Full Product Details
Author: Liangyue Li ,
Hanghang Tong (University of Illinois, Urbana-Champaign)
Publisher: Cambridge University Press
Imprint: Cambridge University Press
Dimensions:
Width: 15.60cm
, Height: 1.30cm
, Length: 23.40cm
Weight: 0.350kg
ISBN: 9781108498548
ISBN 10: 110849854
Pages: 164
Publication Date: 03 December 2020
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Available To Order
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Reviews
'A comprehensive study that pushes forward our understanding of and ability to forecast and design team performance - a critical, yet complex human-subject phenomenon to which this book brings in-depth technical rigor.' Leman Akoglu, Carnegie Mellon University 'Li and Tong have provided a thorough and insightful exploration of current research on teams in networks, linking computational techniques with results from the social sciences. A pleasure to read.' Sucheta Soundarajan, Syracuse University 'This is a timely book for team science, with a unique perspective that uses computational approaches to study the network effect on team performance. The book has a nice balance of theory, algorithms, and empirical studies. The authors possess years of experience in the field.' Charu Aggarwal, IBM Research AI 'This pioneering book is essential to technologists, data scientists, and researchers alike, offering a modern, computational approach to the science of teaming and how to manage the convergence of people, information, and technology in networked organizations.' Norbou Buchler, US Army Data and Analysis Center
Author Information
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).
