Mayank Baranwal

Mayank Baranwal

Senior Scientist, Data and Decision Sciences

About

Mayank Baranwal is a Senior Scientist with the Tata Consultancy Services (TCS) research division in Mumbai. He also holds an Adjunct appointment with the Systems and Control group at the Indian Institute of Technology, Bombay (IITB). Before joining TCS, he was a postdoctoral scholar in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor. He received his Bachelor in Mechanical Engineering in 2011 from the Indian Institute of Technology, Kanpur (IITK), an MS in Mechanical Science and Engineering in 2014, an MS in Mathematics in 2015, and PhD in Mechanical Science and Engineering in 2018, all from the University of Illinois at Urbana-Champaign (UIUC).

His research interests are modeling, optimization, control, and inference in network systems with applications to distributed optimization, supply-chain networks, power networks, control of microgrids, bioinformatics, computational biology, and deep learning theory. Mayank is a recipient of the Institute Silver Medal in 2011 (IIT Kanpur), the ME Outstanding Publication Award in 2017 (the University of Illinois), the Young Scientist Award in 2022 (Tata Consultancy Services), and the AI Research Award in 2024 (Nasscom AI). He is also a Young Associate with the Indian National Academy of Engineering (INAE).

Optimization is one of the most powerful tools for decision making, yet it remains inaccessible to most domain experts because turning real-world goals and constraints into formal mathematical models requires specialized expertise. This talk explores how large language models can change that. We present an AI co-pilot that translates natural-language problem descriptions into optimization models, code, and solution workflows, while keeping humans in the loop for oversight and refinement.

For dynamic, process-oriented problems, the framework further uses LLM-generated digital twins and agentic reinforcement learning to discover effective decision policies. By lowering the barrier between domain knowledge and advanced optimization, this approach aims to make optimization usable by everyone, not just experts, and to reshape how complex decisions are modeled, solved, and deployed.

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