Title: Reinforcement Learning for Complex Optimization: From Quantum Computing to Hybrid Energy and Next-Gen Mobility
Date and time: 20 March 2025 (Thursday), 9:30 – 10:30 a.m.
Venue: Online
Meeting link: Link
Abstract: Reinforcement learning (RL) is redefining complex optimization by enabling intelligent decision-making across diverse domains. This talk will primarily focus on RL-based quantum optimization, where RL enhances combinatorial and variational quantum algorithms for improved efficiency and scalability. Additionally, I will discuss RL-driven data valuation strategies for complex heterogeneous datasets, addressing critical challenges in data-centric optimization.
Beyond these core areas, I will introduce future research directions, including RL-integrated game theory for UAV cooperation, mixed-integer optimization using RL (MIOC-RL) for hybrid electric vehicle (HEV) energy management, and RL-based optimization for hybrid battery systems in electric vehicles, focusing on energy efficiency and longevity. I will also explore RL-driven multi-objective optimization for next-generation mobility solutions like Hyperloop. By bridging quantum computing, energy systems, and autonomous mobility, this talk aims to highlight the transformative potential of RL in solving high-dimensional, multi-criteria optimization challenges.
Bio: Dr. Nilanjan Mukherjee did his B.Tech in Electrical Engineering from West Bengal University of Technology in 2012, followed by M.Tech in Control Systems Engineering from Indian Institute of Technology, Kharagpur in 2015. Thereafter, he worked as a research engineer at Aeronautical Development Agency in Bangalore in the Light Combat Aircraft Division till 2019, followed by a Ph.D in Machine Learning from Indian Institute of Technology, Kharagpur as TCS research fellow in December, 2024. Currently, he is working as a visiting faculty in the Department of Computer Science and Engineering, University of Calcutta since 21st March, 2024.