Tuesday, February 16, 2016
8:00 AM
Goergen Room 101
Dr. Qi Zhai
National University of Singapore (NUS)
Defining and understanding complex markers from large-scale behavioral data of clinical populations



We develop computational and experimental methods to gain insights into visual functions and neuropsychiatric disorders. We also build deep learning models that predict human behaviors.

In this talk, I will share our recent innovations to record large-scale attention data and to identify complex markers. I will first introduce our new approach to characterize complex stimuli with rich semantics. It allows quantifying behavioral differences of multiple clinical groups. As an example, I will elaborate findings that use data and models to decipher the neurobehavioral signature of autism. I will then demonstrate an innovative psychophysical method to enable large-scale collection of attention data. I will also present our deep learning attention model that makes a big leap towards human performance. Live demos will be shown to illustrate our findings and results.

Overall the integrated computational and experimental approach offers new opportunities for neuroscience research, as well as clinical and machine applications. I will conclude by discussing future works in these domains.


Qi Zhao is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Ophthalmology at the National University of Singapore (NUS). She is the principal investigator at the Visual Information Processing Lab (http://www.ece.nus.edu.sg/stfpage/eleqiz). Qi received the MSc and PhD degrees in computational vision from the University of California, Santa Cruz, in 2007 and 2009, respectively. Prior to joining NUS, she was a postdoctoral researcher in the Computation & Neural Systems, and Division of Biology at the California Institute of Technology from 2009 to 2011. Her main research interests include computational and cognitive vision, machine learning, big data analytics, and mental disorders. Qi has published more than 30 journal and conference papers in top computer vision, cognitive neuroscience, and machine learning venues. She edited a book with Springer, titled Computational and Cognitive Neuroscience of Vision, which provides a systematic and comprehensive overview of vision from various perspectives, ranging from neuroscience to cognition, and from computational principles to engineering developments.