{"670214":{"#nid":"670214","#data":{"type":"news","title":"Hao and Li Join Department of Energy Artificial Intelligence Initiative to Propel Nuclear Physics Research","body":[{"value":"\u003Cp\u003EThe U.S. Department of Energy (DOE) announced\u0026nbsp;\u003Ca href=\u0022https:\/\/www.energy.gov\/science\/articles\/department-energy-announces-16-million-research-artificial-intelligence-and\u0022\u003E$16 million\u003C\/a\u003E\u0026nbsp;for fifteen projects that will leverage the power of artificial intelligence and machine learning (AI\/ML) to accelerate scientific discovery within the realm of nuclear physics. The fifteen projects were announced in August and will work to advance understanding of atomic structure and the nature of matter.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAmong the select group of researchers involved in a specific project are professors \u003Ca href=\u0022https:\/\/ece.gatech.edu\/directory\/callie-hao\u0022\u003ECong \u201cCallie\u201d Hao\u003C\/a\u003E\u0026nbsp;and\u0026nbsp;\u003Ca href=\u0022https:\/\/ece.gatech.edu\/directory\/pan-li\u0022\u003EPan Li\u003C\/a\u003E\u0026nbsp;in the\u0026nbsp;\u003Ca href=\u0022https:\/\/ece.gatech.edu\/\u0022\u003EGeorgia Tech School of Electrical and Computer Engineering\u003C\/a\u003E\u0026nbsp;(ECE).\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EHao and Li will play a pivotal role in the \u0022Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC detectors\u0022 project; a $2 million endeavor being led by Ming Xiong Liu of the\u0026nbsp;\u003Ca href=\u0022https:\/\/www.lanl.gov\/\u0022\u003ELos Alamos National Laboratory\u003C\/a\u003E\u0026nbsp;(LANL).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cI am thrilled to see how our achievements in computer architecture research can have cross-disciplinary impact on high-energy physics experiments, extending well beyond mere academic papers and hardware prototypes,\u201d said Hao. \u201cWe are highly motivated and proud to tackle real-world problems by offering solutions that are both intelligent and practical.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EHao and her research team will work to furnish an ultra-fast field programmable gate arrays (FPGA) system for particle detection and tracking in high-energy physics experiments. The system will be equipped with an automated design flow that eliminates the need for manual intervention. Achieving this necessitates sophisticated hardware optimizations, coupled with algorithmic innovations, according to Hao.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELi and his team will contribute to the development of robust learning algorithms via domain adaptation methods, aiming to address the online shift of the beam point of particle collisions.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAll projects are supported by the DOE Office of Science, Nuclear Physics Program, and were selected through a rigorous and competitive peer review process. According to the DOE press release, the fifteen projects will be conducted by researchers at eight DOE national laboratories and 22 universities. Projects will include the development of deep learning algorithms to identify a unique signal for studying physics of fundamental symmetry in extremely rare nuclear decays that if observed would demonstrate how our universe could have become dominated by matter rather than antimatter.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe research teams will develop an ultra-fast field-programmable gate array system and robust learning algorithms.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Hao\u0027s research team is developing an ultra-fast field-programmable gate array system to detect and track particles in high-energy physics experiments."}],"uid":"36172","created_gmt":"2023-10-05 18:34:38","changed_gmt":"2024-03-25 12:50:55","author":"dwatson71","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-10-05T00:00:00-04:00","iso_date":"2023-10-05T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"673477":{"id":"673477","type":"image","title":"Hao_Li_DOE Nuclear Physics graphic.jpg","body":null,"created":"1711371018","gmt_created":"2024-03-25 12:50:18","changed":"1711371018","gmt_changed":"2024-03-25 12:50:18","alt":"Pan Li and Callie Hao Nuclear Physics research graphic","file":{"fid":"256876","name":"Hao_Li_DOE Nuclear Physics graphic.jpg","image_path":"\/sites\/default\/files\/2024\/03\/25\/Hao_Li_DOE%20Nuclear%20Physics%20graphic.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/03\/25\/Hao_Li_DOE%20Nuclear%20Physics%20graphic.jpg","mime":"image\/jpeg","size":86815,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/03\/25\/Hao_Li_DOE%20Nuclear%20Physics%20graphic.jpg?itok=drzufPii"}}},"media_ids":["673477"],"groups":[{"id":"1255","name":"School of Electrical and Computer Engineering"}],"categories":[],"keywords":[{"id":"193134","name":"Cong"},{"id":"66891","name":"Georgia Tech School of Electrical and Computer Engineering"},{"id":"28931","name":"U.S. Department of Energy"},{"id":"2556","name":"artificial intelligence"},{"id":"9167","name":"machine learning"},{"id":"193135","name":"nuclear physics"},{"id":"193136","name":"field programmable gate arrays"}],"core_research_areas":[{"id":"39451","name":"Electronics and Nanotechnology"},{"id":"39481","name":"National Security"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDan Watson\u003C\/p\u003E\r\n","format":"limited_html"}],"email":["dwatson@ece.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}