{"683623":{"#nid":"683623","#data":{"type":"event","title":"PhD Defense by  Lingchao Mao","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Machine Learning Methods for Data Disentanglement and Fusion in Biomedical Applications\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: August 14, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 1:00 pm \u2013 3:00 pm (EST)\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Groseclose 403 and zoom: \u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/99195465628?pwd=OlEpmY6yP8MLwiKxaWRz9da3c4hP4Q.1\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/99195465628?pwd=OlEpmY6yP8MLwiKxaWRz9da3c4hP4Q.1\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELingchao Mao\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003EH. Milton Stewart School of Industrial and\u003C\/p\u003E\u003Cp\u003ESystems Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E1 Dr. Jing Li (ISYE, Georgia Tech) (Advisor)\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E2 Dr. Jianjun Shi (ISYE, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E3 Dr. Kamran Paynabar (ISYE, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E4 Dr. Xiaochen Xian (ISYE, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E5 Dr. Lauren Steimle (ISYE, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E6 Dr. Jiajing Huang (Data Science and Analytics, Kennesaw State University)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis thesis develops machine learning methods to address challenges in biomedical data analysis, including limited supervision, missing modalities, and high-dimensional temporal dynamics. The proposed models aim to disentangle complex biomedical data and fuse diverse sources of information for more reliable prediction and interpretation. First, a Weakly Supervised Transfer Learning (WS-TL) framework enables personalized tumor cell density prediction from MRI using imprecise labels and domain adaptation. Second, the Multi-Modal Fission Learning (MMFL) model decomposes multi-modal data into globally shared, partially shared, and unique components, with natural extension to incomplete multi-modal settings and showing effectiveness in a case study for Alzheimer\u2019s prediction. Third, DynMoCo applies graph-based and knowledge-informed dynamic community detection to 4D Molecular Dynamics (MD) simulations, uncovering modular, localized, and functionally relevant motions and providing a new lens for interpretation and knowledge-discovery. Together, these contributions advance interpretable and robust learning for biomedical data analysis.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EMachine Learning Methods for Data Disentanglement and Fusion in Biomedical Applications\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Machine Learning Methods for Data Disentanglement and Fusion in Biomedical Applications"}],"uid":"27707","created_gmt":"2025-08-07 17:48:07","changed_gmt":"2025-08-07 17:48:07","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-08-14T13:00:00-04:00","event_time_end":"2025-08-14T15:00:00-04:00","event_time_end_last":"2025-08-14T15:00:00-04:00","gmt_time_start":"2025-08-14 17:00:00","gmt_time_end":"2025-08-14 19:00:00","gmt_time_end_last":"2025-08-14 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 403 and zoom","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":""}}}