Machine Learning and Computer Vision for Neurodevelopmental Disorders: Helping One Child at a Time

27 Sep
Electrical Engineering Seminar Series
Guillermo Sapiro, Duke University
Friday, September 27, 2019 - 3:00pm to 4:00pm
Maxwell Dworkin G125

Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in neurodevelopmental disorders, including autism spectrum disorder, the core of this talk. Such behavioral ratings are subjective, require significant clinician expertise and training, typically do not capture data from the children in their natural environments such as homes or schools, and are not scalable for large population screening, low-income communities, or longitudinal monitoring, all of which are critical for outcome evaluation in multisite studies and for understanding and evaluating symptoms in the general population. The development of computational approaches to standardized objective behavioral assessment is, thus, a significant unmet need in autism spectrum disorder in particular and developmental and neurodegenerative disorders in general. Here, we discuss how computer vision and machine learning can develop scalable low-cost mobile health methods for automatically and consistently assessing existing biomarkers, from eye tracking to movement patterns and affect, while also providing tools and big data for novel discovery. We will present results from our multiple clinical studies, where we have already collected the largest available data in the field, as well as the challenges of the discipline. The work presented here is in collaboration with Geri Dawson, Kim Carpenter, Jordan Hashemi, Zhuoqing Cheng, Dmitry Isaev, Matthieu Bovery, Steven Espinosa, Kathleen Campbell, Elena Tenenbaum, and others in this interdisciplinary team of MDs, therapists, engineers, developers, advocates, and most of all, children participants.

Speaker Bio: 

Guillermo Sapiro was born in Montevideo, Uruguay, on April 3, 1966. He received his B.Sc. (summa cum laude), M.Sc., and Ph.D. from the Department of Electrical Engineering at the Technion, Israel Institute of Technology, in 1989, 1991, and 1993 respectively. After post-doctoral research at MIT, Dr. Sapiro became Member of Technical Staff at the research facilities of HP Labs in Palo Alto, California. He was with the Department of Electrical and Computer Engineering at the University of Minnesota, where he held the position of Distinguished McKnight University Professor and Vincentine Hermes-Luh Chair in Electrical and Computer Engineering. Currently he is a James B. Duke School Professor with Duke University.

G. Sapiro works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. He has authored and co-authored over 450 papers in these areas and has written a book published by Cambridge University Press, January 2001.

G. Sapiro was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991, the  Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992, the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, the National Science Foundation Career Award in 1999, and the National Security Science and Engineering Faculty Fellowship in 2010.  He received the test of time award at ICCV 2011 and at ICML 2019.  He was elected to the American academy of Arts and Sciences in 2018.

G. Sapiro is a Fellow of IEEE, SIAM, and the American Academy of Arts and Sciences (AAAS).  G. Sapiro was the founding Editor-in-Chief of the SIAM Journal on Imaging Sciences.

Demba Ba
Gioia Sweetland