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Context-aware Decision-Making

Machine learning is already very powerful, but there are still so many tasks outside of its reach. How do we develop machine learning and optimization algorithms for difficult, large-scale tasks? This stream of work aims to push the boundaries on algorithm tractability while providing performance guarantees.

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Learning to cover: online learning and optimization with irreversible decisions

A. Jacquillat, M. L. Li

Major Revision at Management Science

2025

Interpretable Matrix Completion: A Discrete Optimization Approach

D. Bertsimas, M. L. Li

INFORMS Journal on Computing

2023

Cramming Contextual Bandits for On-policy Statistical Evaluation

Z. Jia, K. Imai, M. L. Li

Submitted to Management Science

2025

When Loss is More: Optimizing Prescription Alerts under Fatigue

M. L. Li, H. Piri

Submitted to Management Science

2025

🥇CBLS Best Paper Award

Scalable Holistic Linear Regression

D. Bertsimas, M. L. Li

Operations Research Letters

2018

Branch-and-Price for Prescriptive Contagion Analytics

A. Jacquillat, M. Rame, K. Wang, M. L. Li

Operations Research

2025

Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

D. Bertsimas, M. L. Li

Journal of Machine Learning Research

2020

Stochastic Cutting Planes for Data-Driven Optimization

D. Bertsimas, M. L. Li

INFORMS Journal on Computing

2021

Balancing optimality and diversity: Human-centered decision making through generative curation

M. L. Li, S. Zhu

Submitted to Manufacturing & Service Operations Management

2025

Slowly Varying Regression Under Sparsity

D. Bertsimas, O. S. Lami, V. Digalakis, M. L. Li

Operations Research

2025

©2025 by Michael Lingzhi Li

Contact

mili at hbs dot edu (Academic)

 

michaelliling2 at gmail dot com (Personal)​​

Technology & Operations Management, 
Harvard Business School

​

Morgan Hall, Soldiers Field
Boston, MA 02163

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