Current Environment:

Research Overview

  Dr. Li's research is focused on using data-driven analytics and optimization to create real-world impact in healthcare, public health and beyond. To achieve this goal, he works on both new optimization algorithms and evaluation frameworks as well as important collaborations with corporations, hospitals, and governments worldwide. Specifically, at Boston Children's Hospital, he is focused on developing novel algorithms and robust evaluation metrics to ensure that machine learning can be safely and effectively deployed in hospitals. He is the recipient of awards including the 2021-2022 INFORMS Edelman Finalist, 2021 INFORMS Doing Good with Good OR Finalist, the 2021 Innovative Applications in Analytics Award, the 2020 INFORMS Pierskalla Award and the 2019 MSOM Best Student Paper Finalist Award.
 

Research Background

Michael Lingzhi Li completed his BA (Hons.) degree in Mathematics from University of Cambridge. He earned a Masters of Business Analytics from the Massachusetts Institute of Technology (MIT) and went on to complete a PhD at the MIT Operations Research Center.

Selected Publications:

  1.  Experimental Evaluation of Individualized Treatment Rules (with Imai, K.). Journal of the American Statistical Association (2021): 1-41.
  2. Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion (with Bertsimas, D.). Journal of Machine Learning Research, 21(231), 1-43 (2020).
  3. Scalable Holistic Linear Regression (with Bertsimas, D.). Operations Research Letters (2020).
  4. Selecting Children with VUR Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR (with Wang, H. H. S., Li, M., Bertsimas, D., Estrada, C., & Caleb, N.). The Journal of Urology, 10-1097 (2020).
  5. Targeted Workup after Initial Febrile Urinary Tract Infection: Using a Novel Machine Learning Model to Identify Children Most Likely to Benefit from Voiding Cystourethrogram (part of Advanced Analytics Group of Pediatric Urology and ORC Personalized Medicine Group). The Journal of Urology, 202(1), 144-152 (2019).
  6. Prescriptive Analytics for Reducing 30-day Hospital Readmissions after General Surgery (with Bertsimas, D., Paschalidis, I. C., & Wang, T.). PloS one, 15(9), e0238118 (2020).
  7. 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.) medRxiv (2020). Minor Revision at Operations Research.
  8. From predictions to prescriptions: A data-driven response to COVID-19 (with Bertsimas, D., Boussioux, L., Wright, R. C., Delarue, A., Digalakis Jr, V., Jacquillat, A., ... & Nohadani, O.). arXiv preprint arXiv:2006.16509. Accepted at Health Care Management Science.
  9. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the US (with Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., ... & COVID-19 Forecast Hub Consortium.). PNAS (2021).