{"682786":{"#nid":"682786","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Riyasat Ohib","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003EPrincipled Sparsity for Efficient Deep Learning Across Computational Paradigms\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Calhoun, Advisor\u003C\/p\u003E\u003Cp\u003EDr. Plis, Co-Advisor\u003C\/p\u003E\u003Cp\u003EDr. Anderson, Chair\u003C\/p\u003E\u003Cp\u003EDr. Xiong\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe objective of the proposed research is to develop and evaluate novel, principled sparsity-inducing techniques to enhance the computational efficiency of deep learning models across diverse applications. This work proposes methods for explicit sparse projection, the discovery of efficient task-specific subnetworks in reinforcement learning, and the design of lightweight federated learning frameworks, with the ultimate goal of enabling more scalable, adaptable, and interpretable AI systems.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Principled Sparsity for Efficient Deep Learning Across Computational Paradigms"}],"uid":"28475","created_gmt":"2025-06-13 18:11:33","changed_gmt":"2025-06-13 18:13:02","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-06-17T10:00:00-04:00","event_time_end":"2025-06-17T12:00:00-04:00","event_time_end_last":"2025-06-17T12:00:00-04:00","gmt_time_start":"2025-06-17 14:00:00","gmt_time_end":"2025-06-17 16:00:00","gmt_time_end_last":"2025-06-17 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online","extras":[],"related_links":[{"url":"https:\/\/gatech.zoom.us\/j\/7609011746?pwd=QVFqdGJZdVZhZkUrbHZBaWlhdzZ2UT09\u0026omn=92007870180","title":"Zoom link"}],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"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":""}}}