I am a Ph.D. candidate in Computer Science at Vanderbilt University, working under the supervision of Prof. Abhishek Dubey and closely collaborating with Dr. Ayan Mukhopadhyay. My research interests include reinforcement learning, deep learning, planning, with a focus on applications in autonomous cyber-physical systems. I am particularly interested in developing innovative approaches for decision-making in high-dimensional, stochastic, and non-stationary environments, as well as enhancing the safety and efficiency of autonomous vehicles.

Prior to my doctoral studies, I earned my M.S. in Computer Engineering from Northwestern University, where I worked with Prof. Qi Zhu on securing connected and autonomous vehicles. My master’s thesis on Sybil Attack Detection in VANET received the Northwestern Best MS Computer Engineering Thesis Award. I completed my B.S. in Computer Engineering with a dual degree in Computer Science from Rensselaer Polytechnic Institute. My research has been published in conferences such as ICLR, NeurIPS, AAMAS and ICCPS, and I have been honored with awards including the Vanderbilt Provost’s Pathbreaking Discovery Award, Vanderbilt C.F. Chen Best Paper Runner-Up Award.

Services

Conference & Journal Reviewer

• International Conference on Learning Representations (ICLR 2026)
• The ACM Web Conference (WWW 2026)
• Neural Information Processing Systems (NeurIPS 2025)
• Knowledge Discovery and Data Mining (KDD 2025)
• International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025)
• International Joint Conference on Neural Networks (IJCNN 2025)
• International Conference on Neural Information Processing (ICONIP 2024)
• IEEE International Transportation Systems Conference (ITSC 2022)
• IEEE Transactions on Intelligent Vehicles
• ACM Transactions on Computing for Healthcare
• IEEE Internet of Things Journal

Technical Program Committee

• Artifact Evaluation Committee, ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2024/2025/2026)
• International Conference on Data Mining and Big Data (DMBD 2024)

Research

My research focuses on developing intelligent decision-making systems for autonomous cyber-physical systems, with a particular emphasis on addressing challenges in complex, uncertain, and dynamic environments. The work spans several interconnected areas:

Decision Making with High Dimensional Action Space

• Latent Macro Action Planner: Developed for offline reinforcement learning, enabling efficient decision-making in high-dimensional, stochastic environments through learned temporally extended actions.

Decision Making in Non-Stationary Environments

• Adaptive Monte Carlo Tree Search: Enables safe exploration and online adaptation to changing dynamics in model-based reinforcement learning tasks.
• NS-Gym Toolkit: Provides standardized evaluation environments for online decision-making algorithms in dynamically changing settings.

Runtime Safety Assurance of Autonomous Vehicles

• Dynamic Simplex framework: Improves performance without compromising safety in autonomous systems through planning with multiple generative models in dynamic environments.
• Advanced sampling techniques: Enhances robustness of autonomous vehicle systems through high-risk scenario generation in AV testing.
• Automated testing framework: Evaluates autonomous vehicles under adversarial conditions in simulations.

Securing Connected and Autonomous Vehicles

• Hybrid GCN-RNN Model: Developed to detect Sybil attacks in connected vehicle networks.
• Dual Cyber-Physical Blockchain Framework: Created for efficient security in large-scale vehicular networks.

Find out publications for each of these projects.