{"674202":{"#nid":"674202","#data":{"type":"event","title":"PhD Defense | Generalizable and Explainable Methods for Learning from Physiological Data and Beyond","body":[{"value":"\u003Cp\u003ERan Liu - Machine Learning PhD Student - School of Electrical and Computer Engineering\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EApril 22nd\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;\u003C\/strong\u003E3:30 PM \u2013 5:00 PM ET\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\u003EMeeting 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\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr.\u0026nbsp; Eva Dyer (Advisor), Biomedical Engineering, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Anqi Wu, Computational Science and Engineering, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Zsolt Kira, Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Vidya Muthukumar, Electrical and Computer Engineering, Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Vince Calhoun, Electrical and Computer Engineering, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\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":"Ran Liu - Machine Learning PhD Student - School of Electrical and Computer Engineering"}],"uid":"36518","created_gmt":"2024-04-16 12:49:52","changed_gmt":"2024-04-16 12:56:50","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-22T15:30:00-04:00","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 19:30:00","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":"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":""}}}