CV

Education

• B.S.E. in Computer Science
  • University of Michigan, Ann Arbor
  • Aug 2025 - Present
  • GPA: 3.88/4.00
  • Selected Coursework: Machine Learning, Operating Systems, GPU Parallel Programming, Database Management Systems.
• B.Eng. in Mechanical Engineering
  • Shanghai Jiao Tong University
  • Aug 2023 - Present

Research Interests

AI Agents, with a focus on Web Agents and their memory.

Selected Research Projects

• Agent Memory Decay Under Controlled Web Drift (Jan 2026 – Present) [Progress Report]
  • Advisor: Prof. Honglak Lee (Ann Arbor, MI)
  • Investigating the robustness of test-time agent memory systems under controlled website GUI evolution.
  • Designed a reproducible evaluation testbed injecting 6 distinct drift variants (surface, structural, content, runtime, access, functional) into open-source web applications.
  • Extracted and implemented varying abstraction levels of agent memory—from raw trajectories (ExpRAG) and semantic insights (ExpeL) to procedural workflows (AWM).
  • Evaluated memory decay by measuring Experience Transfer Gap (ETG) and Decay Rate (EDR) to systematically demonstrate the vulnerability of fixed test-time experiences against DOM structure and UI visual changes.
  • Pioneering the systematic study of environmental coupling in agent memory, providing empirical guidelines for self-evolving integration.
• Efficient Inference for Embodied Foundation Models (Nov 2025 – Present)
  • Advisor: Dr. Jiachen Liu (Ann Arbor, MI)
  • Profiled Video Action Models (VAM) for robotic policy, implementing cross-attention KV caching and token compression to accelerate inference in dynamic environments.
  • Explored quantization (dynamic precision) and speculative decoding on VLA models, optimizing the computational backbone required for real-time agentic decision-making.
• Gravitational Effect on Swarming Behavior of Microorganisms (Sep 2024 - Aug 2025)
  • Advisor: Prof. Zijie Qu (Shanghai, China)
  • Trained a U-Net model for colony boundary segmentation; diagnosed systematic failure modes, then adopted a SAM-based pipeline that achieved reliable automated detection, replacing manual annotation.
• Probabilistic Motion Planning for Redundant Robots (Nov 2025 – Dec 2025)
  • EECS 465 (Intro to Algorithmic Robotics) course project (Ann Arbor, MI)
  • Evaluated RRT-Connect and PRM on a 7-DOF Franka Panda; optimized sampling strategies in high-dimensional state spaces, achieving a 75% reduction in planning latency.

Skills

• Frameworks: PyTorch, Hugging Face, CUDA, Docker, Linux
• Languages: Python, C/C++, Java, SQL, LaTeX, Typst