My research philosophy centers on addressing problems that not only push the boundaries of fundamental understanding in optimization and machine learning, but also generate a tangible, positive impact on society. I focus on deep theoretical questions—many of which are grounded in real-world challenges—striving to bridge the gap between rigorous theory and meaningful practice. Broadly, my research focuses on the following key challenges pertaining to (Q1) incomplete and erroneous data, (Q2) multi-criteria decisions, and (Q3) bridging discrete, continuous, and quantum optimization.

Q1. Algorithms for Incomplete and Noisy Data: How can we make decisions with incomplete and noisy data? How do errors in data impact decisions, and when is obtaining more information the most useful?
For more details, check out this page. Here are some selected publications in this thread:

  • Improving Clinical Decision Support through Interpretable Machine Learning and Error Correction in Electronic Health Records – with M. Arora, H. Mortagy, N. Dwarshius, J. Wang, P. Yang, A. Holder, R. Kamaleswaran, Journal of the American Medical Informatics Association (JAMIA) 2025.
  • Secretary Problems with Biased Evaluations using Partial Ordinal Information – with J. Salem. Management Science 2023.
  • Discovering Opportunities in New York City’s Discovery Program: an Analysis of Affirmative Action Mechanisms – with Y. Faenza, X. Zhang, EC 2023 (journal version under submission). arxiv
  • Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions – with Y. Faenza, A. Vuorinen, X. Zhang, ACDA 2023 (journal version under revision at M&SOM) arxiv
  • Using Algorithms to Tame Discrimination – with D. Desai, J. Salem, UC Davis Law Review, 2023
  • Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox – with J. Salem, D. Desai, ACM FAccT 2022
  • Closing the GAP: Mitigating Bias in Online Resume-Filtering, with J. Salem. WINE 2020. link

Q2. Multi-Criteria Decision-Making: Can we find algorithms that provide simultaneous guarantees on a set of objectives? Can we find a portfolio of a small set of solutions or algorithms that “cover” a given set of objectives, so that any objective has a “good enough” solution in the portfolio?
For more details, check out this page. Here are some selected publications in this thread:

Portfolio of Small Set of Solutions:

  • Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning, with C. Kim, J. Moondra, S. Verma, M. Pollack, L. Kong, M. Tambe, ICML 2025.
  • Approximation Algorithms for Fair Portfolio of Solutions in Combinatorial Optimization – with J. Moondra, M. Singh, SODA 2025.
  • Too many fairness metrics: Is there a solution? – with A. Jalan, G. Ranade, H. Yang, S. Zhuang, Fields Institute Communication Series 2025.
  • Which Lp norm is the fairest? Approximations for fair facility location across all “p” – with J. Moondra, M. Singh. EC 2023.

Multi-Criteria Guarantees with a Single Solution or Algorithm:

  • Algorithmic Challenges in Ensuring Fairness at the Time of Decision – with J. Salem, V. Kamble, WINE 2022, and Operations Research 2025.
  • Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks – with C. Hettle and D. Molzahn, INFORMS Journal on Computing 2025
  • Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices – with M. Pollack, R. Piansky, D. Molzahn, Applied Energy, 2025
  • Temporal Fairness in Online Decision-Making, Swati Gupta, Jad Salem, Vijay Kamble, Book Chapter in Ethics in Artificial Intelligence: Bias, Fairness and Beyond, 2023.
  • Individual Fairness in Hindsight – with V. Kamble, Journal of Machine Learning Research 2021.
  • Balanced Districting on Grid Graphs with Provable Compactness and Contiguity – with
    C. Hettle, S. Zhu, Y. Xie, FORC 2021
  • Group-Fair Online Allocation in Continuous Time – with S. Cayci, A. Eryilmaz, NeurIPS 2020.

Q3. Bridging Discrete, Continuous, and Quantum Optimization: Can we exploit the structure of combinatorial polytopes to speed up convex optimization problems? Can we exploit convex relaxations of discrete problems to find good solutions? With the growing computational demands of our era, can new quantum primitives help scale optimization further, and can techniques from classical methods help scale quantum algorithms?
For more details, check out this page. Here are some selected publications in this thread:

Discrete and Continuous Optimization:

  • Approximation Algorithms for Fair Portfolio of Solutions in Combinatorial Optimization – with J. Moondra, M. Singh, SODA 2025. (Invited to Special Issue in Transactions in Algorithms)
  • Hardness and Approximation of Submodular Minimum Linear Ordering Problems, with M. Farhadi, S. Sun, P. Tetali, M. Wigal Mathematical Programming 2023
  • Which Lp norm is the fairest? Approximations for fair facility location across all “p” – with J. Moondra, M. Singh. EC 2023.
  • Electrical Flows over Spanning Trees with A. Khodabhaksh, H. Mortagy, E. Nikolova Mathematical Programming B, 2022
  • Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization with H. Mortagy, S. Pokutta, NeurIPS 2020, (major rev) Mathematics of Operations Research.
  • Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes with J. Moondra, H. Mortagy NeurIPS 2021
  • Balanced Districting on Grid Graphs with Provable Compactness and Contiguity – with
    C. Hettle, S. Zhu, Y. Xie, FORC 2021
  • Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives with M. Udell, S. Zhou NeurIPS 2018 (spotlight)
  • What works best when? A Framework for Systematic Heuristic Evaluation with J. Silberholz, I. Dunning, INFORMS Journal on Computing, 2018
  • A 4/3 approximation for TSP on cubic 3-edge-connected graph with N. Agarwal, N. Garg, Operations Research Letters, 2018
  • Newton’s Method for Parametric Submodular Function Minimization with M. Goemans, P. Jaillet, IPCO 2017

Bridging Quantum and Classical Optimization:

  • Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations – with J. Moondra, G. Mohler, P. Lotshaw (under revision at Quantum)
  • Strategies for running the QAOA at hundreds of qubits – with B. Augustino, M. Cain, E. Farhi, S. Gutmann, B. Ranard, E. Tang and K. Van Kirk, 2025 (arxiv)
  • Quantum Optimization: Potential, Challenges, and the Path Forward – with 40+ authors, Nature Reviews Physics 2024
  • Classically-inspired Mixers for QAOA Beat Goemans-Williamson’s Max-Cut at Low Circuit Depths, with R. Tate, J. Moondra, B. Gard, G. Mohler Quantum 2023
  • Generating Target Graph Couplings for QAOA from Native Quantum Hardware Couplings with J. Rajakumar, J. Moondra and C. Herold, Physical Review A, 2022
  • Bridging Classical and Quantum using SDP initialized warm-starts for QAOA with R. Tate, M. Farhadi, C. Herold and G. Mohler ACM Transactions of Quantum Computing, 2022