{"335021":{"#nid":"335021","#data":{"type":"event","title":"CSE Distinguished Lecturer Seminar","body":[{"value":"\u003Cp class=\u0022p1\u0022\u003ESpeaker:\u0026nbsp;\u003Cstrong\u003EJoydeep Ghosh\u003C\/strong\u003E\u003C\/p\u003E\u003Cp class=\u0022p1\u0022\u003ESchlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin\u003C\/p\u003E\u003Cp class=\u0022p1\u0022\u003EDate: Friday, October 24, 2014\u003C\/p\u003E\u003Cp class=\u0022p1\u0022\u003ETime: 02:00PM-03:00PM, EST\u003C\/p\u003E\u003Cp class=\u0022p1\u0022\u003ELocation: \u003Cstrong\u003EKACB 1116 W\u003C\/strong\u003E\u003C\/p\u003E\u003Cp class=\u0022p1\u0022\u003EFor more information please contact\u0026nbsp;Dr. Jimeng Sun at \u003Ca href=\u0022mailto:jsun@cc.gatech.edu\u0022\u003Ejsun@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp class=\u0022p2\u0022\u003E\u003Cstrong\u003EPredictive Healthcare Analytics under Privacy Constraints\u003C\/strong\u003E\u003C\/p\u003E\u003Cp class=\u0022p3\u0022\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr \/\u003E The move to electronic health records is producing a wealth of information, which has the potential of providing unprecedented insights into the cause, prevention, treatment and management of illnesses. Analyses of such data also promises numerous opportunities for much more effective and efficient delivery of healthcare. However (valid) privacy concerns and restrictions prevent unfettered access to such data. In this talk I will first provide a perspective on the privacy vs. utility trade-off in the context of healthcare analytics. I will then \u0026nbsp;outline two approaches that we have recently and successfully taken that provide privacy-aware predictive modeling with little degradation in model quality despite restrictions on what can be shared or analyzed. The first approach focuses on extracting predictive value from data that has been aggregated at various levels due to privacy concerns, while the second introduces a novel, non-parametric sampler that can generate \u0022realistic but not real\u0022 data given a dataset that cannot be shared as is.\u003C\/p\u003E\u003Cp class=\u0022p3\u0022\u003E\u003Cstrong\u003EBiography\u003C\/strong\u003E\u003Cbr \/\u003E Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at, (B. Tech \u002783) and The University of Southern California (Ph.D\u201988). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a Fellow of the IEEE. Dr. Ghosh has taught graduate courses on data mining and web analytics every year to both UT students and to industry, for over a decade. He was voted as \u0022Best Professor\u0022 in the Software Engineering Executive Education Program at UT.\u003C\/p\u003E\u003Cp class=\u0022p3\u0022\u003EDr. Ghosh\u0027s research interests lie primarily in data mining and web mining, predictive modeling \/ predictive analytics, machine learning approaches such as adaptive multi-learner systems, and their applications to a wide variety of complex real-world problems. He has published more than 300 refereed papers and 50 book chapters, and co-edited over 20 books. His research has been supported by the NSF, Yahoo!, Google, ONR, ARO, AFOSR, Intel, IBM, and several others. He has received 14 Best Paper Awards over the years, including the 2005 Best Research Paper Award across UT and the 1992 Darlington Award given by the IEEE Circuits and Systems Society for the overall Best Paper in the areas of CAS\/CAD. Dr. Ghosh has been a plenary\/keynote speaker on several occasions such as MICAI\u002712, KDIR\u002710, ISIT\u002708, ANNIE\u201906 and MCS 2002, and has widely lectured on intelligent analysis of large-scale data. He served as the Conference Co-Chair or Program Co-Chair for several top data mining oriented conferences, including SDM\u002713, SDM\u0027\u002712, KDD 2011, CIDM\u201907, ICPR\u002708 (Pattern Recognition Track) and SDM\u002706. He was the Conf. Co-Chair for Artificial Neural Networks in Engineering (ANNIE)\u002793 to \u002796 and \u002799 to \u002703 and the founding chair of the Data Mining Tech. Committee of the IEEE Computational Intelligence Society. He has also co-organized workshops on high dimensional clustering, Web Analytics, Web Mining and Parallel\/ Distributed Knowledge Discovery.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Predictive Healthcare Analytics under Privacy Constraints"}],"uid":"28124","created_gmt":"2014-10-17 12:31:32","changed_gmt":"2016-10-08 02:09:50","author":"Tyler Sharp","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2014-10-24T15:00:00-04:00","event_time_end":"2014-10-24T16:00:00-04:00","event_time_end_last":"2014-10-24T16:00:00-04:00","gmt_time_start":"2014-10-24 19:00:00","gmt_time_end":"2014-10-24 20:00:00","gmt_time_end_last":"2014-10-24 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[{"id":"106711","name":"Healthcare Analytics"},{"id":"3221","name":"privacy"},{"id":"167978","name":"Schlumberger"},{"id":"10938","name":"University of Texas"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003ECarolyn Young\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:cyoung@cc.gatech.edu\u0022\u003Ecyoung@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}