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$20 Million Grant Drives IDEAL Research in Data Science

A Chicago-based research coalition that includes researchers from Illinois Institute of Technology has been awarded a share of a five-year, $20 million grant

United statesNorthwestern universityUniversity of chicagoIllinois institute of technologyShekhar bhansaliJinqiao jeffrey duanBinghui wangMuseum of scienceTransdisciplinary research in principles of data scienceUniversity of illinois chicagoToyota technological instituteInstitute for dataNational science foundationIllinois instituteTransdisciplinary researchData science

Rachel Ward Receives NSF TRIPODS Award

Rachel Ward Receives NSF TRIPODS Award
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United statesRachel wardShekhar bhansaliJohn holdenTransdisciplinary research in principles of data scienceInstitute for emergingNational science foundationCollege of natural sciencesOden institute for computational engineeringUniversity of texas at austinUniversity of pennsylvaniaData sciencesTransdisciplinary researchData scienceNatural sciencesOden institute

New NSF Awards will Advance Theoretical Foundations of Data Science Research

New NSF Awards will Advance Theoretical Foundations of Data Science Research
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Northwestern universityUnited statesUniversity of chicagoIllinois institute of technologyUniversity of california san diegoShekhar bhansaliLos angelesUniversity of californiaMuseum of scienceTransdisciplinary research in principles of data scienceToyota technological institute at chicagoToyota technological instituteInstitute for dataInstitute for emergingUniversity of illinois chicagoUniversity of texas at austin

Showcasing the Work and Perspectives of Early-Career Researchers

On December 9-10, the Northwestern CS Theory Group and IDEAL co-hosted the Junior Theorists Workshop as part of the Quarterly Theory Workshop series.

Stanford universityUnited statesCollege parkUniversity of chicagoUniversity of illinois at chicagoAditi laddhaAdrienne barrisJason hartlineShravas raoAnnie liangBenjamin golubKiran shiragurYingkai liKonstantin makarychevXiao wangSamir khuller

Exploring New Directions on Robustness in Machine Learning

On November 16, IDEAL hosted a workshop focused on new directions on robustness in machine learning as part of the fall 2021 special quarter organized and moderated by a multi-institutional team including Professor Aravindan Vijayaraghavan.

University of chicagoUnited statesUniversity of illinois at chicagoGautam kamathJason hartlineKamalika chaudhuriMark sellkePranjal awasthiYu chengJinshuo dongKonstantin makarychevYury makarychevAravindan vijayaraghavanChao gaoMccormick school of engineeringTransdisciplinary research in principles of data science