Computational Approaches to the Network Science of Teams

Li, Liangyue; Tong, Hanghang

ISBN 10: 110849854X / ISBN 13: 9781108498548

Editore: Cambridge University Press, 2021

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Codice articolo ABNR-287791

Dati bibliografici

• Titolo: Computational Approaches to the Network Science of Teams
• Editore: Cambridge University Press
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 110849854X
• ISBN 13: 9781108498548
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

Surveys recent models and algorithms characterizing, predicting, optimizing, and explaining team performance in a variety of settings.

Informazioni sugli autori

Liangyue Li is an applied scientist at Amazon. He received his PhD in computer science from Arizona State University. He has served as a program committee member in top data-mining and artificial intelligence venues (such as SIGKDD, ICML, AAAI and CIKM). He has given a tutorial at WSDM 2018, KDD 2018, and a keynote talk at CIKM 2016 Workshop on Big Network Analytics (BigNet 2016).

Hanghang Tong is an associate professor at University of Illinois, Urbana-Champaign since August 2019, Before that, he was an associate professor at Arizona State University, an assistant professor at City College, City University of New York, a research staff member at IBM T.J. Watson Research Center, and a postdoctoral fellow at Carnegie Mellon University, Pennsylvania. He received his M.Sc. and Ph.D. degrees, both in machine learning, from Carnegie Mellon University in 2008 and 2009. His research interest is in large-scale data mining for graphs and multimedia. He received several awards, including NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper Award (2015), four best paper awards (TUP'14, CIKM'12, SDM'08, ICDM'06), six `bests of conference' (ICDM'18, KDD'16, SDM'15, ICDM'15, SDM'11 and ICDM'10), one best demo, honorable mention (SIGMOD'17), and one best demo candidate, second place (CIKM'17). He has published over 100 referred articles. He is the editor-in-chief of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Neurocomputing Journal (Elsevier); He has served as a program committee member in multiple data-mining, database, and artificial intelligence venues (including SIGKDD, SIGMOD, AAAI, WWW and CIKM).