Daniel Forger, Ph.D.

I study the mathematics of physiological factors which affect human performance such as sleep, proper timing by our internal daily (circadian) clock, and mood regulation. Applications of this work include an app to help travelers avoid jet lag, an analysis of the sleep and circadian patterns of medical interns and comparisons of performances of the Trio Sonatas by J. S. Bach. My students, post-docs, and I develop novel algorithms to measure these factors from smartphones, wearables, or other sensors in the real world. This data has low signal-to-noise ratios, contains large gaps which create mathematical artifacts, and is masked by unexpected real-world events.

I am also particularly interested in how physiology, e.g., neuropeptide regulation, the electrical activity of neurons, and cellular biochemistry, affects human performance.  To understand this physiology and differences that arise due to genetics or pharmacological intervention, we develop large-scale models to bridge scales from the genome to human behavior. Much of my time focuses on applying mathematical techniques from dynamical systems, numerical methods, and machine learning to simulate and analyze such large-scale models, often using graphics processing units.