Michael Lingzhi Li
Assistant Professor of Business Administration
Assistant Professor of Business Administration
Michael Lingzhi Li is an Assistant Professor in the Technology and Operations Management unit at HBS. He teaches the first-year TOM course in the required curriculum.
Professor Li’s research focuses on the end-to-end development of decision algorithms based on machine learning, causal inference and operations research. He examines the implementation of such algorithms in hospitals, pharmaceutical companies, and public health organizations, and their potential to fundamentally transform healthcare operations. He is the recipient of awards including the 2022 INFORMS Edelman Finalist, the 2021 INFORMS Pierskalla Award, and the 2021 Innovative Applications in Analytics Award.
Professor Li holds a PhD in Operations Research and a Master’s in Business Analytics from MIT, along with a Bachelor of Arts from the University of Cambridge. In his free time, Professor Li enjoys hiking, swimming, and discovering new restaurants in Boston.
Michael Lingzhi Li is an Assistant Professor in the Technology and Operations Management unit at HBS. He teaches the first-year TOM course in the required curriculum.
Professor Li’s research focuses on the end-to-end development of decision algorithms based on machine learning, causal inference and operations research. He examines the implementation of such algorithms in hospitals, pharmaceutical companies, and public health organizations, and their potential to fundamentally transform healthcare operations. He is the recipient of awards including the 2022 INFORMS Edelman Finalist, the 2021 INFORMS Pierskalla Award, and the 2021 Innovative Applications in Analytics Award.
Professor Li holds a PhD in Operations Research and a Master’s in Business Analytics from MIT, along with a Bachelor of Arts from the University of Cambridge. In his free time, Professor Li enjoys hiking, swimming, and discovering new restaurants in Boston.
- Published Manuscripts
-
- Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201. View Details
- Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256. View Details
- Journal Articles
-
- Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024). View Details
- Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics (forthcoming). (Pre-published online July 8, 2024.) View Details
- Jacquillat, Alexandre, Michael Lingzhi Li, Martin Ramé, and Kai Wang. "Branch-and-Price for Prescriptive Contagion Analytics." Operations Research (forthcoming). (Pre-published online March 13, 2024.) View Details
- Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278. View Details
- Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023). View Details
- Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li, and et al. "Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States." e2113561119. Proceedings of the National Academy of Sciences 119, no. 15 (April 12, 2022). (See full author list here.) View Details
- Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965. View Details
- Bertsimas, Dimitris, and Michael Lingzhi Li. "Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion." Journal of Machine Learning Research 21, no. 1 (2020). View Details
- Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426. View Details
- Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208. View Details
- Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272. View Details
- Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200. View Details
- Working Papers
-
- Bertsimas, Dimitris, Michael Lingzhi Li, and Saksham Soni. "THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions." Working Paper, April 2022. View Details
- Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023. View Details
- Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Working Paper, March 2022. View Details
- Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022. View Details
- Cases and Teaching Materials
-
- Huckman, Robert S., Michael Lingzhi Li, and Camille Gregory. "Hospital for Special Surgery: Returning to a New Normal? (B)." Harvard Business School Supplement 624-093, July 2024. View Details
- Huckman, Robert S., Michael Lingzhi Li, and Camille Gregory. "Hospital for Special Surgery: Returning to a New Normal? (A)." Harvard Business School Case 624-092, June 2024. (Revised August 2024.) View Details
- Additional Information
- Areas of Interest
-
- analytics
- machine learning
- operations management
- statistics
- health care quality
- supply chain management
- health care
- insurance industry
Additional TopicsIndustries