Michael Lingzhi Li
Researcher & Entrepreneur
Hey, you have landed on the personal website of Michael Lingzhi Li, an academic researcher at the intersection of optimization, machine learning, and causal inference, and working on utilizing machine learning to create demonstratable and robust social impact.
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Note 1: I am actively looking for PhDs / research assistants to work with me across optimization, machine learning, and causal inference! Please contact me if you are interested.
Personal Overview
Currently, I am serving as an Assistant Professor at the Technology and Operations Management Unit in Harvard Business School and the Chief Data Scientist at Waffle Labs.
I graduated from University of Cambridge with a bachelor's degree in mathematics with First Class Honors, and hold a PhD in Operations Research from Massachusetts Institute of Technology, advised by Prof. Dimitris Bertsimas. Growing up, I lived in Canada, China, United States and United Kingdom, each for multiple years.
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To know more about me, please visit the Who am I page. If you are interested in my research, follow the Publications page to see my latest work. The Entrepreneurship page records my most recent attempts to help change the world. I also do have a scarcely updated Blog.
Publications
Statistical Inference for HTE Discovered by Generic ML in Randomized Experiments (with Imai, K.) Accepted at Journal of Business & Economics Statistics.
Branch-and Price for Prescriptive Contagion Analytics (with Jacquillat, A., Rame, M., Wang, K.) Accepted at Operations Research (2024).
Slowly Varying Regression Under Sparsity (with Bertsimas, D., Digalakis, V., Lami, O. S.) Accepted at Operations Research (2024).
Forecasting COVID-19 and Analyzing the Effect of Government Interventions (with Bouardi, H. T., Lami, O. S., Trikalinos, T. A., Trichakis, N. K., & Bertsimas, D.) Operations Research (2022).
Experimental Evaluation of Individualized Treatment Rules (with Imai, K.). Journal of the American Statistical Association (2021): 1-41.
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion (with Bertsimas, D.). Journal of Machine Learning Research, 21(231), 1-43 (2020).