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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

Slowly Varying Regression Under Sparsity

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

Operations Research

2025

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

M. L. Li, S. Zhu

Submitted to Manufacturing & Service Operations Management

2025

Stochastic Cutting Planes for Data-Driven Optimization

D. Bertsimas, M. L. Li

INFORMS Journal on Computing

2021

Scalable Holistic Linear Regression

D. Bertsimas, M. L. Li

Operations Research Letters

2018

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

D. Bertsimas, M. L. Li

Journal of Machine Learning Research

2020

Branch-and-Price for Prescriptive Contagion Analytics

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

Operations Research

2025

When Loss is More: Optimizing Prescription Alerts under Fatigue

M. L. Li, H. Piri

Submitted to Management Science

2025

🥇CBLS Best Paper Award

Cramming Contextual Bandits for On-policy Statistical Evaluation

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

Submitted to Management Science

2025

Interpretable Matrix Completion: A Discrete Optimization Approach

D. Bertsimas, M. L. Li

INFORMS Journal on Computing

2023

©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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