{"671820":{"#nid":"671820","#data":{"type":"event","title":"School of CSE Seminar Series: Wei Jin","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E\u0026nbsp;Emory University Assistant Professor Wei Jin\u003Cbr \/\u003E\r\n\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp;January 19, 2:00-3:00 p.m.\u003Cbr \/\u003E\r\n\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Coda 114\u003Cbr \/\u003E\r\n\u003Cstrong\u003EHost:\u003C\/strong\u003E\u0026nbsp;B. Aditya Prakash\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDeep Learning on Graphs: A Data-Centric Exploration\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMany learning tasks in Artificial Intelligence require dealing with graph data, ranging from biology and chemistry to finance and education. Graph neural networks (GNNs), as deep learning models, have shown exceptional capabilities in learning from graph data. Despite their successes, GNNs often grapple with challenges stemming from data size and quality. This talk emphasizes a data-centric approach to enhance GNN performance. First, I will demonstrate methods to significantly reduce graph dataset sizes while retaining essential information for model training. Next, I will introduce a model-agnostic framework that enhances the quality of imperfect input graphs, thereby boosting prediction performance.\u0026nbsp;These data-centric strategies not only enhance data efficiency and quality but also complement existing models. Finally, I will introduce recent advances in graph data valuation and graph generation. Join us to explore innovative approaches for overcoming data-related challenges in graph data mining.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E \u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWei Jin is an Assistant Professor of Computer Science at Emory University. He obtained his Ph.D. from Michigan State University in 2023. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EHis research focuses on graph machine learning and data-centric AI, with notable accomplishments such as\u003Cspan\u003E AAAI New Faculty Highlights, KAUST Rising Star in AI, Snap Research Fellowship, Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. \u003C\/span\u003EHe has organized tutorials and workshops at top conferences, and published in top-tier venues such as ICLR, KDD, ICML, and NeurIPS. He has served as (senior) program committee members at these conferences and received the WSDM Outstanding Program Committee Member award.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E\u0026nbsp;Emory University Assistant Professor Wei Jin\u003Cbr \/\u003E\r\n\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp;January 19, 2:00-3:00 p.m.\u003Cbr \/\u003E\r\n\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Coda 114\u003Cbr \/\u003E\r\n\u003Cstrong\u003EHost:\u003C\/strong\u003E\u0026nbsp;B. Aditya Prakash\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003Cem\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDeep Learning on Graphs: A Data-Centric Exploration\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/em\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"School of CSE hosts a seminar from Wei Jin, assistant professor at Emory University"}],"uid":"36319","created_gmt":"2024-01-03 16:22:35","changed_gmt":"2024-01-12 19:30:05","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-19T14:00:00-05:00","event_time_end":"2024-01-19T15:00:00-05:00","event_time_end_last":"2024-01-19T15:00:00-05:00","gmt_time_start":"2024-01-19 19:00:00","gmt_time_end":"2024-01-19 20:00:00","gmt_time_end_last":"2024-01-19 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda, Room 114","extras":["free_food"],"hg_media":{"672654":{"id":"672654","type":"image","title":"Wei Jin.jpeg","body":null,"created":"1704298963","gmt_created":"2024-01-03 16:22:43","changed":"1704298963","gmt_changed":"2024-01-03 16:22:43","alt":"Wei Jin","file":{"fid":"255931","name":"Wei Jin.jpeg","image_path":"\/sites\/default\/files\/2024\/01\/03\/Wei%20Jin.jpeg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/01\/03\/Wei%20Jin.jpeg","mime":"image\/jpeg","size":865582,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/01\/03\/Wei%20Jin.jpeg?itok=bh9NitwE"}}},"media_ids":["672654"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[{"id":"166983","name":"School of Computational Science and Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EB. Aditya Prakash (badityap@cc.gatech.edu)\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}