Ryan Cory-Wright

Assistant Professor

Imperial College Business School
Imperial-X

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 focuses on optimization, machine learning, statistics, and their application in business analytics. I am interested in broadening the scope of optimization to encompass problems that frequently arise in practice. For instance, in my PhD thesis, we proposed a generalization of integer optimization that tackles rank constraints, which arise in product recommendation applications. I am also interested in leveraging optimization to facilitate a rapid and economically viable transition to a low-carbon economy. For instance, we recently collaborated with OCP, a fertilizer manufacturer, to develop a framework that guides a two billion USD investment in solar panels and batteries.

I have received an IBM Herman Goldstine Postdoctoral Fellowship (2022-23), the Nicholson Prize (2020), the INFORMS Data Mining Society Student Paper Award (2021), the INFORMS Computing Society Student Paper Award (2019), and the INFORMS Health Applications Society Pierskalla Award (2020). I have also been a finalist in the 2023 M&SOM practice-based research competition.

Before joining ICBS, I spent a year as a postdoctoral fellow at IBM Research (Cambridge, MA). I received my PhD in Operations Research from MIT in 2022, advised by Dimitris Bertsimas. Before joining MIT, I received a BE (1st class Hons) in Engineering Science from the University of Auckland.

News

April 2024: It was great to co-organize the 2024 London Operations Research Day (LORD 2024). Check out a live thread of the conference on Twitter.

March 2024: Organizing session FriB8 at IOS 2024. I will be talking about our paper AI Hilbert and Andrés Gómez will be talking about our paper A New Approach to Cross-Validation for Sparse Regression.

March 2024: Our research on decarbonization is featured by Imperial Business News.

January 2024: Delighted to be selected as a 2024 INFORMS Journal on Computing Meritorious Reviewer.

January 2024: Excited to be teaching classes on Decision Making Under Uncertainty (PhD), Optimization and Decision Models (Online MSc Business Analytics), and Data Structures and Algorithms (UG Economics, Finance, and Data Science). The PhD class is new—check out the syllabus.

November 2023: Our paper on Decarbonizing OCP has been accepted for publication at M&SOM as part of the 2023 M&SOM practice-based research competition.

November 2023: Excited to be giving talks on our paper Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions at the Toronto Rotman Young Scholar's Seminar Series (virtual) and the Imperial College London Control and Optimization Seminar Series (in-person, slides).

November 2023: Overhauled our paper Sparse PCA With Multiple Components, including adding a new section and open-sourcing the code. Please do check out the new version!

October 2023: Our paper Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach has been accepted for publication at The Journal of Machine Learning Research.

October 2023: I started co-advising Lingjun Meng (joint with Wolfram Wiesemann) as a doctoral student. Welcome Lingjun!

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 as an affiliated faculty member.

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

June 2023: Our paper on Decarbonizing OCP has been selected as a finalist in the 2023 M&SOM practice-based research competition, and as an honorable mention in the 2023 MIT Operations Research Center Student Paper 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. Check out the five-page summary .

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.

Working Papers

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 Slides Code

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

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

1. 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

Journal Papers

11. Decarbonizing OCP
Manufacturing & Service Operations Management, 2023, with Dimitris Bertsimas and Vassilis Digalakis Jr.
Finalist, 2023 Manufacturing & Service Operations Management Practice-Based Research Competition
Honorable Mention, 2023 MIT ORC Student Paper Competition (Digalakis)
Preprint DOI Slides OCP's Green Investment Program Imperial Business Article

10. Sparse Plus Low-Rank Matrix Decomposition: A Discrete Optimization Approach
Journal of Machine Learning Research, 24(267):1-51, 2023, with Dimitris Bertsimas and Nicholas Johnson
First place, 2021 INFORMS Data Mining Student Paper Award
Preprint DOI Code

9. A New Perspective on Low-Rank Optimization
Mathematical Programming, 202:47-92, 2023, with Dimitris Bertsimas and Jean Pauphilet
Preprint DOI Slides 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
MIT Libraries Five-page Summary

Teaching

Teaching at Imperial

BUSI79046 Python and Machine Learning (MSc in AI and Entrepreneurship, Fall 2024)
Designed a new class which introduces machine learning in Python

BUSI40008 Data Structures and Algorithms (UG Economics, Finance, and Data Science, Spring 2024)
Designed a new class, partly based on a pre-existing MSc class, which introduces computational problem-solving through the lens of algorithms and data structures in Python

BUSI70447 Decision Making Under Uncertainty (PhD, Spring 2024)
Designed a new class introducing techniques for decision-making under uncertainty widely used in operations research. Includes stochastic optimization, robust optimization, and dynamic programming

BUSI70218 Optimisation and Decision Models (Online MSc Business Analytics, Spring 2024)
Class which introduces students to theory and applications of linear, discrete, and nonlinear optimization

Teaching at MIT

Head TA, 15.095 Machine Learning Under a Modern Optimization Lens (MBAn/PhD, Fall 2019, 2021)
Head TA, 15.071 The Analytics Edge (MBA, Fall 2020)
TA, 15.093 Optimization Methods (MSc/MBAn/PhD, Fall 2018)
Participant, Kaufman Teaching Certificate Program (Fall 2021)
Session Instructor, 15.S60 Computing for Optimization and Statistics (MSc/PhD, Jan 2019, 2020)