Cambridge, Massachusetts, United States
Hi, I'm currently a MS Biostatistics student at Harvard and I studied applied math and computer science in undergrad at USC. With a keen interest in statistics, I hope to understand the mechanisms of estimation and inference and develop new algorithms that help people better understand inherent structures in data and extract interpretable insights. My research interest spans causal inference, semi-parametric inference, and high-dimensional statistics. I'm also excited to learn about modern deep learning techniques and how it can transform data-driven tasks in biomedical domain. I have interned in a hedge fund company in Beijing, China in 2023.
• Causal inference with network interference: Random graph asymptotic analysis + semi-parametric outcome model (submitted to ICML) • Generative AI model for aligning EHR and genomics data • Proximal causal inference for identifying ATE with unmeasured confounding - for time series/panel data with applications in environmental health
• Developed factors using daily data of main futures contracts and backtested them for 12 years • Led the research of optimizer for intra-day stock trading project and reviewed papers on online portfolio selection • Refined Python programs to evaluate CTA funds and researched the companies that sell superior funds
• Worked with Prof. Jacob Bien on JSM conference scheduler, a recommender system for attendees • Worked with Prof. Matteo Sesia on variable selection using knockoff filters on real-world data with both continuous and categorical variables • Joined Cognitive Learning for Vision and Robotics (CLVR) Lab from May 2021 to January 2022; Learned deep reinforcement learning from CS 285, participated in weekly reading group, and reimplemented a paper on autoencoder for RL representation learning