{"611338":{"#nid":"611338","#data":{"type":"news","title":"New TRIPODS+X Awards Target Data Science Solutions in Science, Engineering, and Mathematics","body":[{"value":"\u003Cp\u003EThree data science projects in the National Science Foundation\u0026rsquo;s Transdisciplinary Research in Principles of Data Science (\u003Ca href=\u0022https:\/\/www.nsf.gov\/news\/news_summ.jsp?cntn_id=242888\u0022\u003ETRIPODS\u003C\/a\u003E) program have been awarded to Georgia Tech investigators.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe awards were designed to expand the scope of the TRIPODS cross-disciplinary institutes established last year, including \u003Ca href=\u0022http:\/\/triad.gatech.edu\/\u0022\u003EGeorgia Tech\u0026rsquo;s Transdisciplinary Research Institute for Advancing Data Science\u003C\/a\u003E (TRIAD).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;The multidisciplinary approach for addressing the increasing volume and complexity of data enabled through the TRIPODS+X projects will have a profound impact on the field of data science and its use,\u0026rdquo; said Jim Kurose, NSF assistant director for Computer and Information Science and Engineering (CISE). \u0026quot;This impact will be sure to grow as data continues to drive scientific discovery and innovation.\u0026rdquo;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EA total of $8.5 million in \u003Ca href=\u0022https:\/\/www.nsf.gov\/pubs\/2018\/nsf18542\/nsf18542.htm\u0022\u003ETRIPODS+X \u003C\/a\u003E\u0026nbsp;grants were awarded this year, supporting 19 collaborative projects at 23 universities, and bringing new perspectives to complex and entrenched data science problems in science, engineering, and mathematics.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe three Georgia Tech projects span three different NSF priorities in education, visualization, and research.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cbr \/\u003E\r\n\u003Cstrong\u003EEducation: Data-driven Discovery and Alliance\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPrasad Tetali and his multi-institutional team including traditional women\u0026rsquo;s and historic black colleges and universities, are developing undergraduate courses for STEM majors to give more students access to a data-driven future. With this grant, the collaborative alliance, grounded in math, statistics and computer science theory, will develop a toolkit of data science modules to integrate into science curriculum at Agnes Scott College, Morehouse College, and Spelman College. They will also hold boot camps and workshops. The educational outreach will enrich the knowledge of these institutions\u0026rsquo; faculty, and later, the team plans to adapt the initiative to serve other research-intensive women\u0026rsquo;s and HBCU institutions.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;The NSF-supported educational alliance is exciting in many ways,\u0026rdquo; Tetali says. \u0026ldquo;It gives opportunity to infuse the foundational data science curriculum with real-world applications from the physical and life sciences. It will also likely catalyze collaborative research in data science and related fields between Georgia Tech and Atlanta area colleges.\u0026rdquo;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EInvestigators:\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EPrasad Tetali (lead), Georgia Tech School of Mathematics and School of Computer Science\u003C\/li\u003E\r\n\t\u003Cli\u003EBrandeis Marshall (collaborative lead), Spelman College\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003C\/li\u003E\r\n\t\u003Cli\u003EChris DePree, Agnes Scott College\u003C\/li\u003E\r\n\t\u003Cli\u003EAlan Koch, Agnes Scott College\u003C\/li\u003E\r\n\t\u003Cli\u003EWenjing Liao, Georgia Tech School of Mathematics\u003C\/li\u003E\r\n\t\u003Cli\u003EChuang Peng, Morehouse College\u003C\/li\u003E\r\n\t\u003Cli\u003EDavid Sherrill, Georgia Tech School of Chemistry and Biochemistry\u003C\/li\u003E\r\n\t\u003Cli\u003EJoshua Weitz, Georgia Tech School of Biological Sciences\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EAward Amount: $200,000\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EVision: Creating an Annual Data Science Forum\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDana Randall and colleagues from Carnegie Mellon University and Columbia University are creating a week-long\u0026nbsp;Data Science Forum\u0026nbsp;built around the\u0026nbsp;Second Symposium on Machine Learning in Science and Engineering (MLSE). The forum\u0026nbsp;combines multiple events aimed at catalyzing communication across foundations, applications, and disciplinary fields, and at fostering diversity and inclusion. Two new workshops that complement the conference are a part of the forum: A Women in Data Science Workshop, and a Foundations of Data Driven Discovery workshop.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMLSE, begun last year by Georgia Tech and Carnegie Mellon, was the first annual machine learning conference organized to collocate tracks within traditional disciplines using machine learning while allowing an exchange of ideas across disciplines. This cross-disciplinary breadth combined with efforts to build diversity in attendance will permeate all MLSE events, and enable a visioning working group at the meeting to\u0026nbsp;develop an inclusive report on the future of machine learning.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;The first MLSE last summer was a great success, providing a new forum for machine learning discussions among scientists and engineers,\u0026rdquo; said Randall.\u0026nbsp;\u0026ldquo;It\u0026rsquo;s very exciting that this grant allows us to expand the event for the next two years by including more students, women, and adding a workshop promoting theoretical foundations, consistent with the goals of TRIAD and IDEaS.\u0026rdquo;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EInvestigators:\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EDana Randall (lead), Georgia Tech School of Computer Science\u003C\/li\u003E\r\n\t\u003Cli\u003ESrinivas Aluru, Georgia Tech School of Computational Science and Engineering\u003C\/li\u003E\r\n\t\u003Cli\u003ENewell Washburn, Carnegie Mellon University\u003C\/li\u003E\r\n\t\u003Cli\u003EJeanette Wing, Columbia University\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EAward Amount: $200,000\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EResearch: Scaling Up Descriptive Epidemiology and Metabolic Network Models Using Faster Sampling\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESantosh Vempala and researchers at the University of Washington are interested in solving a sampling problem that will help researchers spanning many disciplines. Sampling from a given distribution from a space with many attributes is a fundamental problem in computer science. Over the past two decades, practical applications of sampling have proliferated in statistics, networking, biology, differential privacy, and, most notably, machine learning. Sampling is used to evaluate models, as a subroutine for optimization, and more generally for exploring large complex spaces.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe researchers will help develop a toolkit for sampling and evaluate it on real data sets\u0026mdash;a large-scale, high-dimensional toolkit for sampling smooth and non-smooth distributions, and a suite of functions that can be computed or estimated using access to samples. It will be developed by working with domain experts in health metrics and systems biology.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EInvestigators:\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EYin Tat Lee (lead), University of Washington\u003C\/li\u003E\r\n\t\u003Cli\u003ESantosh Vempala (collaborative lead), Georgia Tech School of Computer Science\u003C\/li\u003E\r\n\t\u003Cli\u003EAbraham Flaxman, University of Washington\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EAward Amount: $600,000\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETransdisciplinary Research Institutes\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech\u0026rsquo;s TRIAD, part of a \u003Ca href=\u0022https:\/\/nsf-tripods.org\/institutes\/\u0022\u003Ecommunity of TRIPODS institutes\u003C\/a\u003E that share expertise and work together, integrates research and education in mathematical, statistical, and algorithmic foundations for data science. TRIAD also hosts focused working groups, national and international workshops, and organized innovation labs, to share data science insights and resources locally and nationally.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;The TRIPODS program, and with it our own TRIAD institute, were established to expand our collective capabilities and accelerate progress,\u0026rdquo; said Xiaoming Huo, executive director of TRIAD. \u0026ldquo;Whether it is for education, defining a vision for the future, or pushing the frontiers of research, the new ideas we need come from bridging the boundaries of science, engineering and mathematics.\u0026rdquo;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Three data science projects in the National Science Foundation\u2019s Transdisciplinary Research in Principles of Data Science (TRIPODS) program have been awarded to Georgia Tech investigators."}],"uid":"27343","created_gmt":"2018-09-11 20:38:11","changed_gmt":"2018-09-11 20:48:48","author":"Jennifer Salazar","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2018-09-11T00:00:00-04:00","iso_date":"2018-09-11T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"611337":{"id":"611337","type":"image","title":"TRIAD Team","body":null,"created":"1536697243","gmt_created":"2018-09-11 20:20:43","changed":"1536697243","gmt_changed":"2018-09-11 20:20:43","alt":"","file":{"fid":"232737","name":"TRIAD.jpg","image_path":"\/sites\/default\/files\/images\/TRIAD.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/TRIAD.jpg","mime":"image\/jpeg","size":886521,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/TRIAD.jpg?itok=aOpPlSzT"}}},"media_ids":["611337"],"groups":[{"id":"545781","name":"Institute for Data Engineering and Science"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"129","name":"Institute and Campus"}],"keywords":[{"id":"175350","name":"TRIAD"},{"id":"175351","name":"TRIPODS"},{"id":"92811","name":"data science"},{"id":"4449","name":"ideas"},{"id":"171795","name":"data engineering"}],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDr. JF Salazar\u003C\/p\u003E\r\n\r\n\u003Cp\u003EInstitute for Data Engineering and Science\u003C\/p\u003E\r\n","format":"limited_html"}],"email":["jsalazar@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}