Dr. Daniel Lacker

Biography

Daniel Lacker works at the intersection of applied probability, stochastic analysis, and mathematical finance. His primary research areas include mean field game theory and interacting particle systems, forming the mathematical foundation for a wide range of models involving large-scale systems of interacting agents. The modeling framework, which originated in statistical physics, has been adapted to serve a variety of applications in social sciences, financial markets, income inequality, and pedestrian crowd dynamics. The common approach in physics approximates a large collection of discrete particles constituting a fluid by modeling a continuum of particles, making it easier to analyze and simulate; this approximation procedure is mathematically rigorous. Additionally, recent extensions of these models have found relevance in social scientific applications. Daniel's main research objective is to mathematically justify and quantify the ubiquitous 'mean field' approximations that arise in new and increasingly complex areas of application, particularly in game theory. He was also a National Science Foundation postdoctoral fellow in the Division of Applied Mathematics at Brown University from 2015 to 2017. He received his PhD from Princeton University in 2015 and his BS from Carnegie Mellon University in 2010.

Research Interests

Experience