Feb 13 2019 12:00 PM
Feb 13 2019 01:00 PM
The Machine Learning Hub @ KAUST is designed to be the one-stop-shop for machine learning (ML) and artificial intelligence (AI) at KAUST. It is an informal forum for exchanging ideas in these areas, including (but not limited to) theoretical foundations, systems, tools, and applications. It will be providing several offerings to the KAUST community interested in ML and AI, including a regular seminar series where new research in the field is presented, an online social forum dedicated to AI and ML discussions, announcements, brainstorming, collaborations, and hands-on activities (e.g. tutorials/workshops and hackathons) to bolster the growing need for ML and AI education/training on campus. For more details, please visit ml.kaust.edu.sa. This talk will serve as the first installment in the seminar series, during which a more detailed overview of The Hub will be presented. All who are interested in ML and AI on campus are invited to join.
Bernard Ghanem is currently an Associate Professor in the CEMSE division and a theme leader at the Visual Computing Center at KAUST. Before that, he was a Senior Research Scientist at the University of Illinois Urbana-Champaign (UIUC) in Singapore. His research interests lie in computer vision, machine learning, and structured optimization. He received his Bachelor's degree in Computer and Communications Engineering from the American University of Beirut (AUB) in 2005 and his MS/Ph.D. in Electrical and Computer Engineering from UIUC in 2010. He has co-authored more than 80 peer-reviewed conference and journal papers in his field. His work has received several awards and honors, including the two Best Paper Awards (CVPRW 2013 and ECCVW 2018), a two-year KAUST Seed Fund, and a Google Faculty Research Award in 2015.
For more information, contact Prof. Bernard S. Ghanem. Email: Bernard.Ghanem@kaust.edu.sa
Date: Wednesday 13th Feb 2019
Time:12:00 PM - 01:00 PM
Location: Building 9, Lecture Hall 2 Room 2325
Brown bag lunch will be provided at 11:45 a.m.