Hi! I'm an assistant professor of Operations Research and Information
Engineering (ORIE) at Cornell Tech and Cornell University, and an ORIE, Computer Science, and Information Science field member.
Our work: "Full Stack, Public Interest AI"
I advance high-stakes, public interest AI in the wild. My work connects computational systems, individual decisions, and societal outcomes, considering three interrelated roles of algorithms: (1) algorithms allocate societal resources and attention; (2) algorithms support individuals navigating these systems; and (3) AI helps researchers understand these systems. We consider these roles in various domains: Human-Algorithmic Market
Design, Foundations and Systemic Effects of Modern AI, and Operations and Algorithms for Government.
My work is “full stack,” spanning the research-to-deployment pipeline: from theoretical modeling to empirical methods development, deployments and RCTs, and qualitative and policy analyses. These stages are complementary — a distinctive aspect of my work is in contributing across disciplines: operations, economics, machine learning theory and practice, statistics, and human-computer interaction.
See a recent
"manifesto" on the challenges caused by
heterogeneous participation in participatory systems (dubbed ``algorithmic administrative burdens''), which also surveys my work broadly. See a recent
talk video.
Our work has received several awards, including the NSF CAREER, William T. Grant Foundation Scholars Award, INFORMS George Dantzig Dissertation award, ACM SIGecom
Dissertation Award (Honorable Mention), and paper awards from EC, CSCW, INFORMS, and others.
Full bio.
Public Goods for Researchers
We build tools and workflows for other researchers: please use them!
AppliedModelingLib
[Library and workflow · Paper]
A Lean library and AI-assisted workflow for formalizing applied mathematical research -- autoformalize your papers! Over 40 papers formalized, including most of my own work.
Universal Concept Dictionary
[Model · Paper]
An interpretable embedding model that maps text to sparse vectors over 80,000 labeled concepts for statistical analysis and discovery.
Paper Skygest
[Feed · Paper]
A personalized Bluesky feed for discovering research shared by your network, with open-source code for building custom feeds. Over 1,000 daily active users.
MIGRATE
[Request data access · Paper]
Fine-grained estimates of annual migration between US neighborhoods, combining detailed address data with reliable Census statistics. Data requested by over 200 researchers.
Human-Algorithmic Market Design
Recommenders help us find work, education, and healthcare. I study public interest recommenders, tackling individual-level (e.g., exploration and agency) and societal challenges (e.g., fairness and congestion).
NYC High School matching and recommendation in equilibrium. We first showed (Nature Cities'26) that disadvantaged students disproportionately do not apply to high-performing programs they would have been admitted to had they applied; the gap was largest among the most competitive applicants. In collaboration with NYC Public Schools, we then developed personalized recommendations to reduce disparities, highlighting nearby, high-performing programs where a student had high predicted offer likelihood (ACM EC'26, Best Paper with a Student Lead Author). The central challenge was recommendation-induced congestion: naive recommendations cause sharp, disparate decreases in acceptance rates. We developed and theoretically analyzed a congestion-aware bilevel optimize-and-simulate approach to allocate recommendations and improve match outcomes in equilibrium.
We deployed our approach in the Fall 2025 cycle in an RCT! See the Cornell Chronicle story, and talk video.
Social media feed design. Leveraging Bluesky's open infrastructure, we launched Paper Skygest (paper). Our other projects in the space — a Skytrails research demo and essay — demonstrate a social media experience that allows users to explore their interests.
Platform to help place discharged hospital patients into long-term-care facilities. In Hawaiʻi, a PhD advisee built and deployed a platform to help place discharged hospital patients into one of more than 1,000 long-term-care facilities, many of which are run by single individuals out of their homes. The platform texts homes to ask for updated capacity and preference information, and then provides this information to about 10 hospital social workers; it has helped place hundreds of patients (CSCW'25, Best Paper Award). We also experimentally studied how preferences and incentives shape placements (CSCW'25, Impact Recognition), and then deployed and evaluated active information acquisition with language model support.
Theory. We theoretically model algorithmic monoculture and the wisdom of crowds in matching markets (NeurIPS'24; ACM EC'24) and the downstream strategic, diversity, and fairness implications of ranking algorithms in recommender ecosystems (NeurIPS'24; The Web Conference'24).
Foundations and Systemic Effects of Modern AI
AI helps researchers understand these systems. From my PhD work on text embeddings to modern LLM-based interpretability, I develop language modeling methods to use text as data.
Methods to use text as data for human understanding. During my PhD, I developed methods to use word embeddings to study historical societal stereotypes (PNAS'18). LLM-based word embeddings have since dramatically improved, but still face an interpretability challenge: individual vector dimensions are not inherently interpretable, complicating their use. More recently, we used a modern LLM interpretability method — sparse autoencoders —
to tackle this issue (ICML'25). Our method has had considerable follow-up use and contributed to renewed interest in SAEs (ICML'26). We have since begun applying these techniques to social-science questions and studying their theoretical foundations through connections to compressed sensing (COLT'26) and a testable theory of atomic features.
LLM homogeneity and its market implications.
We showed that LLMs make correlated errors, reflecting concerns of LLM homogeneity, with implications for LLM-as-judge and hiring markets (ICML'25). On the other hand, homogeneity may also be leveraged to detect LLM text in a semi-supervised manner, using test-time adaptation for robustness to distribution shift and strategic behavior (NeurIPS'26).
Diversity and verification in AI-assisted mathematics. Finally, I am working on how to best integrate the progress in AI proof generation into scientific processes through AppliedModelingLib.
Operations and Algorithms for Government
I develop computational methods for efficient and equitable government service allocation.
Resident crowdsourcing, with the NYC Department of Parks and Recreation (Talk video). Do some neighborhoods report more than others for the same underlying conditions on the ground, thus receiving better government services? Answering this question requires new methods because we do not directly observe those conditions. First, we used duplicate 311 reports to estimate reporting rates without external ground-truth data, finding that higher-income and more educated neighborhoods report the same types of problems faster (Nature Computational Science'24). We also leveraged spatial correlation (AAAI'24), combined government ratings and crowdsourced reports (AAAI'26), and used vision-language models (Nature Communications'26) to identify incidents and quantify underreporting.
We then connect measurement to operational decisions. We co-design capacity planning and “service-level agreements” (response-time guarantees). We use Bayesian optimization to allocate service capacity across boroughs and incident types, and theoretically analyze efficiency-equity tradeoffs and the gains from centralization (ACM EC'24). We have also built and transferred a dashboard comparing reporting and response patterns and an integer program for NYCDPR's nine-year tree-planting cycle, balancing geographic constraints with heat-vulnerability-based need.
Other applications. With the New York Public Library, we found that digital holds move the most desired books from low-income to high-income branches (AAAI'24) and developed an operational intervention that optimizes which branch supplies each requested book to mitigate this inequity. We showed that multi-member districts with ranked-choice voting can curtail partisan gerrymandering (Operations Research'26; ACM EC'22) and studied equity in congestion pricing (ACM EC'24). We also use optimization to debias demographic predictions in voter files (PNAS Nexus'25) and granular estimates of intra-US migration (Nature Communications'26).
