive innovation and leverage the immense scientific knowledge and expertise to bring new ideas into practice. Our goal is to explore all the dimensions from collaborative research with faculty members, to establishing knowledge sharing seminars and building a long-term talent pipeline. You can read more about our research group here: https://aka.ms/systems-innovation.

Bios:

• Saravan Rajmohan is a Partner Director at Microsoft, leading the M365 Research group focused on AI and Applied Research. He oversees diverse teams in the US, Latin America, UK, India, and China, conducting groundbreaking research 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 Research labs. They drive AIOps Cloud Intelligence research, improving efficiency and reliability across M365 services. His Efficient AI team optimizes generative 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.
• Chetan Bansal is a Principal Research Manager at Microsoft leading the AIOps and Cloud Intelligence research. His research is focused on improving Cloud Efficiency and Reliability through AI and data-driven methods. His research has had a significant impact on Cloud Efficiency and Developer Productivity at Microsoft with millions of dollars of savings. He has published 30+ papers in top international 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 work on Formal verification of Web Protocols was recognized by awards from Facebook, Mozilla and Google.
• Anjaly Parayil is a Senior Researcher at M365 Research leading applied research at the intersection of efficiency and reliability of cloud services.  In particular, she works on data driven optimizations to ensure continuous availability of cloud services as well as workload aware techniques for improving the efficiency of Cloud infrastructure. Before joining Microsoft, Anjaly was a Post Doc with the Computational and Information Sciences Directorate at the US Army Research Laboratory where she focused on Reinforcement Learning and Bayesian Inferencing. Anjaly completed her graduate studies at the Department of Aerospace Engineering 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 intelligence, control systems and optimization.
• Mayukh Das is a Senior Researcher at Microsoft driving applied AI research for Cloud Efficiency.  In particular, he works on varied decision-making problems for configuration tuning for performance optimization of cloud services, for capacity provisioning, for power and energy optimization, and, operational efficiency of ML workloads. He completed his PhD from UT Dallas and his thesis work was focused on Guided Reinforcement Learning and Probabilistic Modeling in Noisy domains. Prior to Microsoft he was at Samsung Research solving Edge-AI problems. He serves on the program committee of various conferences including 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.

Host: Yogesh Simmhan, CDS

ALL ARE WELCOME