About Me

Welcome to my academic page! I am an undergraduate student currently pursuing a B.S.E in Computer Science at the University of Michigan, Ann Arbor, and a B.Eng. in Mechanical Engineering at Shanghai Jiao Tong University.

My primary research interest is in Web Agents. My goal is to develop highly capable and intelligent systems that can reliably perceive, reason, and act in complex web environments. I am particularly interested in evaluating, benchmarking, and developing robust MLLM-based agentic systems.

Research Experience

• WebEvolve: Robustness Benchmarking for Web Agents (Jan 2026 – Present)
  Advisor: Prof. Honglak Lee (University of Michigan)
  Developed WebEvolve, a version-aware benchmark framework that evaluates MLLM-based web agent robustness under natural website version shift. Designed a knowledge graph based representation pipeline and built a cross-version alignment mapping capability breakdowns. Implemented an end-to-end task maintenance pipeline mapping out tasks for reuse, refresh, or regeneration.

• Efficient Inference for Embodied Foundation Models (Nov 2025 – Present)
  Advisor: Dr. Jiachen Liu (University of Michigan)
  Profiled Video Action Models (VAM) for robotic policy, implementing cross-attention KV caching and token compression to accelerate inference in dynamic environments. Explored quantization and speculative decoding on VLA models to optimize real-time decision-making.

• Gravitational Effect on Swarming Behavior of Microorganisms (Sep 2024 – Aug 2025)
  Advisor: Prof. Zijie Qu (Shanghai Jiao Tong University)
  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.

Selected Projects

• Probabilistic Motion Planning for Redundant Robots (Nov 2025 – Dec 2025)
  EECS 465 (Intro to Algorithmic Robotics) course project
  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.

Technical Skills

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