News by Faculty
Emily Mower Provost
CSE researchers report over $11M in research grants last quarter
The awards were distributed to 18 different primary investigators.
Faculty Profile: Emily Mower Provost
Mower Provost talks about getting awards, doing industry research, understanding human behavior – and Star Wars.
Emily Mower Provost named Toyota Faculty Scholar
Her work uses machine learning to measure mood, emotion, and other aspects of human behavior for purposes of providing early or real-time interventions for people in managing their health.
Nine CSE graduate students recognized by NSF Graduate Research Fellowship Program
The nine students represent a broad range of research areas in the department.
Emotion recognition has a privacy problem – here’s how to fix it
Researchers have demonstrated the ability to “unlearn” sensitive identifying data from audio used to train machine learning models.
CSE researchers present 9 papers at leading AI conferenceThe students and faculty submitted projects spanning several key application areas for AI.
Michigan Daily: October 1, 2019
University Professors talk using AI technology for bipolar disorderTwo professors involved in the intersection of artificial intelligence and mental health shared their work Friday evening at the Ann Arbor District Library in partnership with the University of Michigan’s AI Laboratory.
Medium: September 30, 2019
Interspeech 2019 — Machine Learning-enabled Creativity and Innovation In Speech TechCoverage of Interspeech 2019, including Prof. Emily Mower Provost’s research on automatically detecting suicidal ideation from natural phone conversations.
Student awarded NSF Fellowship for automating speech-based disease classification
Perez’s research focuses on analyzing speech patterns of patients with Huntington Disease.
Precision Health Award for measuring moods
The result will be new measurement methods to determine how moods are shaped by both the behavior of an individual and daily interactions over time
The logic of feeling: Teaching computers to identify emotions
A Q&A with machine learning expert Emily Mower Provost.
Detecting Huntington’s disease with an algorithm that analyzes speech
New, preliminary research found automated speech test accurately diagnoses Huntington’s disease 81 percent of the time and tracks the disease’s progression.
Zakaria Aldeneh selected for IBM Ph.D. Fellowship
Aldeneh’s research focuses on identifying the features of speech that make human interaction feel natural.
Emily Mower Provost receives NSF CAREER Award to develop emotion and mood recognition for mental health monitoring and treatment
Prof. Mower Provost’s research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology.
Collecting data to better identify bipolar disorder
Prof. Emily Mower Provost is collaborating to develop new technologies that provide individuals with insight into how the disease changes over time.
U-M, IBM partner on advanced conversational computing system
The project aims to develop a cognitive system that functions as an academic advisor for undergraduate computer science and engineering majors at the university.
Duc Le Selected for Mary A. Rackham Institute Graduate Student Research Assistantship
Duc is interested in using the computer’s modeling power to better understand the inner workings of the human mind, and using this understanding to create more intelligent software programs.
Emily Mower Provost Receives Oscar Stern Award for Research in Emotion Expression and Perception
The proposed work investigates computational methodologies to differentiate emotion perception patterns of healthy controls and individuals with Major Depressive Disorder or Bipolar Disorder.
Over 100 High School Girls Explore Computer Science at Girls Encoded
The attendees were able to learn from hands-on activities, guest speakers, panel discussions, and projects
Yelin Kim wins Best Student Paper Award at ACM Multimedia 2014 for research in facial emotion recognition
She computationally measures, represents, and analyzes human behavior data to illuminate fundamental human behavior and emotion perception, and develop natural human-machine interfaces.