Learning Complex Markers from Large-Scale Behavioral Data
Tuesday, June 27, 2017
Keller Hall, Room 4-178A*
200 Union Street SE
University of Minnesota, Minneapolis
*Enter through the main ECE Department Office - 4-174 Keller Hall
About the presentation
Assistant Professor Zhao develops computational and experimental methods to predict human behaviors and diagnose people with neuropsychiatric disorders. In this talk, Zhao will discuss her recent innovations on data and models. As an example, she will elaborate findings that decipher the neurobehavioral signature of autism. She will then demonstrate deep learning models that are able to learn semantic attributes from complex natural scenes, leading to breakthrough performance in attention prediction and identifying people with autism.
About the speaker
Catherine Qi Zhao received her Ph.D. in computer engineering from the University of California, Santa Cruz in 2009. Previously, she was a postdoctoral researcher in the Computation & Neural Systems, and Division of Biology at the California Institute of Technology from 2009 to 2011. Prior to joining the University of Minnesota in 2016, she was an assistant professor in the Electrical and Computer Engineering Department and the Ophthalmology Department at the National University of Singapore, and the principal investigator at the Visual Information Processing Lab. Her main research interests include computational vision, machine learning, cognitive neuroscience, and mental disorders.
CS&E or ECE Alumni are welcome to register here.