The Schedule

Emily Mower Provost is a Professor and Associate Chair for Graduate Affairs in Computer Science and Engineering and Professor of Psychiatry (by courtesy) at the University of Michigan. She received her Ph.D. in Electrical Engineering from the University of Southern California (USC), Los Angeles, CA in 2010. She has been awarded a Toyota Faculty Scholar Award, National Science Foundation CAREER Award, and the Oscar Stern Award for Depression Research. She is a co-author of multiple award-winning papers in the field of automatic emotion recognition. 

Mower Provost's research is focused on advancing speech-centered maching learning for human behavior detection. The CHAI lab focuses on three main areas: 1) emotion recognition, 2) mental health modeling, and 3) assistive technology. Mower Provost is the creator of an smartphone application and set of computational algorithms called PRIORI (Predicting Indidividual Outcomes for Rapid Intervention) which aims to detect mood instability via changes in emotional valence and arousal detected in speech. 

Daniel Forger is the Robert W. and Lynn H. Browne Professor in Science in the Mathematics Department at the University of Michigan. He received a Masters of Science in Applied Mathematics (Medical Sciences) From Harvard University and his Ph.D. in Mathematics from New York University. Forger is funded by the National Science Foundation, National Institutes of Health, Department of Defense, and the Human Frontier Science Program. He has won numerous awards for his significant contributions to mathematical sciences. 

Forger studies the mathematics of physiological factors which affect human performance such as sleep, the circadian clock, and mood regulation. He and his lab develop novel algorithms to measure these factors in the real world using smartphones, wearables, and other sensors. This data has low signal to noise ratios, contains large gaps which create mathematical artifacts, and is masked by unexpected real-world events. In order to account for this, Forger and his team apply mathetmatical techniques from dynamical systems, numerical methods and machine learning to simulate and analyze high dimensional data, and often use graphics processing units.