Christopher Hsu
Research Scientist at University of Pennsylvania
Philadelphia, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolChristopher Hsu is a research scientist and PhD candidate in Electrical and Systems Engineering at the University of Pennsylvania with eight years of experience applying deep learning, reinforcement learning, and spatial AI to robotics and perception problems. He currently leads customer-funded projects at the U.S. Army DEVCOM Army Research Laboratory, serving as PI on multi-robot and spatial-AI efforts that secured nearly $2M in funding. His work spans bench algorithm development to technical proposal review and subject-matter expertise, bridging academic research and defense-focused engineering. Earlier research includes scalable multi-agent RL policies (Parameter Shared Set Policy) for cooperative tracking and practical diagnostics for mechanical systems, showing a strong mix of theoretical innovation and real-world deployment. Based in Philadelphia, he combines advanced robotics research with hands-on systems integration experience from competitions and fielded projects. An often overlooked strength is his ability to translate complex multi-agent models into efficient, decentralized policies suited for resource-constrained robotic teams.
8 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree, Mechanical Engineering, Bachelor’s Degree, Mechanical Engineering at Villanova UniversityDoctor of Philosophy - PhD, Electrical and Systems Engineering, Doctor of Philosophy - PhD, Electrical and Systems Engineering at University of Pennsylvania
Chinese
Github Skills (38)
optimization9
python8
ros8
hyperparameter-optimization8
reinforcement-learning8
machine-learning8
large-language-models8
parallel8
pytorch8
data-science8
multi-agent-reinforcement-learning8
llm7
llm-inference7
tensorflow7
deep-learning7
Programming languages (4)
Github contributions (5)
christopher-hsu/scalableMARL
Nov 2021 - Jul 2022
Multi-Agent Reinforcement Learning (MARL) method to learn scalable control polices for multi-agent target tracking.
Contributions:27 commits, 17 pushes, 6 comments in 8 months
multi-agentmulti-agent-reinforcement-learning
christopher-hsu/ttenv
Jan 2020 - Oct 2020
Target tracking environments for RL
Contributions:10 commits, 10 PRs, 59 pushes in 8 months
target-trackingreinforcement-learningenvironmentstracking
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