{"618856":{"#nid":"618856","#data":{"type":"event","title":"ISyE Statistic Seminar - Chao Zhang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMultidimensional Text Mining with Limited Supervision\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EUnstructured text, as one of the most important data forms, plays a crucial role in domains such as cybersecurity, healthcare informatics, and\u0026nbsp;cyber-physical systems. In many emerging applications, people\u0026#39;s information\u0026nbsp;need from text data is becoming multidimensional---they demand useful insights\u0026nbsp;along multiple aspects from the given text corpus. However, acquiring\u0026nbsp;multidimensional knowledge from massive text data challenges existing data\u0026nbsp;mining techniques. In this talk, I will present a structuring-and-mining\u0026nbsp;framework for facilitating acquiring multidimensional knowledge from text data.\u0026nbsp;It organizes unstructured text into a multidimensional and multi-granular\u0026nbsp;structure, from which end users can easily select relevant data with\u0026nbsp;declarative queries and apply any data mining primitives thereafter. I will\u0026nbsp;detail two core algorithms in this framework, including (1) a weakly supervised\u0026nbsp;text classification algorithm; and (2) an abnormal event detection algorithm.\u0026nbsp;The algorithms in the framework all require little supervision and are thus\u0026nbsp;particularly appealing in scenarios where labeled data are expensive to\u0026nbsp;acquire.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EChao Zhang is an Assistant Professor at College of Computing, Georgia Institute\u0026nbsp;of Technology. His research area is data mining and machine learning. He is\u0026nbsp;particularly interested in developing label-efficient and robust learning\u0026nbsp;techniques, with applications in text mining and spatiotemporal data mining.\u0026nbsp;Chao has published more than 40 papers in top-tier conferences and journals,\u0026nbsp;such as KDD, WWW, SIGIR, VLDB, and TKDE.\u0026nbsp; He is the recipient of the ECML\/PKDD\u0026nbsp;Best Student Paper Runner-up Award (2015) and the Chiang Chen Overseas Graduate\u0026nbsp;Fellowship (2013). Before joining Georgia Tech, he obtained his Ph.D. degree in\u0026nbsp;Computer Science from University of Illinois at Urbana-Champaign in 2018.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EUnstructured text, as one of the most important data forms, plays a crucial role in domains such as cybersecurity, healthcare informatics, and\u0026nbsp;cyber-physical systems. In many emerging applications, people\u0026#39;s information\u0026nbsp;need from text data is becoming multidimensional---they demand useful insights\u0026nbsp;along multiple aspects from the given text corpus. However, acquiring\u0026nbsp;multidimensional knowledge from massive text data challenges existing data\u0026nbsp;mining techniques. In this talk, I will present a structuring-and-mining\u0026nbsp;framework for facilitating acquiring multidimensional knowledge from text data.\u0026nbsp;It organizes unstructured text into a multidimensional and multi-granular\u0026nbsp;structure, from which end users can easily select relevant data with\u0026nbsp;declarative queries and apply any data mining primitives thereafter. I will\u0026nbsp;detail two core algorithms in this framework, including (1) a weakly supervised\u0026nbsp;text classification algorithm; and (2) an abnormal event detection algorithm.\u0026nbsp;The algorithms in the framework all require little supervision and are thus\u0026nbsp;particularly appealing in scenarios where labeled data are expensive to\u0026nbsp;acquire.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Multidimensional Text Mining with Limited Supervision"}],"uid":"34977","created_gmt":"2019-03-05 21:16:14","changed_gmt":"2019-03-06 13:59:24","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-03-11T15:00:00-04:00","event_time_end":"2019-03-11T16:00:00-04:00","event_time_end_last":"2019-03-11T16:00:00-04:00","gmt_time_start":"2019-03-11 19:00:00","gmt_time_end":"2019-03-11 20:00:00","gmt_time_end_last":"2019-03-11 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}