# CAREER:Taming and exploiting uncertainty in complex systems: mean field games and diffusion generative models

> **NSF 01003031DB NSF RESEARCH & RELATED ACTIVIT** · Columbia University (NY) · $400,000

## Abstract

Modern engineering and economic challenges often involve complex systems that push the limits of traditional approaches, driven by uncertainty and the high dimensionality of real-world data. Scientists in a variety of fields have unprecedented access to massive amounts of data which hinge on complex structures. Decision makers often need to optimize strategies involving large populations full of randomness. This project will address these challenges by developing principled approaches for analyzing and solving complex systems. The project will focus on developing innovations in stochastic control theory and artificial intelligence (AI) algorithms which will inform basic science, technology and economic questions arising in a broad range of disciplines. The results will be disseminated broadly across scientific communities and the general public. The project will also provide training opportunities for graduate students and AI literacy programming at the K-12 level.

The project will focus on two interconnected research thrusts to address the aforementioned challenges, centered around the theoretical foundations of stochastic control and games. The first thrust involves studying mean field games (MFGs), which are used to model the macroscopic profile of large interacting systems, unraveling high degrees of freedom of these systems and enhancing tractability of large-scale problems. The goal is to build a quantitative theory for first-order MFGs in response to a surge of interest in modeling large systems with strong signals. Both theoretical and numerical challenges will be addressed, and the project will focus on direct applications in statistical physics and decentralized finance. The second thrust involves advancing generative AI to help mitigate uncertainty in the design of large-scale systems, and provide data-driven insights to identify unforeseen challenges in these systems. The goal of this second part of the project is to build a rigorous mathematical para

## Key facts

- **NSF award ID:** 2538791
- **Awardee organization:** Columbia University (NY)
- **SAM.gov UEI:** F4N1QNPB95M4
- **PI:** Wenpin Tang
- **Primary program:** 01003031DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, CAREER-Faculty Erly Career Dev
- **Estimated total:** $400,000
- **Funds obligated:** $253,570
- **Transaction type:** Continuing Grant
- **Period:** 05/15/2026 → 04/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2538791

## Citation

> US National Science Foundation, Award 2538791, CAREER:Taming and exploiting uncertainty in complex systems: mean field games and diffusion generative models. Retrieved via AI Analytics 2026-08-17 from https://api.ai-analytics.org/grant/nsf/2538791. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*