Projects
Explore publicly documented work across healthcare, markets, education, optimization, and scientific understanding.
Nine projects · Explore the public research and tools
Healthcare
José Blanchet, Peter Glynn, David Scheinker
SWIM (Simulation for Whole-Institution Modeling) uses discrete-event simulation to model patient flow and explore hospital capacity and operational choices. An interactive front end helps users examine how decisions across clinical units affect a shared system.
Financial markets
When many traders adopt the same AI forecast, their individual choices can crowd the same trades and reduce the forecast’s value to its adopters. Monoculture Lab combines a market model with an interactive simulation platform to explore how shared information, communication, and feedback shape adoption and trading outcomes.
Financial markets
How do settlement rules shape incentives across connected markets? This research combines a model and empirical analysis to study prediction-market contracts whose settlement prices can be influenced by trading the underlying asset, and examines the implications for contract design and price discovery.
Platforms
In dynamic systems, a decision changes both current outcomes and the conditions for future decisions. The truncated policy gradient estimator uses short outcome trajectories from randomized experiments to estimate treatment effects under nonstationarity, with theoretical analysis and real-world case studies.
Causal inference
CausalSmith coordinates AI agents that propose theoretical results in causal inference, formalize statements, and construct proofs checked by Lean. A statement audit compares formal theorems with their intended informal claims, while human inspection remains part of the research process.
Optimization
Ellen Vitercik, Madeleine Udell
FLARE combines language models with formal theorem proving to check whether reformulating a mixed-integer linear program preserves the underlying optimization problem. Its Lean-checked proofs support verification of proposed reformulations, evaluated using the FormulationBench benchmark.
Scientific understanding
Readable Intelligence is a research-reading prototype that starts with mathematics. Readable versions sit alongside original papers, with paper maps, notation tables, and a reading agent that links answers to source material. The platform helps readers connect explanations to the research they are trying to understand.
Education & entrepreneurship
How can students learn to determine whether a problem is real, important, and worth solving? Proxy-Expert Practice builds AI proxies of specific, consenting Stanford Distinguished Careers Institute fellows, grounded in multiple discovery interviews. Students practice entrepreneurial problem validation with these proxies before meeting the real fellows. The team is testing whether this practice improves live conversations through a randomized study in E145 and MS&E 272. PhD students Yikai Cao and Xilan Zhang develop the project with Chuck Eesley and Itai Ashlagi, with funding from the Stanford Accelerator for Learning.
Education & public systems
This work brings optimization and stakeholder feedback into an iterative school-zone design process. Interactive tools help participants explore candidate zones and tradeoffs, including diversity and travel distance, so that mathematical models can inform practical assignment decisions.
