About

MIT Sloan · Operations Research & Statistics

I am an Associate Professor at the MIT Sloan School of Management in OR & Statistics and a Class of 1947 Career Development Professor. I am a core faculty member of the Operations Research Center, and affiliated with LIDS and CCSE. I received a Ph.D. in Operations Research from MIT and was fortunate to be advised by Michel Goemans and Patrick Jaillet. I hold a joint Masters and B.Tech in Computer Science from IIT Delhi. My Erdős number is 2, and I’m on the scientific advisory board of Intrare.

Contact

first name and last initial at mit.edu
E62-582 (or a coffee shop near MIT)

Support staff

Patrick W. McGill
pmcgil19 at mit.edu

Latest news

Paper on scholarships to students to reduce filtering effect of middle schools, accepted to M&SOM. More updates

Research: Rethinking Optimization in Today’s World

Optimization, machine learning/AI, and societal impact

Foundations of optimization were built for a world that no longer exists: clean data, fixed objectives, and static computation. Modern systems challenge each of these assumptions. Data are noisy, biased, and heterogeneous, shaped by how they were produced. Stakeholders disagree, and preferences often emerge only through interaction and language. Computation itself is increasingly heterogeneous. Solvers now combine classical subroutines, learned language models, specialized hardware, and quantum devices, each with their own cost and noise.

My group develops the mathematical foundations of optimization for this regime and carries them into consequential systems—from hiring, school admissions, and organ allocation to power systems, AI infrastructure, and quantum computing. Our goal is to expand what optimization can guarantee when the ingredients of the problem must themselves be learned, elicited, or composed.

Hover a topic in the map to see its papers; click to open them on the publications page.

Thrust 1

Contextual and Noisy Data

  • Ordinal models
  • Cardinal models

Data carries the imprint of how it was made — social dynamics, measurement error, historical bias — and those flaws propagate unevenly into the decisions built on it. We develop both ordinal and cardinal models to confront this. Ordinal methods act on rankings rather than scores, sidestepping the contested numerical weightings that make decisions legally vulnerable; cardinal methods correct biased data directly and trace how the remaining uncertainty shapes downstream outcomes. Together, they have yielded new insights into matching markets, faster detection of critical health conditions, and novel scholarship mechanisms for disadvantaged students.

Publications

Hiring and school admissions
Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventionswith Y. Faenza, A. Vuorinen, X. Zhang. M&SOM 2026
Discovering Opportunities in New York City’s Discovery Program: Disadvantaged Students in Highly Competitive Marketswith Y. Faenza, X. Zhang. EC 2023; Operations Research (major revision)
Secretary Problems with Biased Evaluations using Partial Ordinal Informationwith J. Salem. Management Science 2023
Using Algorithms to Tame Discrimination: A Path to Algorithmic Diversity, Equity and Inclusionwith 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 Paradoxwith J. Salem, D. R. Desai. FAccT 2022
Closing the Gap: Mitigating Bias in Online Resume-Filteringwith J. Salem. WINE 2020
Alignment and Misalignment of Fairness Constraints in Real-World Candidate-Job Matchingswith A. Trapp, M. Vasconcellos. Working paper
Data Heterogeneity and Domain Expertise
Uncertainty-Aware AI Significantly Reduces Organ Non-Utilization Rateswith M. Pollack, T. Daillak, M. Toner, K. Uygun, H. Yeh. Working paper
Improving Clinical Decision Support through Interpretable Machine Learning and Error Correction in Electronic Health Recordswith M. Arora, H. Mortagy, N. Dwarshuis, J. Wang, P. Yang, A. Holder, R. Kamaleswaran. JAMIA 2025

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Thrust 2

Beyond a Single Objective

  • Portfolios
  • Multi-criteria guarantees
  • Trajectories

Real-world systems must balance competing demands — fairness, efficiency, reliability, compliance with law and policy — and no single objective function captures them all. Rather than commit to one formulation, our work builds provably small sets of solutions that collectively cover any formulation in a given class (portfolios), turning an unresolvable modeling debate into an actionable menu. We also develop methods that account for repeated user interactions over time, as data evolves and the system learns its parameters. Here, our algorithms constrain the entire trajectory of iterates in online learning and stochastic optimization — not just the endpoint — to balance fairness against efficiency along the way.

Publications

Portfolios
Balancing Notions of Equity: Trade-offs Between Fair Portfolio Sizes and Achievable Guaranteeswith J. Moondra, M. Singh. SODA 2025 (invited to Transactions on Algorithms); Mathematical Programming 2026
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learningwith A. C. W. Kim, J. Moondra, S. Verma, M. Pollack, L. Kong, M. Tambe. ICML 2025
Which Lp Norm is the Fairest? Approximations for Fair Facility Location across all pwith J. Moondra, M. Singh. EC 2023
Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised MLwith J. Moondra, A. Chughtai, B. Lanka. Under review
Many Preferences, Few Policies: Towards Scalable and Tractable Language Model Personalizationwith A. C. W. Kim, J. Moondra, R. Nahavandi, A. Perrault, M. Tambe. COLM 2026 Workshop on Scientific Understanding of Language Models
Provably Small Portfolios for Multi-objective Optimization with Application to Subsidized Facility Locationwith J. Moondra, M. Singh. Working paper
Multi-criteria guarantees
Too Many Fairness Metrics: Is There a Solution? Equity across Demographic Groups for the Facility Location Problemwith A. Jalan, G. Ranade, H. Yang, S. Zhuang. Fields Institute Communications 2026
Equitably Allocating Wildfire Resilience Investments for Power Grids: The Curse of Aggregation and Vulnerability Indiceswith M. Pollack, R. Pianski, D. Molzahn. Applied Energy 2025
Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks using Submodularitywith C. Hettle, D. Molzahn. INFORMS Journal on Computing 2025
Mathematically Quantifying Gerrymandering and Non-Responsiveness of the 2021 Georgia Congressional Districting Planwith Z. Zhao, C. Hettle, J. Mattingly, D. Randall, G. Herschlag. EAAMO 2022
Balanced Districting with Provable Compactness and Contiguitywith C. Hettle, S. Zhu, Y. Xie. FORC 2021
Fair Hierarchical Facility Location Problemwith J. Moondra, M. Singh. Working paper
Trajectories
Algorithmic Challenges in Ensuring Fairness at the Time of Decisionwith J. Salem, V. Kamble. WINE 2022; Operations Research 2025
Temporal Fairness in Online Decision-Makingwith V. Kamble, J. Salem. Ethics in AI: Bias, Fairness and Beyond, 2023 (book chapter)
Individual Fairness in Hindsightwith V. Kamble. EC 2019; JMLR 2021
Group-Fair Online Allocation in Continuous Timewith S. Cayci, A. Eryilmaz. NeurIPS 2020

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Thrust 3

Optimization under Heterogeneous Computation

  • Discrete & continuous
  • Optimization & AI/ML
  • Classical & quantum

Faster computation requires opening the black boxes that traditional optimization methods typically assume, and exploiting the structure of decisions across computational paradigms. Our work first bridges discrete and continuous optimization, through new ways of warm-starting and rounding fractional solutions and carrying structural information between subproblems in iterative methods. A second bridge joins optimization and AI: we bring learning inside optimization subroutines. The third, looking ahead, spans classical and quantum compute: methods that compose classical optimization subroutines within quantum computation.

Publications

Bridging discrete and continuous optimization
Improved Regret Guarantees for Online Mirror Descent using a Portfolio of Mirror Mapswith J. Moondra, M. Singh. Under submission (arXiv)
Hardness and Approximations for Submodular Minimum Linear Ordering Problemswith M. Farhadi, S. Sun, P. Tetali, M. Wigal. Mathematical Programming 2023
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimizationwith H. Mortagy, S. Pokutta. NeurIPS 2020; Mathematics of Operations Research (major revision)
Electric Flows over Spanning Treeswith A. Khodabakhsh, H. Mortagy, E. Nikolova. Mathematical Programming B 2020
Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectiveswith S. Zhou, M. Udell. NeurIPS 2018
Newton’s Method for Parametric Submodular Function Minimizationwith M. Goemans, P. Jaillet. IPCO 2017
Faster Parametric Submodular Function Minimization using Dualitywith A. Zhu. Working paper
Solving Combinatorial Games using Products, Projections and Lexicographically Optimal Baseswith M. Goemans, P. Jaillet. Working paper
Bridging optimization and AI/ML
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learningwith W. Won, M. Elavazhagan, S. Srinivasan, A. Durg, S. Kaul, T. Krishna. MICRO 2024
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopeswith H. Mortagy, J. Moondra. NeurIPS 2021
What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBOwith I. Dunning, J. Silberholz. INFORMS Journal on Computing 2018
Adaptive Cost-Aware Stochastic Optimization with Heterogeneous Gradient Oracleswith T. Li. Working paper
Hybrid classical–quantum optimization
Promise of Graph Sparsification and Decomposition on Noise Reduction: Analysis for Trapped-Ion Compilations for QAOAwith J. Moondra, P. Lotshaw, G. Mohler. Quantum 2026
Comparison of Hyperplane Rounding for Max-Cut and Quantum Approximate Optimization Algorithm over Certain Regular Graph Familieswith R. Tate. Operations Research Letters 2026
Quantum Optimization: Potential, Challenges, and the Path Forwardwith 40+ authors. Nature Reviews Physics 2024
Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson’s Max-Cut at Low Circuit Depthswith R. Tate, J. Moondra, B. Gard, G. Mohler. Quantum 2023
Generating Target Graph Couplings for QAOA from Native Quantum Hardware Couplingswith J. Rajakumar, J. Moondra, C. Herold. Physical Review A 2022
Bridging Classical and Quantum using SDP Initialized Warm-starts for QAOAwith R. Tate, M. Farhadi, C. Herold, G. Mohler. ACM Transactions on Quantum Computing 2022
Strategies for Running the QAOA at Hundreds of Qubitswith B. Augustino, M. Cain, E. Farhi, S. Gutmann, D. Ranard, E. Tang, K. Van Kirk. Working paper

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Browse the full publication list

Research Group

Students, postdocs, and alumni

I’m fortunate to work with many fantastic students (Research Group). If you are a current MIT student, and are interested in joining the group, feel free to reach out.

Current PhD and Masters

Madeleine Pollack — PhD, ORC (2023–)
Jonathan Zhou — PhD, ORC (2025–), co-advised
Emily Kay-Leighton — PhD, ORC (2026–), co-advised
Lorinc Mate — Masters, ORC (2026–)
Mehrdad Sohrabi — incoming

PhD and Postdoc Alumni

Tianjiao Li (2026) — IBM Herman Goldstine Memorial Postdoctoral Fellow (2026-2027), Assistant Professor at Wisconsin Madison ISyE (2027+)
Jai Moondra (2025) — Postdoc, CMU Tepper School of Business On the job market
Jad Salem (2023) — Assistant Professor, US Naval Academy
Reuben Tate (2023) — Research Scientist, Los Alamos National Labs
Hassan Mortagy (2023) — OR Scientist, Roadie

Selected Awards and Honors

Individual awards, funding, and group recognition

  • 2023NSF CAREER Award
  • 2021JP Morgan Chase Early Career Faculty Recognition
  • 2020–21CIOS Honor Roll for teaching excellence, Georgia Tech
  • 2019NSF CISE Research Initiation Initiative Award
  • 2018Simons-Berkeley Research Fellowship (Real-Time Decision Making), funded as a Microsoft Research Fellowship
  • 2017Simons-Berkeley Research Fellowship (Bridging Continuous and Discrete Optimization)
  • 2011Google India Women in Engineering Award

Keynotes

  • 2025Purdue Quantum AI, Gavriel Salvendy International Symposium
  • 2022Lorentz Center Workshop on Advanced Optimization for Social Choice

I led the technical thrust of Ethical AI in the multi-institution NSF AI Institute on Advances in Optimization (ai4opt.org) from 2021–2023, and was the Georgia Tech PI on the $9.2M multi-institution DARPA award on Optimization for Trapped Ion Qubits from 2020–2024. My research is also supported by cross-disciplinary initiatives at MIT, including MIT-MGB HEALS (2025), SERC (2024), the MIT HSI Initiative (2025), and the MIT-IBM Computing Initiative.

Group recognition

Professional Service

Editorial roles, organizing, program and prize committees

Editorial and leadership

Associate Editor, Open Journal of Mathematical Optimization (2020–present)
Job Market Showcase Track Chair, INFORMS Annual Meeting (2026)
Co-editor, OPTIMA, newsletter of the Mathematical Optimization Society (2021–2025)
Guest Editor, Fairness special issue, Health Care Management Science (2021–2024)
Guest Editor, Data Science and Optimization, Fields Institute Communications (2020–2024)
Lead, Ethical AI thrust, NSF AI Institute AI4OPT (2021–2023)
Member, INFORMS Computing Society Quantum Computing Working Group (2020–2022)

Program committees

MLxOR NeurIPS Workshop 2026
IPCO 2026
IPCO 2024
NeurIPS 2023 · area chair
SIAM ACDA 2023
ACM FAccT 2022 · area chair
ACM EAAMO 2022
WINE 2021
FORC 2021 · publications co-chair
EC Workshop on Operations of People-Centric Systems 2021
The Web Conference 2021
AAAI and AAAI AI for Social Impact Track 2020
APPROX 2019

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Workshops organized

Grid Science Winter School, Los Alamos National Lab 2023
ICERM Workshop on Trends in Computational Discrete Optimization 2023
Quantum Computing and Operations Research, Fields Institute 2022
CCC–INFORMS AI/OR Workshop 2022
IPCO Organizing Committee 2021
Focused Program on Data Science and Optimization, Fields Institute 2019

Prize committees

INFORMS Public Sector OR Prize 2026
INFORMS Computing Society Student Paper 2023
SIAM ACDA Best Student Presentation 2023
INFORMS DEI Best Student Paper 2022
INFORMS Doing Good with OR Student Paper 2021

Miscellaneous

News, teaching, media, and art

So proud of my husband, Tushar Krishna, who just won the ACM-SIGARCH Maurice Wilkes Award 2026!