News

  • January 2024: Excited to be teaching classes on Decision Making Under Uncertainty (PhD), Optimization and Decision Models (MSc), and Data Structures and Algorithms (undergraduate). The PhD class is new—check out the syllabus

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

  • 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

  • 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

  • 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

Teaching

Teaching at Imperial:
  • Data Structures and Algorithms (Undergraduate 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.

  • 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.

  • Decision Making Under Uncertainty (Online MSc Business Analytics, Spring 2024)

    Class which introduces students to theory and applications of linear, discrete, and nonlinear optimization.

Teaching at MIT: