About Me
Hi! I am an Assistant Professor in the Technology and Operations Management Unit of Harvard Business School. I am also a Faculty Affiliate at the Immigration Policy Lab at Stanford University.
My research uses AI, machine learning, and optimization to improve decision-making in public and social-sector operations. I study how algorithmic systems can personalize interventions, allocate scarce resources, and incorporate fairness considerations in ways that are useful to the people and organizations that deploy them. One area of focus is improving access to healthy food, in collaboration with the Massachusetts Department of Transitional Assistance and Met Council. Another stream of research develops algorithmic decision-support tools to improve refugee resettlement. This work is in collaboration with the GeoMatch team at the Stanford Immigration Policy Lab, where I completed a postdoc.
I received my PhD in Operations Research from MIT in 2021, where I was co-advised by Retsef Levi and Georgia Perakis. Before coming to MIT, I received a B.S. in Math, B.S. in Statistics, and M.A. in Math from the Pennsylvania State University.
Email: epaulson (at) hbs (dot) edu
Working Papers
- Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices.
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A Dual Perspective on Decision-Focused Learning: Scalable Training via Dual-Guided Surrogates.
- Accepted to the NeurIPS 2025 MLxOR Workshop
- CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets.
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Group Fairness in Dynamic Refugee Assignment.
- Extended abstract appeared at the 24th ACM Conference on Economics and Computation (EC), 2023
- Extended abstract appeared at the 4th annual Symposium on Foundations of Responsible Computing (FORC), 2023
Published
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Heterogeneous Treatment Effects in Panel Data: Insights into the Healthy Incentives Program
Manufacturing & Service Operations Management, 2026 (Accepted)
- Accepted to the 2025 Sustainable Operations SIG-Day Conference
- Accepted to the NeurIPS 2025 MLxOR Workshop
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Enhancing the Benefits of Dual Sourcing with Reverse Information Sharing.
Manufacturing & Service Operations Management, 2026 (Accepted)
- 2nd place, POMS College of Supply Chain Management Best Student Paper Competition, 2021
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Dynamic Matching with Post-Allocation Service and its Application to Refugee Resettlement.
Management Science, 2026 (Articles in Advance)
- Honorable Mention, George Nicholson Best Student Paper Competition, 2025, Entrant: S. Lee
- Finalist, Service Science Best Student Paper Award, 2025, Entrant: S. Lee
- 1st place, MSOM Best Student Paper Prize, 2024, Entrant: S. Lee
- 1st place, Michael H. Rothkopf Junior Researcher Paper Prize, 2024, Entrant: S. Lee
- Accepted to the 25th ACM Conference on Economics and Computation (EC), 2024
- Accepted to the 5th annual Symposium on Foundations of Responsible Computing (FORC), 2024
- Accepted to the 9th MarketPlace Innovation Workshop (MIW), 2024
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Designing Inclusive Offerings.
Management Science, 2025 (Articles in Advance)
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Optimal Interventions for Increasing Healthy Food Consumption Among Low-Income Populations.
Management Science, 2025 (Articles in Advance)
- Finalist, POMS College of Sustainable Operations Best Student Paper Competition, 2021
- Finalist, IBM Best Student Paper Award, 2019
- Accepted for oral presentation, 4th Workshop on Mechanism Design for Social Good (MD4SG '20)
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Public Attitudes on Performance for Algorithmic and Human Decision-Makers.
PNAS Nexus, 2024
- Accepted to the 7th AAAI Conference on AI, Ethics, and Society (AIES), 2024
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Learning Under Random Distributional Shifts.
Artificial Intelligence and Statistics (AISTATS), 2024
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Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing.
Operations Research, 2024
- Extended abstract appeared at the 23rd ACM Conference on Economics and Computation (EC), 2022
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Public Health Risks Arising from Food Supply Chains: Challenges and Opportunities.
Naval Research Logistics (special issue on OR Models in Developmental Studies), 2021
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A Game Theoretic Model for Resource Allocation Among Countermeasures with Multiple Attributes.
European Journal of Operations Research, 2016
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Cooperation Can Emerge in Prisoner’s Dilemma from a Multi-Species Predator Prey Replicator Dynamic.
Mathematical Biosciences, 2016
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Deriving an Optimally Deceptive Policy in Two-Player Iterated Games.
American Control Conference, 2016
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Optimal Process Control of Symbolic Transfer Functions.
Feedback Computing, 2015
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Better Timing of Cyber Conflict.
Third ASE International Conference on Cyber Security, 2014
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Helfende Wände: A Housing Platform for a Humanitarian Crisis
E. Paulson, N. Jönsson, and C. Moniz. Harvard Business School Case 626-063, June 2026.
In 2025, Helfende Wände, a non-profit housing platform launched by Berlin-based digital marketplace for furnished medium-term rentals Wunderflats, faced a critical juncture. Created in early 2022 in response to Russia’s invasion of Ukraine, the platform adapted Wunderflats’ commercial technology to connect displaced Ukrainians with private housing in Germany for free or at reduced rents. What began as an emergency initiative evolved into an official housing channel in partnership with the German Federal Ministry of the Interior and the non-profit ProjectTogether, ultimately housing tens of thousands of refugees. However, as refugee inflows slowed, landlord participation declined, and public funding tapered off, the platform’s future became uncertain. The case explores the strategic choices confronting Wunderflats’ founders: whether to keep Helfende Wände as a crisis-response tool, expand it to serve other refugee groups, or transfer ownership to the government, raising broader questions about the role of private digital platforms in delivering public goods.
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Resilience Lab
E. Paulson and T. Quinn. Harvard Business School Case 625-078, March 2025.
In January 2025, Resilience Lab, a hybrid mental health care company, was trying to improve one of the most important parts of its operating model: matching clients to therapists. The company employed roughly 300 clinicians, many of them early-career therapists, and trained them through its own Resilience Institute in a standardized, measurement-informed approach to care. Improving the matching algorithm raised platform design questions around filters, rankings, user choice, clinician utilization, fairness, and evaluation. At the same time, Resilience Lab was expanding its use of data and AI, including through its 2024 acquisition of OptionsMD, a startup developing AI tools to support treatment decisions for more complex mental health conditions. The case asks students to consider how far data, algorithms, and AI can improve a deeply human service, and what safeguards are needed to preserve patient trust, clinician trust, and professional judgment.
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VOCEL(A): Democratizing Brain Science for Early Childhood Education
E. Paulson, C. T. Ryan, and N. Zhang. Harvard Business School Case 625-081, January 2025. Revised April 2025.
VOCEL(B): Powered by VOCEL
E. Paulson, C. T. Ryan, and N. Zhang. Harvard Business School Supplement 625-082, January 2025.
VOCEL, a non-profit organization founded to apply cutting-edge brain science to early childhood education, initially focused on operating a high-quality preschool. However, due to funding constraints and labor challenges, the organization pivoted to a new model: the VOCEL Child Parent Academy (VCPA), a two-generation program where trained VOCEL staff worked directly with both parents and children to foster developmental skills. As VCPA expanded across Chicago, operational complexities arose, particularly in scheduling and staff deployment. A key inflection point occurred when a school principal requested that VOCEL empower local stakeholders rather than deliver the program themselves. This request triggers a deeper reflection about VOCEL’s theory of change, mission integrity, and operational model. Students are left with the dilemma about whether to accept the request or continue developing the VCPA in its current mode.
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Market by Met Council: Revolutionizing Food Pantries in the Digital Age
E. Paulson and M. Toffel. Harvard Business School Case 624-060, May 2024.
In fall 2023, the Food Program of Met Council—America’s largest Jewish charity dedicated to fighting poverty—completed the rollout of the newest version of its digital pantry platform to twelve food pantries in the Met Council food pantry network. The digital initiative coincided with a shift from food pantries’ traditional “pre-packed” model—in which pantry staff and volunteers pre-packed standardized bags of foods and handed them out to long lines of waiting clients (the standard model in the US)—to a “client choice” model, where clients could choose their own food items. Over half of the pantries in Met Council’s network were undergoing the transition to client choice. For most of these pantries, the client choice model was initially implemented as an in-person shopping experience, similar to a small-scale grocery store. For the digital pantries, though, clients would be able to see available items and place orders online, similar to an online grocery shopping experience. Met Council viewed the digital initiative as the next step towards increasing the dignity of the pantry experience and incentivizing healthy food choices. This case discusses the evolution of the digital pantry; specifically, the pros and cons of each pantry model from an operational efficiency perspective, how operational levers can influence consumers’ purchasing decisions, fairness in resource allocation problems, and “push” versus “pull” inventory distribution models.
My research has been featured in the following articles:
- “Design Smarter—Not Bigger—Product Lines to Draw More Customers” HBS Working Knowledge
- “Who Should Approve Bank Loans: People or Algorithms?” HBS Working Knowledge
- “How AI Could Ease the Refugee Crisis and Bring New Talent to Businesses” HBS Working Knowledge
- “Can Government Programs Get People to Eat More Healthily?” Chicago Booth Review
- “Unequal Access to Food” MIT Sloan Year in Review
- “Using Machine Learning to Help Refugees Succeed” Stanford HAI