{"679210":{"#nid":"679210","#data":{"type":"event","title":"PhD Defense by Chen Xu","body":[{"value":"\u003Cp\u003EYou are cordially invited to my thesis defense on Tuesday at 1:30 PM, January 7th, 2025.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;Conformal prediction for time-series and flow-based generative models\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;Jan 7th, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;1:30PM \u2013 3PM\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Groseclose 403,\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/95278758914?pwd=HZoupL3TS24biV5qggCj66zbnbe0KS.1\u0022 title=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NDY4YzFhMTMtMjBhMi00NTdkLTg0OTEtOWY5OGI5YjM0OWM4%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2285b3a23a-6b6d-4161-9ffa-669984431950%22%7d\u0022\u003Emeeting link\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EChen Xu\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EOperations Research PhD Student\u003C\/p\u003E\u003Cp\u003EH. Milton Stewart School of Industrial and Systems 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\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Yao Xie (Advisor), H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Arkadi Nemirovski, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Johannes Milz, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Xiuyuan Cheng, Duke University\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Feng Qiu, Argonne National Laboratory\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;This thesis addresses two key areas: quantifying uncertainty in point prediction models (Chapters 1 and 2) and modeling data distributions with flow-based generative models (Chapters 3 and 4). Chapter 1 extends conformal prediction to time-series data, providing prediction intervals with bounded conditional coverage gaps and demonstrating superior empirical performance. Chapter 2 builds on this by sequentially updating non-conformity quantiles to better capture time-series dependencies and introducing ellipsoidal prediction regions for multivariate time-series. On the other hand, Chapter 3 develops flow-based models using ordinary differential equations, enabling novel sample generation and likelihood estimation via a framework based on the Jordan-Kinderlehrer-Otto scheme for stage-wise training. Finally, Chapter 4 enhances the scalability of ODE-based models by introducing a local flow matching approach, improving training efficiency and distillation performance.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EConformal prediction for time-series and flow-based generative models\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Conformal prediction for time-series and flow-based generative models"}],"uid":"27707","created_gmt":"2025-01-06 20:57:02","changed_gmt":"2025-01-06 20:57:41","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-07T13:30:04-05:00","event_time_end":"2025-01-07T15:00:00-05:00","event_time_end_last":"2025-01-07T15:00:00-05:00","gmt_time_start":"2025-01-07 18:30:04","gmt_time_end":"2025-01-07 20:00:00","gmt_time_end_last":"2025-01-07 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 403, meeting link","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":""}}}