About Me

Hi! I’m a machine learning engineer/researcher focused on building ML systems for real-world decision-making problems in product and enterprise settings. I am currently open to new opportunities to deepen my experience in designing ML-driven decision systems across domains such as fintech, insurance, supply chain optimization, and consumer products, where the key challenge lies in problem formulation and end-to-end system design rather than model optimization alone.

Previously, I worked at UC Capital as a machine learning researcher/engineer, where I built production-grade ML systems in a trading environment. I was involved in the full system stack, including data pipelines, multi-threaded infrastructure, interprocess communication, and latency-sensitive deployment. The role also required close collaboration with traders in a fast-moving and ambiguous environment, translating domain heuristics into machine learning systems under real-world constraints.

Prior to joining the industry, I led research in reinforcement learning and deep learning at National Taiwan University, resulting in publications at ICML and NeurIPS. My work was supported by the NSTC international travel grant., which enabled me to present my research in Vienna and Vancouver. My research spans reinforcement learning, imitation learning, diffusion models, and decision-making systems.

Outside of work, I enjoy building LEGO sets, snowboarding in winter, and recently started learning tennis, still early but enjoying the process.