I am an second-year Ph.D. student at UChicago Data Science Institute (DSI). At DSI, I have the pleasure of working with Prof. Nikolaos Ignatiadis and Prof. Haifeng Xu. Previously, I finished my M.S. in Statistics under the supervision of Prof. Daniel Sanz-Alonso. I am also a part of the AER Lab under the mentorship of Prof. Sebastian Musslick. Here is my Google Scholar.
My research focuses on developing and validating principled frameworks that leverage abundant, noisy signals from large models to improve the statistical efficiency, interpretability, and real-world performance of decision-making systems. I work on problems that can be viewed from dual perspectives:
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From a theorist perspective, I design systems that retain statistical rigor and formal guarantees. This involves adapting principles from Empirical Bayes and high-dimensional statistics to create methods that are provably sound.
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From a practitioner perspective, I build systems that provide actionable insights and lower noise-levels. My work aims to move beyond abstract metrics to create methods that integrate with black-box models, identify reliable signals from noise, and enable real-world impact.
More about me: even earlier, I graduated from UC Berkeley EECS & Statistics. I enjoyed working as a software engineer in an “earlier life”.