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), and a Guest appointment with the Indian Institute of Management, Mumbai (IIM-Mumbai). 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 (from IIT Kanpur), the ME Outstanding Publication Award in 2017 (from the University of Illinois), the Young Scientist Award in 2022 (Tata Consultancy Services), the Gold Award for Best Smart Technology in Electricity Transmission in 2023 (India Smart Grid Forum), and the third prize in the L2RPN-Delft Challenge in 2023.
In this workshop, you'll journey through Reinforcement Learning (RL), starting with fundamental concepts and advancing to complex techniques, focusing on real-world applications. Time permitting, you'll also explore how Large Language Models (LLMs) can optimize RL reward functions in a human-centric manner. Whether you're a seasoned AI professional or just beginning, this workshop equips you with the skills and knowledge to tackle real-world challenges using RL.
Discover how cutting-edge technologies leverage Reinforcement Learning (RL) to achieve groundbreaking results! For instance, the revolutionary generative model ChatGPT utilizes RL techniques behind the scenes. The core principle driving ChatGPT is Reinforcement Learning from Human Feedback (RLHF), which aligns Large Language Models (LLMs) with human preferences. This demonstrates the immense potential of RL to solve real-world problems and transform industries. Join our workshop to harness the power of RL and become a part of the AI revolution!
Read MoreWhile the potential of reinforcement learning (RL) in automated driving systems is immense, it's not without its challenges. One of the major hurdles is creating precise reward functions that ensure safe, efficient, and human-like driving behavior. Despite this, RL's ability to adapt to various traffic conditions makes it a promising tool for developing intelligent driving systems.
Using large language models (LLMs) as a stand-in for reward signals introduces a novel approach. LLMs can intuitively steer RL agents toward desired outcomes by interpreting user goals into reward signals through natural language. This innovative method holds potential across multiple applications, such as automated driving and dynamic traffic management.
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read More