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BEGIN:VEVENT
DTSTAMP:20230222T210149Z
UID:AB93CE30-A6EF-485E-84AF-58FC685F194B
DTSTART;TZID=America/Los_Angeles:20230221T173000
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DESCRIPTION:Zoom link: https://us02web.zoom.us/j/84699061961?pwd=aTN4Y3dPMW
 95a0I0dU93dk15WlArUT09\n\nMeeting ID: 846 9906 1961\n\nPasscode: 915275\n\
 nThe safety\, integrity\, and well-being of users\, communities\, and plat
 forms on the web and social media is a critical\, yet challenging task. In
  this talk\, I will describe the AI and machine learning methods\, advanci
 ng natural language processing\, graph machine learning\, and adversarial 
 machine learning\, that my group has developed to efficiently fight malici
 ous users and bad content online. I will talk about the four main pillars 
 of my research: 1) Detection: developing multi-lingual\, multi-modal\, and
  multi-platform detection models\; 2) Robustness: developing adversarially
  robust detection models\; 3) Attribution: quantifying harms and impact of
  bad actors\; 4) Mitigation: developing solutions and tools to mitigate on
 line harms.\n\nCo-sponsored by: CH06325 - San Diego Section\, Information 
 Theory Society\, Computational Intelligence Society\n\nSpeaker(s): Srijan 
 Kumar\, Ph.D.\, \n\nVirtual: https://events.vtools.ieee.org/m/347144
LOCATION:Virtual: https://events.vtools.ieee.org/m/347144
ORGANIZER:upalmahbub@yahoo.com
SEQUENCE:8
SUMMARY:Advances in AI for Web Integrity\, Equity and Well-Being
URL;VALUE=URI:https://events.vtools.ieee.org/m/347144
X-ALT-DESC:Description: <br /><p>Zoom link: https://us02web.zoom.us/j/84699
 061961?pwd=aTN4Y3dPMW95a0I0dU93dk15WlArUT09</p>\n<p>Meeting ID: 846 9906 1
 961</p>\n<div dir="ltr">\n<div>\n<div>\n<div id="yiv8933945672">\n<div>\n<
 div class="yiv8933945672WordSection1">\n<div id="yiv8933945672yqt26051" cl
 ass="yiv8933945672yqt1016944228">\n<div>\n<div>\n<p class="yiv8933945672Ms
 oNormal">Passcode: 915275</p>\n</div>\n</div>\n</div>\n</div>\n</div>\n</d
 iv>\n</div>\n</div>\n</div>\n<p>The safety\,&nbsp\;integrity\, and well-be
 ing of users\, communities\, and platforms on the web and social media is 
 a critical\, yet challenging task.&nbsp\;In&nbsp\;this&nbsp\;talk\, I will
  describe the AI and machine learning methods\, advancing natural language
  processing\,&nbsp\;graph machine learning\, and adversarial machine learn
 ing\, that my group has developed to efficiently fight malicious users and
  bad content online. I will talk about the four main pillars of my researc
 h: 1) Detection: developing multi-lingual\, multi-modal\, and multi-platfo
 rm detection models\; 2) Robustness: developing adversarially robust detec
 tion models\; 3) Attribution: quantifying harms and impact of bad actors\;
  4) Mitigation: developing solutions and tools to mitigate online harms.&n
 bsp\;</p>
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