Yu (Andy) Huang, "Computational Models of Transcranial Electrical Stimulation: Methodology, Optimization and Validations"

Dates
Wed, Feb 05, 2020 - 03:00 PM — Wed, Feb 05, 2020 - 04:00 PM
Admission Fee
Free
Event Address
Grove School of Engineering
275 Convent Ave, New York, NY 10031
Event Location
Steinman Hall 402
Event Details

Event details: Biomedical Engineering Seminar, Yu (Andy) Huang Ph.D. Research Fellow Radiology Department, Memorial Sloan Kettering Cancer Center, Research Associate CCNY-MSK AI Partnership will give a talk on “Computational Models of Transcranial Electrical Stimulation: Methodology, Optimization and Validations"

ABSTRACT: Transcranial electrical stimulation (TES) has been shown as a promising neurological therapy for a number of diseases. Nowadays, design of electrode montages and interpretation of experimental results for TES heavily rely on computational models, which predict the current-flow distribution inside the head. In this talk I will show you methodological details in building individualized TES models from structural magnetic resonance images of human heads, including image segmentation, electrode placement, finite element modeling, and numerical optimization for targeted stimulation. Model validations using intracranial in vivo recordings will also be discussed. I will also briefly talk about translational efforts that converts TES models into neuromodulation software, either open-source or proprietary, that are used for clinical research on stroke recovery. BIO: Dr. Huang received his B.S. in 2007 and M.S. in 2010 from University of Electronic Science and Technology of China, both in Biomedical Engineering. He received his Ph.D from The City College of New York (CCNY) in 2017 with research focusing on computational modeling for transcranial electrical stimulation. After that he was working as a joint post-doc in Soterix Medical Inc. and Parra Lab at CCNY. He recently joined Memorial Sloan Kettering Cancer Center for his 2nd post-doc working on cancer detection from medical images using deep neural networks.

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