אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

A coreset (or core-set) of a dataset is its semantic compression with respect to a set of classifiers, such that learning the (small) coreset provably yields an approximate classifier of the original (full) dataset. Composable coresets also allow handling streaming and distributed data, e.g. using a cloud or swarm of drones. In this talk I will forge a link between coresets for fundamentals problems in machine (active and deep) learning -- to problems in real-time robotics. Finally, we will see videos of our coreset-based real-time system of autonomous legal, safe and low-cost drones in the supermarket of Rami Levy at Nesher. This is a joint work with Murad Tukan, Elad Tolichensky and Ibrahim Jubran Bio: Dan is a faculty member and the head of the Robotics & Big Data (RBD) Labs in the University of Haifa, after returning from a 3 years post-doc at the Distributed Robotics Lab of MIT, and a previous post-doc the Center for the Mathematics of Information at Caltech. He is known as one of the leading world-wide coresets researchers, mainly for machine learning of Big Data and AI from sensors. In particular, combining computational geometry with applied multidisciplinary science and engineering. This is the main reason of the grants from both government (e.g. NSF-BSF, GIF, Prime Minister Office, Israel Innovation Authority), and industry (e.g. Samsung, Foxconn, Intel, Refael, Ping-An). The first core-sets were developed for open mathematical problems during his P.hD in the University of Tel-Aviv under the supervision of Prof. Micha Sharir and Prof. Amos Fiat. Now his RBD Labs consist of > 20 graduate students. See lab’s web-site for more details: https://sites.hevra.haifa.ac.il/rbd/