Associate Professor,
Class of 1947 Career Development Professor,
Operations Research & Statistics,
Sloan School of Management, MIT.
My research interests lie in developing new foundations of optimization to address modern societal and computational challenges.
Contact: first name and last initial at mit.edu, E62-582 (or a coffee shop near MIT)
Support Staff: Natalie Jean (njean91 at mit.edu)
About me: 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 Erdös number is 2.
Research Philosophy: The classical foundations of optimization were built for a world that no longer exists: clean data, fixed objectives, and static computational models. Modern decision systems violate each of these assumptions. Data is noisy, biased, and heterogeneous; objectives are contested across stakeholders and increasingly expressed through human interaction and language; and computation itself is evolving through machine learning systems, specialized hardware, and quantum architectures. My research develops new mathematical foundations for optimization in these settings. Rather than applying existing methods to new applications, I aim to redesign the underlying representations of data, objectives, formulations, and computational processes that optimization relies on. Across several research programs discussed below, my group develops new theory and algorithms, while carrying these ideas back into the institutions and systems that motivate them. My work has informed or been deployed in settings spanning hiring platforms, public school admissions, organ allocation, electric utility systems, districting and emergency response, AI infrastructure, and quantum hardware benchmarking. The three broad programs we are currently focused on are:
(1) Contextual and Noisy Data. Algorithms for optimization and learning under systematically biased, noisy, or partially ordered information, with applications in hiring, admissions, recommendation systems, rankings, and healthcare.
(2) Portfolios for Underspecified Formulations. Optimization frameworks that compute provably small sets of solutions spanning stakeholder trade-offs and welfare frontiers, with applications in facility location, reinforcement learning, LLM alignment, and equitable infrastructure planning.
(3) Optimization under Heterogeneous Computation. Optimization methods that compose classical subroutines, learned modules, and hardware-specific compute — including near-term quantum devices — each with its own cost, fidelity, and noise profile.
Underlying these directions is a parallel body of my work on the foundations of optimization, including constrained descent methods, submodular optimization, online optimization, and scalable projections.
Selected Publications:
- Balancing Notions of Equity: Trade-offs Between Fair Portfolio Sizes and Achievable Guarantees – with J. Moondra, M. Singh, SODA 2025 (with invitation to Transactions on Algorithms). Minor rev, Mathematical Programming, 2025.
- Algorithmic Challenges in Ensuring Fairness at the Time of Decision – with J. Salem, V. Kamble, WINE 2022, and Operations Research 2025.
- Secretary Problems with Biased Evaluations using Partial Ordinal Information – with J. Salem. Management Science 2023.
- 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.
You can further explore my research projects and publications here.
I’m fortunate to have the opportunity to work with many fantastic students, check out my Research Group. I am always excited to work with MIT students interested in optimization, machine learning, AI, quantum computing, and algorithmic fairness. If you are interested in joining the group, feel free to reach out. My research group has been supported by industry funding, as well as federal agencies like NSF and DARPA, and from cross-institute interdisciplinary initiatives at MIT such as SERC and MIT-MGB HEALS.
I received the prestigious NSF CAREER Award in 2023, the JP Morgan Early Career Faculty Recognition in 2021, the NSF CISE Research Initiation Initiative Award in 2019, Simons Berkeley Research Fellowship 2017-2018, and the Google Women in Engineering Award (India) in 2011. I also led the technical thrust of Ethical AI in the multi-institution NSF AI Institute on Advances in Optimization (ai4opt.org), from 2021-2023. My group has received recognition at various venues like NeurIPS spotlight, INFORMS Doing Good with OR 2022, INFORMS Undergraduate Operations Research 2018, INFORMS Computing Society 2016, and INFORMS Service Science Student Paper 2016.
I am an associate editor for Open Journal of Mathematical Optimization. I previously served as a co-editor for OPTIMA (newsletter of the Mathematical Optimization Society). I am (or have served) on the PC for IPCO 2026, IPCO 2024, NeurIPS 2023 (area chair), ACDA 2023, EAAMO 2022, FAccT 2022 (area chair), WINE 2021 and FORC 2021 (publications chair), and award committees for INFORMS competitions. I have also guest-edited a special issue on Data Science and Optimization for the Fields Communication Series.
For more information about the latest activity in my research group, check out News!
For more information about my educational activities and courses, check out Teaching.
For media inquiries, reach out to Natalie Jean (njean91 at mit.edu).
For my artistic pursuits and exhibits, check out ProofbyPalette.
