News
| Jun 17, 2026 | Two papers accepted to IROS 2026. One presents a decentralized generative framework for multi-agent coordination, where distilled communication enables effective decision-making under partial observability. The other develops a value-guided flow-matching approach for robot policy learning, enabling scalable integration of RL objectives into expressive controllers. Preprints and code to be released soon! |
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| May 15, 2026 | LexiSafe received a Best Paper Nomination at IEEE/ACM ICCPS 2026. The work introduces a lexicographic prioritization framework for safe RL using partial policy freezing to reduce safety violations while maintaining strong performance in safety-critical CPSs, with an extension to multiple hierarchical safety objectives.  |
| 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).  |
| 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.  |
| Jun 17, 2025 | Defended my MS thesis. Title - "Towards safe and efficient offline reinforcement learning - learning safety constraints and expressive policies via generative modeling" |