Researching the next interface between perception, action, and reasoning.
I am an undergraduate at Peking University, majoring in Intelligent Science and Technology. My work spans embodied intelligence, VLA efficiency, large model reasoning, and long-context systems. I am currently a research intern in Prof. Guozhang Chen's lab, where I work on efficient VLA pipelines and related multimodal systems.
About
What I work on
I am interested in building intelligent systems that can perceive rich environments, reason over long horizons, and make grounded decisions. My recent projects focus on agentic e-commerce assistants, efficient VLA action generation, multimodal dataset construction, and inference-time reasoning behavior in large models.
Education
Academic background
Undergraduate in Intelligence Science · Aug 2023 to Present
Research Experience
What I am building now
Joint University-Enterprise Innovation Workstation Project · May 2025 to May 2026
Building an end-to-end LLM agent post-training pipeline with SFT and RL, long-term memory, benchmark expansion, and reward-stabilized data construction.
Peking University · Feb 2025 to Present
Worked on spike camera data collection for SpikeStereoNet and now focus on efficient VLA systems with conditional SSM-based action denoising and distillation.
TarraVerse Group · Dec 2025 to Feb 2026
Contributed to terrain-centric multimodal data construction for TerraVerse, including collection, cleaning, annotation, and scalable quality filtering.
Selected Publication
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
A representative research output exploring brain-inspired visual computation and stereo depth estimation from spike streams. Publication metadata can be expanded further as soon as venue, year, and paper links are finalized.
Direction 01
Embodied Intelligence
Designing systems that connect multimodal perception, action generation, and decision-making under real-world constraints.
Direction 02
Cognitive Architectures
Exploring memory, tool use, and structured control mechanisms that improve reliability and long-horizon capability in LLM systems.
Direction 03
Interpretability
Studying model reasoning traces, efficiency tradeoffs, and mechanism-level understanding for safer and more controllable AI systems.