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Speaker: Fabien Scalzo
Affiliation: UCLA - Computer Science - Neurology

ABSTRACT: Machine learning has demonstrated tremendous promise for solving computer vision tasks related to many aspects of our daily lives. Several medical disciplines that require rapid analysis of complex information could also benefit from machine learning. Thanks to the growing availability of large datasets of multimodal imaging, the field of neurology is ideally positioned to integrate machine learning-based decision support tools. In this talk, I outline the motivation and rationale behind the use of machine learning in neurology and describe several computational models we have developed over the last few years that provide enhanced diagnostic and decision support. BIO: Dr. Fabien Scalzo is an Assistant Professor at UCLA in Neurology, Computer Science, and Electrical and Computer Engineering. Trained in machine learning during his PhD, he specializes in building computational models for neuroimaging and neurocritical care. He directs the Artificial Intelligence for Medical Imaging and Brain Studies (AI-MIBS) lab where his team develops advanced decision support tools related to brain imaging; including outcome prediction in stoke, diagnosis of traumatic brain injuries, and computational fluid dynamics of blood flow. He is the author of over 150 publications and is a recipient of the BAEF, Spitzer, NSF China Young scholar awards, and five best paper/poster awards. He has been a co-investigator on several grants funded by NIH, NSF, DARPA, and AHA.

Date(s) - Mar 15, 2018
4:15 pm - 5:45 pm


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