BME Seminar Series: Defining and understanding complex markers from large-scale behavioral data of clinical populations
Qi Zhao, Department of Electrical and Computer Engineering Department of Ophthalmology, National University of Singapore
Tuesday, February 16, 2016
River Campus | Robert B. Goergen Hall | Sloan Auditorium (Room 101)
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.