News

Jan 26, 2026 Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning has been accepted to ICLR 2026. We propose a flow-based generative policy for one-shot action generation, enabling much faster training and inference (across offline RL, GCRL, and BC). ICLR 2026 Arxiv Paper
Aug 22, 2025 Thrilled to begin my Ph.D. in Robotics at Cornell University!
Excited to explore cutting-edge research in safe and intelligent autonomy. Supported in my first semester by the Cornell Fellowship.
Jul 15, 2025 Paper accepted to CDC 2025, Rio de Janerio. “FAWAC: Feasibility Informed Advantage Weighted Regression for Persistent Safety in Offline Reinforcement Learning” Coauthored with Dr. Zhanhong Jiang, Dr. Soumik Sarkar and Dr. Cody Fleming. CDC 2025 Arxiv Paper
Jun 17, 2025 Defended my MS thesis. Title - "Towards safe and efficient offline reinforcement learning - learning safety constraints and expressive policies via generative modeling"