BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20211102T155743Z UID:33E7CD47-40CD-4156-9A46-917871285949 DTSTART;TZID=America/Chicago:20211029T110000 DTEND;TZID=America/Chicago:20211029T121500 DESCRIPTION:UIC ECE 595 Seminar Series\n\nIEEE Signal Processing Society Ch icago Chapter Distinguished Lecture Series\n\nSpeaker: Prof. Ami Wiesel\, Rachel and Selim Benin School of Computer Science and Engineering\, Hebrew University of Jerusalem\n\nDate: Friday\, October 29\, 2021\n\nTime: 11:0 0 am – 12:15 pm\n\nZoom link (sign-in required):\n\nhttps://uic.zoom.us/ j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNUTXBTZz09\n\nTitle: Machine Learn ing for Statistical Signal Processing\n\nAbstract: In this talk\, I will d iscuss our recent works on using modern machine learning ideas in classica l statistical signal processing problems. We will present three settings i n which the classical solutions are computationally intractable and will p ropose alternative approaches based on learning. The first topic is deep l earning for maximum likelihood detection. Then\, we'll switch to maximum l ikelihood estimation via deep learning and implicit generative models. Joi nt works with Neev Samuel\, Tzvi Diskin\, Itai Alon\, Amir Globerson\, and Yonina Eldar.\n\nBio: Ami Wiesel received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University\, Tel-Aviv\, Israel\, in 2 000 and 2002\, respectively\, and the Ph.D. degree in electrical engineeri ng from the Technion - Israel Institute of Technology\, Haifa\, Israel\, i n 2007. He was a postdoctoral fellow with the Department of Electrical Eng ineering and Computer Science\, University of Michigan\, Ann Arbor\, USA\, during 2007–2009. He is currently an Associate Professor in the Rachel and Selim Benin School of Computer Science and Engineering\, Hebrew Univer sity of Jerusalem\, Israel. Since 2018\, he is also a Visiting Researcher at Google.\n\nHost: Prof. Mojtaba Soltanalian\n\nChapter Chair: Prof. Mojt aba Soltanalian msol@uic.edu\n\nChapter Vice Chair: Dr. Ouday Hanosh mohan os2@uic.edu\n\nSpeaker(s): Prof. Ami Wiesel\, \n\nChicago\, Illinois\, Uni ted States\, Virtual: https://events.vtools.ieee.org/m/286034 LOCATION:Chicago\, Illinois\, United States\, Virtual: https://events.vtool s.ieee.org/m/286034 ORGANIZER:msol@uic.edu SEQUENCE:3 SUMMARY:IEEE SPS Chicago Chapter Distinguished Lecture by Prof. Ami Wiesel URL;VALUE=URI:https://events.vtools.ieee.org/m/286034 X-ALT-DESC:Description: <br /><p><strong><u>UIC ECE 595 Seminar Series</u>< /strong></p>\n<p><strong><u>IEEE Signal Processing Society Chicago Chapter Distinguished Lecture Series</u></strong></p>\n<h1><span style="font-size : 14pt\;"><strong>Speaker: </strong>Prof. Ami Wiesel\, Rachel and Selim Be nin School of Computer Science and Engineering\, Hebrew University of Jeru salem</span></h1>\n<h1><span style="font-size: 14pt\;"><strong>Date: </str ong>Friday\, October 29\, 2021</span></h1>\n<p><span style="font-size: 14p t\;"><strong>Time: </strong>11:00 am &ndash\; 12:15 pm</span></p>\n<h1><sp an style="font-size: 14pt\;"><strong>Zoom link</strong> (<strong>sign-in r equired): </strong></span></h1>\n<h1><span style="font-size: 14pt\;"><a hr ef="https://uic.zoom.us/j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNUTXBTZz09 ">https://uic.zoom.us/j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNUTXBTZz09</ a></span></h1>\n<p>&nbsp\;</p>\n<p><strong><u>Title:</u></strong> Machine Learning for Statistical Signal Processing</p>\n<p><br /><strong><u>Abstra ct:</u></strong> In this talk\, I will discuss our recent works on using m odern machine learning ideas in classical statistical signal processing pr oblems. We will present three settings in which the classical solutions ar e computationally intractable and will propose alternative approaches base d on learning. The first topic is deep learning for maximum likelihood det ection. Then\, we'll switch to maximum likelihood estimation via deep lear ning and implicit generative models. Joint works with Neev Samuel\, Tzvi D iskin\, Itai Alon\, Amir Globerson\, and Yonina Eldar.</p>\n<p>&nbsp\;</p> \n<p><strong><u>Bio:</u></strong>&nbsp\;Ami Wiesel received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University\, Tel-Avi v\, Israel\, in 2000 and 2002\, respectively\, and the Ph.D. degree in ele ctrical engineering from the Technion - Israel Institute of Technology\, H aifa\, Israel\, in 2007. He was a postdoctoral fellow with the Department of Electrical Engineering and Computer Science\, University of Michigan\, Ann Arbor\, USA\, during 2007&ndash\;2009. He is currently an Associate Pr ofessor in the Rachel and Selim Benin School of Computer Science and Engin eering\, Hebrew University of Jerusalem\, Israel. Since 2018\, he is also a Visiting Researcher at Google.</p>\n<p><strong>&nbsp\;</strong></p>\n<p> <strong>Host: </strong>Prof. Mojtaba Soltanalian</p>\n<p><strong>Chapter C hair:</strong> Prof. Mojtaba Soltanalian <a href="mailto:msol@uic.edu">mso l@uic.edu</a></p>\n<p><strong>Chapter Vice Chair:</strong> Dr. Ouday Hanos h <a href="mailto:mohanos2@uic.edu">mohanos2@uic.edu</a></p> END:VEVENT END:VCALENDAR