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Emily Mower Provost, Ph.D.

Assistant Professor of Electrical Engineering and Computer Science
University of Michigan
College of Engineering

emilykmp@umich.edu

Dr. Mower Provost received her B.S. in Electrical Engineering (summa cum laude and with thesis honors) from Tufts University and her M.S. and Ph.D. in Electrical Engineering from the University of Southern California (USC).

Dr. Mower Provost is an Assistant Professor in the Computer Science and Engineering (CSE) Department. She has been awarded the National Science Foundation Graduate Research Fellowship (2004-2007), the Herbert Kunzel Engineering Fellowship from USC (2007-2008, 2010-2011), the Intel Research Fellowship (2008-2010), and the Achievement Rewards for College Scientists (ARCS) Award (2009-2010). Her research interests are in human-centered speech and video processing and multimodal interface design. The goals of her research are motivated by the complexities of human emotion generation and perception.

As a member of the Prechter Bipolar Research team, Dr. Mower Provost leads the development of computational methods of predicting mood swings in bipolar disorder. Her work focuses on understanding the specific patterns that accompany transitions from healthy euthymic states to either mania or depression. She has worked to develop methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Her investigations suggest that manic and depressive mood states can be recognized from these speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with bipolar disorder.

Dr. Mower Provost’s work also focuses on understanding the human emotion perception process. This research is motivated by the critical need for novel methods that forward the assessment and treatment of mood disorders. Her work focuses on the link between changes in how people perceive emotion and changes in mood. This has important implications for developing therapies and characterizing the severity of mood disorders.