Paper 'AdaCred: Adaptive Causal Decision Transformers with Feature Crediting' accepted to AAMAS 2025 (Dec 2024).
Paper 'RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator' accepted to CORL 2024 (Sep 2024).
Paper 'STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents' accepted to L4DC 2024 (Jul 2024).
Published research in top-tier venues including CORL, L4DC, AAMAS, IMS, and IJCNN.
Serving as reviewer for NeurIPS 2024, ICLR 2025, ICML 2025, AISTATS 2024, and IJCNN 2024.
Background
Robotics researcher focused on building intelligent, generalizable autonomous agents that operate in complex physical environments.
Explores the intersection of Robotics, Generative Modeling, Computer Vision, and Reinforcement Learning, with emphasis on learning from limited supervision and unstructured data.
PhD work bridges perception, prediction, and control through compositional and causal ML models for robust decision-making in real-world scenarios.
Has a proven track record of building production-scale ML systems integrating perception, prediction, and control for autonomous agents.
Previously led a 20-member student autonomous vehicle team, deploying full perception, planning, and control stacks on real hardware.