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:20210917T232654Z
UID:AF22701E-D152-4880-84E2-BE7E0A22A8E9
DTSTART;TZID=America/Chicago:20210917T110000
DTEND;TZID=America/Chicago:20210917T121500
DESCRIPTION:IEEE Signal Processing Society Chicago Chapter Distinguished Le
 cture Series\n\nUIC ECE 595 Seminar Series\n\nSpeaker: Prof. Qing Zhao\, J
 oseph C. Ford Professor of Engineering\, School of Electrical and Computer
  Engineering\, Cornell University\n\nDate: Friday\, September 17\, 2021\n\
 nTime: 11:00 am – 12:15 pm\n\nZoom link (sign-in required): https://uic.
 zoom.us/j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNUTXBTZz09\n\nTitle: Rando
 m Walk on a Tree for Stochastic Optimization and Learning\n\nAbstract: The
  problem of searching for a few rare events of interest among a massive nu
 mber of possibilities is ubiquitous. The rare events may represent opportu
 nities with exceptional returns\, extremely useful information in a deluge
  of data\, or anomalies with potentially catastrophic consequences. The ke
 y challenges are that the search space is massive\, observations are noisy
  and costly\, and stochastic models of the rare events are unknown. Exampl
 e applications include identifying infected individuals in a large populat
 ion\, detecting intrusions and attacks in large communication/computer net
 works\, and the general problem of stochastic optimization for finding the
  optimal point of an unknown objective function in a high-dimensional spac
 e. We discuss in this talk a solution framework and its optimality in term
 s of learning efficiency. The key idea of the approach is to devise a bias
 ed random walk on a tree-based hierarchical representation of the search s
 pace. This is a joint work with Sudeep Salgia and Sattar Vakili.\n\nBio: Q
 ing Zhao joined Cornell University in 2015\, where she is the Joseph C. Fo
 rd Professor of Engineering. Prior to that\, she was a professor with the 
 ECE Department at University of California\, Davis. She received the Ph.D.
  degree in electrical engineering from Cornell University in 2001. Profess
 or Zhao is a Fellow of IEEE\, a Marie Skłodowska-Curie Fellow of the Euro
 pean Union research and innovation program\, a Jubilee Chair Professor of 
 Chalmers University during her 2018-2019 sabbatical leave\, and a Distingu
 ished Lecturer of the IEEE Signal Processing Society. She was the recipien
 t of the 2010 IEEE Signal Processing Magazine Best Paper Award and the 200
 0 Young Author Best Paper Award from IEEE Signal Processing Society. Her r
 esearch interests include sequential decision theory\, stochastic optimiza
 tion\, machine learning\, and algorithmic theory with applications in infr
 astructure\, communications\, and social-economic networks.\n\nHost: Prof.
  Mojtaba Soltanalian\n\nChapter Chair: Prof. Mojtaba Soltanalian msol@uic.
 edu\n\nChapter Vice Chair: Dr. Ouday Hanosh mohanos2@uic.edu\n\nSpeaker(s)
 : Prof. Qing Zhao\, \n\nVirtual: https://events.vtools.ieee.org/m/281637
LOCATION:Virtual: https://events.vtools.ieee.org/m/281637
ORGANIZER:msol@uic.edu
SEQUENCE:2
SUMMARY:IEEE SPS Chicago Chapter Distinguished Lecture by Prof. Qing Zhao
URL;VALUE=URI:https://events.vtools.ieee.org/m/281637
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;&lt;u&gt;IEEE Signal Processing Society 
 Chicago Chapter Distinguished Lecture Series&lt;/u&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;
 &lt;u&gt;UIC ECE 595 Seminar Series&lt;/u&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;h1&gt;&lt;stron
 g&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;Speaker: Prof. Qing Zhao\, Joseph C. For
 d Professor of Engineering\, School of Electrical and Computer Engineering
 \, Cornell University &lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;\n&lt;h1&gt;&lt;strong&gt;&lt;span style=&quot;font
 -size: 14pt\;&quot;&gt;Date: Friday\, September 17\, 2021&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;\n&lt;p
 &gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;Time: 11:00 am &amp;ndash\; 12:15 pm&lt;
 /span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;h1&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;Zoom link
  (sign-in required): &lt;/span&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14pt\
 ;&quot;&gt;&lt;a href=&quot;https://uic.zoom.us/j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNU
 TXBTZz09&quot;&gt;https://uic.zoom.us/j/84613681029?pwd=RlhnWnJib08zVE12cTBpTHNUTX
 BTZz09&lt;/a&gt;&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;span style=&quot;f
 ont-size: 14pt\;&quot;&gt;&lt;u&gt;Title:&lt;/u&gt; Random Walk on a Tree for Stochastic Optim
 ization and Learning&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br /&gt;&lt;strong&gt;&lt;u&gt;Abstract:&lt;/u
 &gt;&lt;/strong&gt; The problem of searching for a few rare events of interest amon
 g a massive number of possibilities is ubiquitous. The rare events may rep
 resent opportunities with exceptional returns\, extremely useful informati
 on in a deluge of data\, or anomalies with potentially catastrophic conseq
 uences. The key challenges are that the search space is massive\, observat
 ions are noisy and costly\, and stochastic models of the rare events are u
 nknown. Example applications include identifying infected individuals in a
  large population\, detecting intrusions and attacks in large communicatio
 n/computer networks\, and the general problem of stochastic optimization f
 or finding the optimal point of an unknown objective function in a high-di
 mensional space. We discuss in this talk a solution framework and its opti
 mality in terms of learning efficiency. The key idea of the approach is to
  devise a biased random walk on a tree-based hierarchical representation o
 f the search space. This is a joint work with Sudeep Salgia and Sattar Vak
 ili.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;u&gt;Bio:&lt;/u&gt;&lt;/strong&gt;&amp;nbsp\;Qing&amp;nbsp\
 ;Zhao joined Cornell University in 2015\, where she is the Joseph C. Ford 
 Professor of Engineering. Prior to that\, she was a professor with the ECE
  Department at&amp;nbsp\;University of California\,&amp;nbsp\;Davis. She received 
 the Ph.D. degree in electrical engineering from Cornell University in 2001
 . Professor Zhao is a Fellow of IEEE\, a Marie Skłodowska-Curie Fellow of
  the European Union research and innovation program\, a Jubilee Chair Prof
 essor of Chalmers University during her 2018-2019 sabbatical leave\, and a
 &amp;nbsp\;Distinguished&amp;nbsp\;Lecturer of the IEEE Signal Processing Society.
 &amp;nbsp\;She was the recipient of the 2010 IEEE Signal Processing Magazine B
 est Paper Award and the 2000 Young Author Best&amp;nbsp\;Paper Award from IEEE
  Signal Processing Society.&amp;nbsp\;Her research interests include sequentia
 l decision theory\, stochastic&amp;nbsp\;optimization\, machine learning\, and
  algorithmic theory with applications in&amp;nbsp\;infrastructure\, communicat
 ions\, and social-economic networks.&amp;nbsp\;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&amp;nbsp\;
 &lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Host: &lt;/strong&gt;Prof. Mojtaba Soltanalian&lt;/p&gt;\n&lt;p
 &gt;&lt;strong&gt;Chapter Chair:&lt;/strong&gt; Prof. Mojtaba Soltanalian &lt;a href=&quot;mailto
 :msol@uic.edu&quot;&gt;msol@uic.edu&lt;/a&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Chapter Vice Chair:&lt;/stron
 g&gt; Dr. Ouday Hanosh &lt;a href=&quot;mailto:mohanos2@uic.edu&quot;&gt;mohanos2@uic.edu&lt;/a&gt;
 &lt;/p&gt;
END:VEVENT
END:VCALENDAR