Grace Cai
M.S. Student in Computer Science · University of Maryland, College Park
I am an M.S. student in Computer Science at the University of Maryland, College Park, where I also received B.S. degrees in Computer Science (with High Honors) and Physics. I am advised by Prof. Ming C. Lin in the GAMMA Lab, where I work on physics-based and differentiable dynamics models of micro-mobility vehicles for simulation and autonomous navigation.
My research interests lie at the intersection of physically-based simulation, differentiable programming, and robotics. I am interested in building models of the physical world that are faithful enough to capture real dynamics and differentiable enough to be used directly for learning, planning, and control of autonomous systems.
Previously, I was a World Model Systems Engineering Intern on the Autonomous Vehicles team at NVIDIA, where I studied how sensor degradation affects obstacle perception, and a Software Engineering Intern at Uber. Outside of research, I enjoy golf and baking.
I am applying to Computer Science Ph.D. programs for Fall 2027.
Publications
* denotes equal contribution
Differentiable Dynamics for Autonomous Micro-Mobility Navigation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026
Project Page / Paper / Poster
GM3: A General Physical Model for Micro-Mobility Vehicles
IEEE International Conference on Robotics and Automation (ICRA), 2026
Project Page / Paper / arXiv / Video / Poster
Work Experience
Evaluating LiDAR sensor capabilities in NVIDIA Isaac Sim and generating digital twins of mechanical parts for a part database.
Developed MagLev workflows to quantify how sensor degradation affects obstacle perception performance. Built an interactive dashboard to compare feature-level performance across sensor configurations, and implemented an AI agent that investigates failures and generates analysis reports.
Implemented multimodal support in Go and Python to analyze images for privacy risks, increasing recall by 25%. Extended the benchmarking suite to assess accuracy and built a multimodal RAG pipeline to semantically retrieve images using a vector database.
Built a proof-of-concept LLM document summarization tool with Python, Streamlit, and LangChain, and integrated it into an internal document review platform using Go, TypeScript, and React.
Honors & Awards
- Computing Research Association (CRA) Outstanding Undergraduate Researcher Award, Honorable Mention2026
- Excellence in Research Award, University of Maryland2026
- CS Departmental Honors, University of Maryland2023–2026
- President's Scholarship, University of Maryland2023–2026
- Dean's List, University of Maryland2023–2026