Ryan Cory-Wright

Assistant Professor

Imperial College Business School

About me

I am an Assistant Professor in the Analytics and Operations Group at Imperial College Business School (ICBS), affiliated with the Imperial-X initiative on interdisciplinary AI/ML.

My research interests lie at the intersection of optimization, machine learning, and statistics, and their applications in business analytics and renewable energy. I am particularly interested in building bridges between different theories of optimization, and using these bridges to facilitate solving previously intractable problems. For instance, in my PhD thesis, we proposed a generalization of integer optimization that tackles rank constraints. I am also interested in using optimization to facilitate a rapid and economically viable transition to a low-carbon economy.

I have been fortunate enough to receive a number of recognitions for my work, including an IBM Herman Goldstine Postdoctoral Fellowship (2022-23), the Nicholson Prize (2020), the INFORMS Computing Society Student Paper Award (2019), and the Pierskalla Award (2020).

Prior to joining ICBS, I spent a year as a postdoctoral fellow at IBM Research (Cambridge, MA). I received my PhD in Operations Research from MIT, where I was advised by Dimitris Bertsimas. Before coming to MIT, I received a BE in Engineering Science from the University of Auckland.

News

September 2023: Excited to be co-organizing the 2024 London Operations Day (LORD) workshop.

August 2023: New preprint out on AI Hilbert: A New Paradigm for Scientific Discovery by Unifying Data and Background Knowledge.

July 2023: Officially joined the Analytics and Operations group at Imperial College Business School and Imperial-X.

June 2023: New preprint out on Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression.

June 2023: Our paper on Decarbonizing OCP has been selected as a finalist in the 2023 MSOM practice-based research competition. I will be presenting this at MSOM 2023.

May 2023: New preprint out on Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions. I will be presenting this at the MIP Workshop.

March 2023: New preprint out on A Stochastic Benders Decomposition Scheme for Large-Scale Data-Driven Network Design.

December 2022: Our paper A New Perspective on Low-Rank Optimization has been accepted for publication at Mathematical Programming.

September 2022: New preprint out on Sparse PCA With Multiple Components.

September 2022: New preprint out on Decarbonizing OCP.

March 2022: Defended my thesis Integer and Matrix Optimization: A Nonlinear Approach.

March 2022: Honored to be selected as a Herman Goldstine Postdoctoral Fellow!

February 2022: I am joining Imperial College Business School (and Imperial-X) starting in July 2023! Very much looking forward to new collaborations and projects at Imperial.

January 2022: Our paper Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality has been accepted for publication at the Journal of Machine Learning Research.

December 2021: Completed the MIT Kaufman Teaching Certificate Program.

October 2021: Excited to receive the 2021 INFORMS Data Mining Student Paper Award with Nicholas Johnson.

September 2021: New preprint out on Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach.

August 2021: Our paper A Scalable Algorithm For Sparse Portfolio Selection has been accepted for publication at INFORMS Journal on Computing.

July 2021: Our paper Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints has been accepted for publication at Operations Research.

November 2020: Very excited to receive the 2020 INFORMS George Nicholson Prize with Jean Pauphilet.

Preprints

6. AI Hilbert: A New Paradigm for Scientific Discovery by Unifying Data and Background Knowledge
Submitted, with Bachir El Khadir, Cristina Cornelio, Sanjeeb Dash, and Lior Horesh
Preprint

5. Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression
Submitted, with Andrés Gómez
Preprint

4. Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Submitted, with Dimitris Bertsimas, Sean Lo, and Jean Pauphilet
Preprint Code

3. Sparse PCA With Multiple Components
Submitted, with Jean Pauphilet
Preprint Code

2. A Stochastic Benders Decomposition Scheme for Large-Scale Data-Driven Network Design
Major Revision, INFORMS Journal on Computing , with Dimitris Bertsimas, Jean Pauphilet, and Periklis Petridis
Preprint

1. Decarbonizing OCP
Minor Revision, M&SOM, with Dimitris Bertsimas and Vassilis Digalakis Jr.
Finalist, 2023 M&SOM Practice-Based Research Competition
Honorable Mention, 2023 MIT ORC Student Paper Competition (Digalakis)
Preprint

Publications

10. Sparse Plus Low-Rank Matrix Decomposition: A Discrete Optimization Approach
Conditionally accepted, Journal of Machine Learning Research, with Dimitris Bertsimas and Nicholas Johnson
First place, 2021 INFORMS Data Mining Student Paper Award
Preprint Code

9. A New Perspective on Low-Rank Optimization
Mathematical Programming, 1-46, 2023, with Dimitris Bertsimas and Jean Pauphilet
Preprint DOI Code Video

8. Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints
Operations Research, 70(6):3321-3344, 2022, with Dimitris Bertsimas and Jean Pauphilet
First place, 2020 INFORMS George Nicholson Paper Award
Preprint DOI Code Video Poster

7. Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Journal of Machine Learning Research, 23(13):1-35, 2022, with Dimitris Bertsimas and Jean Pauphilet
Preprint Url Code Slides

6. A Scalable Algorithm for Sparse Portfolio Selection
INFORMS Journal on Computing, 34(3): 1489-1511, 2022, with Dimitris Bertsimas
Preprint DOI Code Poster

5. A Unified Approach to Mixed-Integer Optimization Problems With Logical Constraints
SIAM Journal on Optimization, 31(3):2340-2367, 2021, with Dimitris Bertsimas and Jean Pauphilet
First place, 2019 INFORMS Computing Society Student Paper Award
Preprint DOI ICS Newsletter Video

4. From Predictions to Prescriptions: A Data-Driven Response to COVID-19
Health Care Management Science, 24:253-272, 2021, with Dimitris Bertsimas et al.
First place, 2020 INFORMS Health Applications Society Pierskalla Paper Award
Preprint DOI Project Code NYT

3. On Stochastic Auctions in Risk-Averse Electricity Markets With Uncertain Supply
Operations Research Letters, 48(3):376-384, 2020, with Golbon Zakeri
Preprint DOI

2. On Polyhedral and Second-Order Cone Decompositions of Semidefinite Optimization Problems
Operations Research Letters, 48(1):78-85, 2020, with Dimitris Bertsimas
Preprint DOI

1. Payment Mechanisms For Electricity Markets With Uncertain Supply
Operations Research Letters, 46(1):116-121, 2018, with Andy Philpott and Golbon Zakeri
First place, 2016 ORSNZ Young Practitioner’s Prize
Preprint DOI

Integer and Matrix Optimization: A Nonlinear Approach
Ph.D. Thesis, Massachusetts Institute of Technology, May 2022
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