This is a fundamental track project for COMPSCI194-196 LLM Agents and LLM Agents hackathon. We focused on exploring robust and generalizable internal representations of lightweight LLMs and investigating the progression of learned features with linear probes and sparse autoencoders in OthelloGPT. Our experiments reveal that SAEs provide a more robust and disentangled decoding of the features the model is learning, particularly for compositional attributes.

This is a Meta Quest track project for the Stanford XR Hackathon. We focused on recovering human psychological dysfunctions, aiming to provide a comprehensive treatment protocol by designing multiple simple interactive meditation games utilizing the power of Meta Quest3. We build interactive environments from scratch in Unity; users can choose different environments, background music, and meditation guidance in Zen.

My small project that took part in Tencent Rhinobird High School Scientific Training Program in high school, uses light-weight neural network backbone, MobileNetV3 to detect fire, targeting for applications in portable devices and edge computing.