Daniel Lacker

These mathematical models appear in diverse areas of science, where the individuals may represent people, viruses, or particles, and the large systems may be financial markets, epidemics, or fluids. Daniel's work explains theoretical principles of how macro-level structures, such as an epidemic, can emerge from micro-level rules, such as person-to-person transmission and social networks.

It is a common practice in physics to approximate a large collection of discrete particles, such as those constituting a fluid, by modeling a continuum of particles. Continuous models are often much easier to analyze or simulate, and this approximation procedure has been made mathematically rigorous. On the other hand, extensions of these models for modern applications beyond physics are not yet well understood. A main objective of Daniel's research is to mathematically justify and quantify these ubiquitous "mean field" approximations as they arise in new and increasingly complex areas of application, particularly game theory.

Daniel was an NSF postdoctoral fellow in the Division of Applied Mathematics at Brown University from 2015-2017. He received his PhD from Princeton University in 2015 and his BS from Carnegie Mellon University in 2010. His research has been recognized with an NSF CAREER award as well as a Sloan Research Fellowship.

Research Areas

• Financial engineering
• Game theory
• Optimization
• Stochastic modeling and simulation