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05/25/2023
Patient Outcome Predictions Improve Operations at a Large Hospital Network
Problem definition: Access to accurate predictions of patients' outcomes...
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05/20/2023
Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Low-rank matrix completion consists of computing a matrix of minimal com...
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09/29/2022
Sparse PCA With Multiple Components
Sparse Principal Component Analysis is a cardinal technique for obtainin...
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09/05/2022
The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations
Many high-stake decisions follow an expert-in-loop structure in that a h...
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06/21/2021
Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
Problem definition: Mining for heterogeneous responses to an interventio...
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05/12/2021
A new perspective on low-rank optimization
A key question in many low-rank problems throughout optimization, machin...
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04/07/2021
Prediction with Missing Data
Missing information is inevitable in real-world data sets. While imputat...
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09/22/2020
Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints
We propose a framework for modeling and solving low-rank optimization pr...
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06/30/2020
From predictions to prescriptions: A data-driven response to COVID-19
The COVID-19 pandemic has created unprecedented challenges worldwide. St...
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05/11/2020
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Sparse principal component analysis (PCA) is a popular dimensionality re...
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07/03/2019
A unified approach to mixed-integer optimization: Nonlinear formulations and scalable algorithms
We propose a unified framework to address a family of classical mixed-in...
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06/25/2019
Certifiably Optimal Sparse Inverse Covariance Estimation
We consider the maximum likelihood estimation of sparse inverse covarian...
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02/18/2019