{"678864":{"#nid":"678864","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Anni Zhou","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; Trustworthy and Robust Early Sepsis Prediction for Intensive Care Unit Patients using Reinforcement Learning and Conformal Prediction\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Raheem Beyah, ECE, Chair, Advisor\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Rishikesan Kamaleswaran, Duke, Co-Advisor\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Yao Xie, ISyE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Saman Zonouz, ECE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Omer Inan, ECE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;David Anderson, ECE\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis dissertation develops advanced machine learning frameworks to improve early sepsis prediction in ICU patients. Three novel approaches are introduced: OnAI-Comp, a multi-armed bandit framework that selects the best-performing model for each patient; Sepsyn-OLCP, a reinforcement learning algorithm with conformal prediction for reliable outcomes; and NeuroSep-CP-LCB, a neural network-based contextual bandit integrating conformal prediction for calibrated, data-driven decisions. These methods prioritize accuracy and trustworthiness, addressing critical needs in predictive healthcare and advancing sepsis prediction in critical care environments.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Trustworthy and Robust Early Sepsis Prediction for Intensive Care Unit Patients using Reinforcement Learning and Conformal Prediction "}],"uid":"28475","created_gmt":"2024-12-13 22:23:16","changed_gmt":"2024-12-13 22:24:23","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-03T13:00:00-05:00","event_time_end":"2025-01-03T15:00:00-05:00","event_time_end_last":"2025-01-03T15:00:00-05:00","gmt_time_start":"2025-01-03 18:00:00","gmt_time_end":"2025-01-03 20:00:00","gmt_time_end_last":"2025-01-03 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online","extras":[],"related_links":[{"url":"https:\/\/gatech.zoom.us\/j\/97568561301?pwd=T5tq9mfWlB0bzaRLaDbIqjThynxJxk.1\u0026from=addon","title":"Zoom link"}],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}