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Dr. Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan - Dearborn. He is also an affiliated faculty member in the Michigan Institute for Data Science (MIDAS) at the University of Michigan. He earned his Ph.D. in Computer Science from the University of Waterloo (Canada) in 1995, specializing in database systems. He also holds an M.Eng. and a B.Sc. from the Southeast University (China) and an M.Sc. from the McMaster University (Canada).
Dr. Zhu has been honored as ACM Distinguished Scientist, IBM CAS Faculty Fellow, and IEEE Senior Member. He was a visiting scientist at the IBM Toronto Laboratory, conducting research on query optimization and multidatabase systems. He was a lecturer at the Southeast University in China, teaching and supervising students at both undergraduate and graduate levels. He was an instructor at the University of Waterloo, teaching a database course.
Dr. Zhu has many years research experience in the database field. He has extensive experience in developing centralized/distributed database systems. He has been involved in research projects in various areas, including query optimization for advanced database systems, multidimenstional non-ordered discrete data indexing, Big Data processing and analytics, k-nearest neighbor search in complex data spaces, spatio-temporal data indexing, streaming data processing, self-managing databases, distributed databases, data/information integration, Web information management, data mining, and bioinformatics. His research has been funded by highly competitive external and internal sources, including NSF, IBM, Ford, OVPR, Rackham, REEDF, and CEEP. He was also a recipient of 2013 ACM Distinguished Scientist Award, 2004 IBM Faculty Award, 2003 IBM Faculty Award, 2001 UMD Distinguished Faculty Research Award, 1999/2000 IBM University Partnership Research Award, and 1988 Scientific Progress Award of PRC Education Commission, as well as a number of best paper awards (including The Wilkes Award 2008 from The British Computer Society), recognizing research excellence and outstanding contributions in his field. He has published over 100 research papers in various journals and conference proceedings including ACM Transactions on Database Systems (TODS), ACM Transactions on Information Systems (TOIS), IEEE Transactions on Knowledge and Data Engineering (TKDE), The VLDB Journal (VLDBJ), Information Systems (IS), Data & Knowledge Engineering (DKE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Computers (TC), and Proceedings of the VLDB Endowment (PVLDB). His research work was cited by numerous research papers and included in several well-known database research/text books. Dr. Zhu was invited to give research presentations at many universities and industrial research centers/labs. He has served as an Editor-in-Chief, Associate Editor and Editorial Board member for several international technical journals including Information Sciences (Elsevier) and Journal of Big Data (Springer). He also served as a program/organizing committee member and a track/workshop/session chair for many international research conferences. He was a referee for numerous journals and conferences.
Dr. Zhu has been honored as ACM Distinguished Scientist, IBM CAS Faculty Fellow, and IEEE Senior Member. He was a visiting scientist at the IBM Toronto Laboratory, conducting research on query optimization and multidatabase systems. He was a lecturer at the Southeast University in China, teaching and supervising students at both undergraduate and graduate levels. He was an instructor at the University of Waterloo, teaching a database course.
Dr. Zhu has many years research experience in the database field. He has extensive experience in developing centralized/distributed database systems. He has been involved in research projects in various areas, including query optimization for advanced database systems, multidimenstional non-ordered discrete data indexing, Big Data processing and analytics, k-nearest neighbor search in complex data spaces, spatio-temporal data indexing, streaming data processing, self-managing databases, distributed databases, data/information integration, Web information management, data mining, and bioinformatics. His research has been funded by highly competitive external and internal sources, including NSF, IBM, Ford, OVPR, Rackham, REEDF, and CEEP. He was also a recipient of 2013 ACM Distinguished Scientist Award, 2004 IBM Faculty Award, 2003 IBM Faculty Award, 2001 UMD Distinguished Faculty Research Award, 1999/2000 IBM University Partnership Research Award, and 1988 Scientific Progress Award of PRC Education Commission, as well as a number of best paper awards (including The Wilkes Award 2008 from The British Computer Society), recognizing research excellence and outstanding contributions in his field. He has published over 100 research papers in various journals and conference proceedings including ACM Transactions on Database Systems (TODS), ACM Transactions on Information Systems (TOIS), IEEE Transactions on Knowledge and Data Engineering (TKDE), The VLDB Journal (VLDBJ), Information Systems (IS), Data & Knowledge Engineering (DKE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Computers (TC), and Proceedings of the VLDB Endowment (PVLDB). His research work was cited by numerous research papers and included in several well-known database research/text books. Dr. Zhu was invited to give research presentations at many universities and industrial research centers/labs. He has served as an Editor-in-Chief, Associate Editor and Editorial Board member for several international technical journals including Information Sciences (Elsevier) and Journal of Big Data (Springer). He also served as a program/organizing committee member and a track/workshop/session chair for many international research conferences. He was a referee for numerous journals and conferences.
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2023 IEEE/ACM SYMPOSIUM ON EDGE COMPUTING, SEC 2023pp.313-318, (2023)
2022 IEEE Global Engineering Education Conference (EDUCON)pp.148-156, (2022)
user-62626388733dcd7b976fc01e(2020)
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