Agenda

Speaker: Alex Bronstein, Technion

Abstract:
Recent spectacular advances in machine learning techniques allow solving complex computer vision tasks -- all the way down to vision-based decision making. However, the input image itself is still produced by imaging systems that were built to produce human-intelligible pictures that are not necessarily optimal for the end task. In this talk, we will try to entertain ourselves with the idea of including the camera hardware among the learnable degrees of freedom. I will show examples from optical, acoustic, magnetic resonance, and radar imaging demonstrating that simultaneously learning the "software" and the "hardware" parts of an imaging system is beneficial for the end task.

Bio Sketch:
Alex Bronstein received an M.Sc. from the Department of Electrical Engineering and a Ph.D. from the Department of Computer Science at the Technion. He is currently a professor at the Department of Computer Science at the Technion holding the Schmidt Chair in Artificial Intelligence and the Dan Broida Academic Chair and heading the Center for Intelligent Systems. He also served as the Vice Dean for Industry Liaisons. Alex’s main research interests are theoretical and computational methods in geometric data analysis and their application to a broad range of problems in imaging, machine vision, graphics, and learning. Alex is a Fellow of the IEEE and ELLIS and the recipient of four grants from the European Research Council (ERC). In addition to his academic activities, he is a technological entrepreneur and investor. He co-founded and served in leading technical roles in various startup companies including the hardware startup Invision which was acquired by Intel and became the basis for Intel’s RealSense depth cameras technology, the media startup Videocites offering Internet-scale B2B video indexing and tracking, the biomedical startup Embryonics building AI products for the fertility market, and the fintech startup Sibylla.