{"670135":{"#nid":"670135","#data":{"type":"news","title":"Wenjing Liao Awarded DOE Early Career Award for Model Simplification, Deep Learning","body":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/wliao60.math.gatech.edu\/\u0022\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWenjing Liao\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/a\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E, an associate professor in the \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Ca href=\u0022https:\/\/math.gatech.edu\/\u0022\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESchool of Mathematics\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/a\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E, has been awarded a \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Ca href=\u0022https:\/\/science.osti.gov\/early-career\u0022\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDepartment of Energy (DOE) Early Career Award\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/a\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E for her research into how deep learning might be leveraging to make mathematical advances in achieving more efficient modeling techniques.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ELiao was selected as one of the 9\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E3 early career scientists from across the country who are receiving a combined $135 million in DOE funding. The awards aim to support the next generation of STEM leaders, and identify early-career scientists whose research will have global impacts.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEarlier this year, \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Ca href=\u0022https:\/\/cos.gatech.edu\/news\/mathematics-wenjing-liao-wins-nsf-career-award\u0022\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ELiao was also selected for an \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ENational Science Foundation (NSF) Faculty Early Career Development Program (CAREER) Award\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/a\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E, \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Eone of the most prestigious grants that a scientist can receive early in their profession.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cSupporting America\u2019s scientists and researchers early in their careers will ensure the U.S. remains at the forefront of scientific discovery and develops the solutions to our most pressing challenges,\u201d said U.S. Secretary of Energy \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EJennifer M. Granholm\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E, adding that the funding \u201cwill allow the recipients the freedom to find the answers to some of the most complex questions as they establish themselves as experts in their fields.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch4\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EModel simplification; complex problems\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h4\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EReal-world applications of computer modeling often call for large, complex data simulations, which can be time-consuming and expensive, limiting their applications. Liao\u2019s project \u201cModel Reduction by Deep Learning: Interpretability and Mathematical Advances\u201d focuses on a technique called model reduction, which allows researchers to reduce the size of problems computer models must solve to smaller ones that computers can efficiently solve.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ELiao notes that while traditional model-reduction methods have been successful, the technique is mostly limited to low dimensional linear models, or those with fewer important features that the model can include. However, many problems found in nature are the opposite. Liao hopes that by identifying the underlying nonlinear structures in natural problems, she can broaden the application of model-reduction techniques.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ETo do so, her research will focus on three key questions. First, she will investigate how to leverage deep neural networks to extract low-dimensional nonlinear structures in data sets. Next, Liao will investigate how to use the nonlinear structures in model reduction. Finally, in order to better harness deep learning, Liao aims to develop new deep learning-based mo\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Edel reduction methods.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cThis project has the potential to drive significant advances in scientific machine learning,\u201d Liao says in her abstract. \u201cThe proposed model-reduction methods can be used to analyze large datasets and simulate complex phenomena in physics, biology, and engineering.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":[{"value":"Liao selected as one of the 93 early career scientists from across the country who are receiving a combined $135 million in DOE funding"}],"field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ELiao\u0027s research will dig into how deep learning might be leveraging to make mathematical advances in achieving more efficient modeling techniques. \u201cThis project has the potential to drive significant advances in scientific machine learning,\u201d Liao says in her abstract. \u201cThe proposed model-reduction methods can be used to analyze large datasets and simulate complex phenomena in physics, biology, and engineering.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Liao selected as one of the 93 early career scientists from across the country who are receiving a combined $135 million in DOE funding"}],"uid":"35599","created_gmt":"2023-10-03 17:37:25","changed_gmt":"2023-12-14 17:10:34","author":"sperrin6","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-10-03T00:00:00-04:00","iso_date":"2023-10-03T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"671931":{"id":"671931","type":"image","title":"Wenjing Liao","body":null,"created":"1696354662","gmt_created":"2023-10-03 17:37:42","changed":"1696355018","gmt_changed":"2023-10-03 17:43:38","alt":"Wenjing Liao","file":{"fid":"255091","name":"Wenjing Liao - faculty.jpeg","image_path":"\/sites\/default\/files\/2023\/10\/03\/Wenjing%20Liao%20-%20faculty.jpeg","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/2023\/10\/03\/Wenjing%20Liao%20-%20faculty.jpeg","mime":"image\/jpeg","size":5709817,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/10\/03\/Wenjing%20Liao%20-%20faculty.jpeg?itok=a1BErhYB"}}},"media_ids":["671931"],"groups":[{"id":"1278","name":"College of Sciences"},{"id":"1279","name":"School of Mathematics"},{"id":"1188","name":"Research Horizons"}],"categories":[],"keywords":[{"id":"192249","name":"cos-community"},{"id":"192251","name":"cos-quantum"},{"id":"173647","name":"_for_math_site_"},{"id":"192863","name":"go-ai"}],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"},{"id":"39541","name":"Systems"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EWritten by Selena Langner\u003C\/p\u003E\r\n","format":"limited_html"}],"email":["jess.hunt@cos.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}