BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20200308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20201101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20200729T145251Z UID:71A2E807-0112-4A42-8AFC-4BF198F53412 DTSTART;TZID=US/Eastern:20200625T170000 DTEND;TZID=US/Eastern:20200625T180000 DESCRIPTION:Webex meeting. Please join us for a presentation by Dr. Sheng L i.\n\nTalk Abstract: Causal inference is a critical research topic across many domains\, such as statistics\, education\, political science and econ omics\, for decades. Estimating causal effect from observational data has become an appealing research direction owing to the large amount of availa ble data and low budget requirement\, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area\, variou s causal effect estimation methods for observational data have sprung up. In this talk\, I will introduce how machine learning helps infer causality from observational data\, and introduce classical causal inference method s as well as the recent deep learning based causal inference methods. Doma in applications and future research directions will also be discussed.\n\n Bio: Dr. Sheng Li is an Assistant Professor of Computer Science at the Uni versity of Georgia since 2018. He was a Research Scientist at Adobe Resear ch from 2017 to 2018. Prior to that\, he obtained the Ph.D. degree in comp uter engineering from Northeastern University in 2017. Dr. Li's research i nterests include graph based machine learning\, deep learning\, user behav ior modeling\, visual intelligence\, and causal inference. He has publishe d over 90 papers at peer-reviewed conferences and journals\, and has recei ved over 10 research awards\, such as the Adobe Data Science Research Awar d\, Baidu Research Fellowship\, and SDM Best Paper Award. He serves as Ass ociate Editor of five international journals including IEEE Computational Intelligence Magazine and Neurocomputing. He has also served as senior pro gram committee member for AAAI\, and program committee member for NeurIPS\ , ICML\, KDD\, IJCAI\, ICCV\, CVPR and ICLR. He is a senior member of IEEE .\n\nSpeaker(s): Dr. Sheng Li\, \n\nAgenda: \n5:00 pm -- Webex meeting sta rts\n\n6:00 pm -- Meeting ends\n\nWebex link for meeting: https://tinyurl. com/ybob6fxt\, Meeting password: IEEE-ATL-CS-2020\, Atlanta\, Georgia\, Un ited States LOCATION:Webex link for meeting: https://tinyurl.com/ybob6fxt\, Meeting pas sword: IEEE-ATL-CS-2020\, Atlanta\, Georgia\, United States ORGANIZER:barry.drake@gtri.gatech.edu SEQUENCE:29 SUMMARY:Machine Learning Meets Causal Inference URL;VALUE=URI:https://events.vtools.ieee.org/m/209740 X-ALT-DESC:Description: <br /><div style="caret-color: #000000\; color: #00 0000\; font-family: Calibri\, Helvetica\, sans-serif\, EmojiFont\, 'Apple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI Symbol'\, 'An droid Emoji'\, EmojiSymbols\; font-size: 13.333333015441895px\; font-style : normal\; font-variant-caps: normal\; font-weight: normal\; letter-spacin g: normal\; orphans: auto\; text-align: start\; text-indent: 0px\; text-tr ansform: none\; white-space: normal\; widows: auto\; word-spacing: 0px\; - webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0px\; text-deco ration: none\;"><strong>Webex meeting. Please join us for a presentation b y Dr. Sheng Li. <br /></strong></div>\n<div style="caret-color: #000000\; color: #000000\; font-family: Calibri\, Helvetica\, sans-serif\, EmojiFont \, 'Apple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI Sym bol'\, 'Android Emoji'\, EmojiSymbols\; font-size: 13.333333015441895px\; font-style: normal\; font-variant-caps: normal\; font-weight: normal\; let ter-spacing: normal\; orphans: auto\; text-align: start\; text-indent: 0px \; text-transform: none\; white-space: normal\; widows: auto\; word-spacin g: 0px\; -webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0px\; text-decoration: none\;">&nbsp\;</div>\n<div style="caret-color: #000000\ ; color: #000000\; font-family: Calibri\, Helvetica\, sans-serif\, EmojiFo nt\, 'Apple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI S ymbol'\, 'Android Emoji'\, EmojiSymbols\; font-size: 13.333333015441895px\ ; font-style: normal\; font-variant-caps: normal\; font-weight: normal\; l etter-spacing: normal\; orphans: auto\; text-align: start\; text-indent: 0 px\; text-transform: none\; white-space: normal\; widows: auto\; word-spac ing: 0px\; -webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0px \; text-decoration: none\;"><strong>Talk Abstract</strong>: Causal inferen ce is a critical research topic across many domains\, such as statistics\, education\, political science and economics\, for decades. Estimating cau sal effect from observational data has become an appealing research direct ion owing to the large amount of available data and low budget requirement \, compared with randomized controlled trials. Embraced with the rapidly d eveloped machine learning area\, various causal effect estimation methods for observational data have sprung up. In this talk\, I will introduce how machine learning helps infer causality from observational data\, and intr oduce classical causal inference methods as well as the recent deep learni ng based causal inference methods. Domain applications and future research directions will also be discussed.</div>\n<div style="caret-color: #00000 0\; color: #000000\; font-family: Calibri\, Helvetica\, sans-serif\, Emoji Font\, 'Apple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI Symbol'\, 'Android Emoji'\, EmojiSymbols\; font-size: 13.333333015441895p x\; font-style: normal\; font-variant-caps: normal\; font-weight: normal\; letter-spacing: normal\; orphans: auto\; text-align: start\; text-indent: 0px\; text-transform: none\; white-space: normal\; widows: auto\; word-sp acing: 0px\; -webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0 px\; text-decoration: none\;">&nbsp\;</div>\n<div style="caret-color: #000 000\; color: #000000\; font-family: Calibri\, Helvetica\, sans-serif\, Emo jiFont\, 'Apple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI Symbol'\, 'Android Emoji'\, EmojiSymbols\; font-size: 13.33333301544189 5px\; font-style: normal\; font-variant-caps: normal\; font-weight: normal \; letter-spacing: normal\; orphans: auto\; text-align: start\; text-inden t: 0px\; text-transform: none\; white-space: normal\; widows: auto\; word- spacing: 0px\; -webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0px\; text-decoration: none\;">\n<div style="caret-color: #000000\; color : #000000\; font-family: Calibri\, Helvetica\, sans-serif\, EmojiFont\, 'A pple Color Emoji'\, 'Segoe UI Emoji'\, NotoColorEmoji\, 'Segoe UI Symbol'\ , 'Android Emoji'\, EmojiSymbols\; font-size: 13.333333015441895px\; font- style: normal\; font-variant-caps: normal\; font-weight: normal\; letter-s pacing: normal\; orphans: auto\; text-align: start\; text-indent: 0px\; te xt-transform: none\; white-space: normal\; widows: auto\; word-spacing: 0p x\; -webkit-text-size-adjust: auto\; -webkit-text-stroke-width: 0px\; text -decoration: none\;"><strong>Bio</strong>: Dr. Sheng Li is an Assistant Pr ofessor of Computer Science at the University of Georgia since 2018. He wa s a Research Scientist at Adobe Research from 2017 to 2018. Prior to that\ , he obtained the Ph.D. degree in computer engineering from Northeastern U niversity in 2017. Dr. Li's research interests include graph based machine learning\, deep learning\, user behavior modeling\, visual intelligence\, and causal inference. He has published over 90 papers at peer-reviewed co nferences and journals\, and has received over 10 research awards\, such a s the Adobe Data Science Research Award\, Baidu Research Fellowship\, and SDM Best Paper Award. He serves as Associate Editor of five international journals including IEEE Computational Intelligence Magazine and Neurocompu ting. He has also served as senior program committee member for AAAI\, and program committee member for NeurIPS\, ICML\, KDD\, IJCAI\, ICCV\, CVPR a nd ICLR. He is a senior member of IEEE.</div>\n</div><br /><br />Agenda: < br /><p>5:00 pm -- Webex meeting starts</p>\n<p>6:00 pm -- Meeting ends</p > END:VEVENT END:VCALENDAR