Stillwell_fellowship
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I was awarded the Stillwell Fellowship by the Department of Aeronautical Engineering at UIUC! I was also honored with an supplementary Robert Beatty Fellowship!
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I was awarded the Stillwell Fellowship by the Department of Aeronautical Engineering at UIUC! I was also honored with an supplementary Robert Beatty Fellowship!
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I was offered a summer internship at NASA Goddard Space Flight Center with a focus on “Decision Making for Collision Avoidance via Machine Learning”!
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I was awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)!
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Our paper titled POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints got accepted at the Robotics: Science and Systems (RSS) 2024!
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Our paper titled Optimal Robotic Assembly Sequence Planning: A Sequential Decision-Making Approach has been accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024!
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I was invited to talk about my work at the NASA AI Showcase at Goddard Space Flight Center! This included an hour-long oral presentation to a crowd of over 50 NASA engineers and scientists, followed by a poster session.
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Our paper titled Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems got accepted at the Conference on Decision and Control (CDC) 2024!
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For the Fall 2024 semester, I got the opportunity to act as the teaching assistant for my advisors’ “ME 292B/EE290: Modeling and Control of Multi-Agent Systems” special topics course.
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I completed my NASA intership! I developed a novel transformer architecture for space weather prediction, along with its effects on satelite orbit perturbation. This was then trained on real satellite data to improve uncertainty quantification efforts.
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Our paper titled Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment got accepted at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2025!
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I passed my UC Berkeley Ph.D Preliminary Exam!
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I was invited for an oral presentation and poster session at the Northern California Aerospace Symposium (NCAS) 2025!
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I delivered a lightning talk at the Spring 2025 BAIR Robotics Workshop: The Dawn of a General Purpose Robotics Era!
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I gave an hour long presentation on my paper titled Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment at the UC Berkeley Control Seminar!
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Our paper titled DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories got accepted at the Conference on Robotics: Science and Systems (RSS) 2025!
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Short description of portfolio item number 2
Published in Texas ScholarWorks, 2021
Near-earth space is geopolitically and commercially contested, and in need of environmental protection. To achieve space safety, security, and sustainability, we are developing ASTRIAGraph, a framework that enables monitoring, assessment, and verification of space actor behavior in the context of legal and policy instruments.
Published in AIAA Journal of Guidance, Control, and Dynamics, 2023
One of the challenges for flying quadrotors in cluttered envi-ronments is to optimize their trajectories subject to collision avoidance constraints in real time. Along such a trajectory, the position of the quadrotor must stay within a set of collision-free corridors. Each corridor is a bounded convex flight space.
Published in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Proposed a new formulation of the robotic assembly sequencing problem as a Markov Decision Process. Then showed how a class of methods called Graph Exploration Assembly Planners (GEAPs) can be used to gather optimal assembly sequences from a graph. We then showcased a deep reinforcement learning extension for handling very complex structures, all while handling diverse constraints.
Published in 2025 International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025
Robots collaborating for shared goals face a challenge: determining each agent’s contribution to team success or failure. This paper introduces LLM-MCA and LLM-TACA, methods that use Large Language Models to assign credit and even tasks to individual agents, significantly outperforming existing approaches.
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Graduate Class, University of California, Mechanical Engineering, 2024
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