Ayan Mukhopadhyay

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020.

His research spans critical domains in smart infrastructure systems with particular focus on:

• Developing robust decision-making frameworks for cyber-physical systems under uncertainty
• Creating multi-agent solutions for emergency response optimization
• Designing machine learning approaches for urban mobility and energy management
• Building proactive incident detection pipelines using heterogeneous data sources

Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration.

Award highlights include:

• CARS Post-Doctoral Fellowship (2019)
• Best Paper Award at ICLR's AI for Social Good Workshop
• Victor Lesser Distinguished Dissertation Award Nomination (2020)

Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.