Michael Lingzhi Li

About Michael Lingzhi Li

Michael Lingzhi Li, With an exceptional h-index of 14 and a recent h-index of 14 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of Integer Optimization, Causal Inference, Precision Medicine, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

Slowly varying regression under sparsity

Branch-and-Price for Prescriptive Contagion Analytics

The Cram Method for Efficient Simultaneous Learning and Evaluation

Benchmarking Large Language Models on CMExam-A Comprehensive Chinese Medical Exam Dataset

High-performing Multi-task Model of Urinary Tract Dilation (UTD) Classification for Neonatal Ultrasound Reports Through Natural Language Processing

Holistic deep learning

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Michael Lingzhi Li Information

University

Position

___

Citations(all)

1156

Citations(since 2020)

1155

Cited By

97

hIndex(all)

14

hIndex(since 2020)

14

i10Index(all)

17

i10Index(since 2020)

17

Email

University Profile Page

Google Scholar

Michael Lingzhi Li Skills & Research Interests

Integer Optimization

Causal Inference

Precision Medicine

Machine Learning

Top articles of Michael Lingzhi Li

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

arXiv preprint arXiv:2404.17019

2024/4/25

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

Kosuke Imai
Kosuke Imai

H-Index: 48

Slowly varying regression under sparsity

Operations Research

2024/3/27

Branch-and-Price for Prescriptive Contagion Analytics

Operations Research

2024/3/13

The Cram Method for Efficient Simultaneous Learning and Evaluation

arXiv preprint arXiv:2403.07031

2024/3/11

Kosuke Imai
Kosuke Imai

H-Index: 48

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

Benchmarking Large Language Models on CMExam-A Comprehensive Chinese Medical Exam Dataset

Advances in Neural Information Processing Systems

2024/2/13

High-performing Multi-task Model of Urinary Tract Dilation (UTD) Classification for Neonatal Ultrasound Reports Through Natural Language Processing

medRxiv

2024

Holistic deep learning

Machine Learning

2023/12/7

Pricing for heterogeneous products: Analytics for ticket reselling

Manufacturing & Service Operations Management

2023/3

Experimental evaluation of individualized treatment rules

Journal of the American Statistical Association

2023/1/2

Kosuke Imai
Kosuke Imai

H-Index: 48

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

Accelerating vaccine innovation for emerging infectious diseases via parallel discovery

Entrepreneurship and Innovation Policy and the Economy

2023/1/1

Data-Driven COVID-19 Vaccine Development for Janssen

INFORMS Journal on Applied Analytics

2023/1

Dimitris Bertsimas
Dimitris Bertsimas

H-Index: 63

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

A machine learning algorithm predicting risk of dilating VUR among infants with hydronephrosis using UTD classification

Journal of Pediatric Urology

2023/11/9

Optimizing pallet location in a warehouse

2023/10/19

Statistical Performance Guarantee for Selecting Those Predicted to Benefit Most from Treatment

2023/10/12

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

Kosuke Imai
Kosuke Imai

H-Index: 48

Interpretable matrix completion: A discrete optimization approach

INFORMS Journal on Computing

2023/9

Dimitris Bertsimas
Dimitris Bertsimas

H-Index: 63

Michael Lingzhi Li
Michael Lingzhi Li

H-Index: 5

High-performance pediatric surgical risk calculator: a novel algorithm based on machine learning and pediatric NSQIP data

The American Journal of Surgery

2023/7/1

Automated pallet profiling

2022/10/20

Distributionally robust causal inference with observational data

arXiv preprint arXiv:2210.08326

2022/10/15

See List of Professors in Michael Lingzhi Li University(Massachusetts Institute of Technology)

Co-Authors

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