{"674338":{"#nid":"674338","#data":{"type":"event","title":"PhD Defense | Change-Point Detection and Causal Inference for Time Series with Applications in Healthcare","body":[{"value":"\u003Cp\u003ESong Wei - Machine Learning PhD Student - School of Industrial and Systems Engineering\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EMay 2nd\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime: \u003C\/strong\u003E12:45 PM \u2013 3:00 PM ET\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Groseclose 403\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMeeting Link\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/6286168510?pwd=UmVacUhQL1RhZHY0VSt6TjRtMGFoQT09\u0022 title=\u0022https:\/\/gatech.zoom.us\/j\/6286168510?pwd=UmVacUhQL1RhZHY0VSt6TjRtMGFoQT09\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/6286168510?pwd=UmVacUhQL1RhZHY0VSt6TjRtMGFoQT09\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yao Xie (Advisor, School of Industrial and Systems Engineering, Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Rishikesan Kamaleswaran (Department of Surgery, Duke University)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Feng Qiu (Argonne National Laboratory)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yajun Mei (School of Industrial and Systems Engineering, Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Gari Clifford (Department of Biomedical Informatics, Emory University \u0026amp; Department of Biomedical Engineering, Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EExplainable prediction algorithms have become increasingly important in automated surveillance systems within the healthcare context, as they offer actionable insights for clinicians on duty to respond to predicted adverse events. In this thesis, I will present a real study on sepsis prediction, and several novel methods motivated by it. Those methods, developed with the help of recent advancements in statistics and optimization, enjoy strong theoretical guarantees and exhibit promising empirical performance. Importantly, with the numerical demonstration on the real data, I hope the developed methods can be extended to a broader range of real applications.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EChange-Point Detection and Causal Inference for Time Series with Applications in Healthcare\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Song Wei - Machine Learning PhD Student - School of Industrial and Systems Engineering"}],"uid":"36518","created_gmt":"2024-04-23 17:20:00","changed_gmt":"2024-04-23 17:22:10","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-05-02T12:45:00-04:00","event_time_end":"2024-05-02T15:00:00-04:00","event_time_end_last":"2024-05-02T15:00:00-04:00","gmt_time_start":"2024-05-02 16:45:00","gmt_time_end":"2024-05-02 19:00:00","gmt_time_end_last":"2024-05-02 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 403 ","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}