{"674230":{"#nid":"674230","#data":{"type":"event","title":"PhD Defense by Ran Liu","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: \u003C\/strong\u003EGeneralizable and Explainable Methods for Learning from Physiological Data and Beyond\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate: \u003C\/strong\u003EApril 22, 2024\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime: \u003C\/strong\u003E3:30 PM - 5:00 PM (EST)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Coda C1103 Lindberg\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EZoom Link\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/97333964943?pwd=UEJBQ2MzU2pXZk1RQmRzeGtkYXh2Zz09\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/97333964943?pwd=UEJBQ2MzU2pXZk1RQmRzeGtkYXh2Zz09\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ERan Liu\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\r\n\r\n\u003Cp\u003EElectrical and Computer Engineering (ECE)\u003Cbr \/\u003E\r\nGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E1 Dr. Eva Dyer (Advisor)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E2 Dr. Anqi Wu\u003C\/p\u003E\r\n\r\n\u003Cp\u003E3 Dr. Zsolt Kira\u003C\/p\u003E\r\n\r\n\u003Cp\u003E4 Dr. Vidya Muthukumar\u003C\/p\u003E\r\n\r\n\u003Cp\u003E5 Dr. Vince Calhoun\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDeep learning (DL) methods have significantly advanced the fields of neuroscience and physiology. However, conventional DL methods that are tailored to specific populations and tasks are no longer adequate in comprehending large-scale, multimodal, and multitask physiological datasets. In this thesis, we propose methods that aim to improve DL methods from the perspective of: (i) Generalizability, enabling applications across diverse modalities, tasks, and subjects, and (ii) Explainability, enabling researchers to understand and potentially customize the learning process to suit specific distributions. These improvements are not only crucial for physiological datasets, which typically require domain knowledge to comprehend, but also improve deep learning methodologies and benefit the broader ML community.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeneralizable and Explainable Methods for Learning from Physiological Data and Beyond\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Generalizable and Explainable Methods for Learning from Physiological Data and Beyond"}],"uid":"27707","created_gmt":"2024-04-16 19:18:21","changed_gmt":"2024-04-16 19:19:03","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-22T15:30:37-04:00","event_time_end":"2024-04-22T17:00:00-04:00","event_time_end_last":"2024-04-22T17:00:00-04:00","gmt_time_start":"2024-04-22 19:30:37","gmt_time_end":"2024-04-22 21:00:00","gmt_time_end_last":"2024-04-22 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda C1103 Lindberg","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"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":""}}}