Yu Wang is an Assistant Professor in the Department of Computer and Information Science at the University of Oregon. Before that, he received his Ph.D. in the Computer Science Department at Vanderbilt University.

 

Yu conducts research in the areas of data mining and machine learning, with emphasis on network analysis, machine learning on graphs, and responsible AI for social good with applications in cyber-security, biochemistry, and information retrieval. He received numerous honors and awards including the sole recipient of Vanderbilt's Graduate Leadership Anchor Award for Research in 2023, the 2023-2024 Recipient of the Vanderbilt Outstanding Doctoral Student Award, the Best Paper Award in 2020 Smokey Mountain Data Challenge Competition by ORNL, first-author of Vanderbilt’s C.F.Chen Best Paper Award in 2022, first-author of the Best Paper Award at GLFrontiers Workshop at Neurips'23, Best Doctoral Forum Poster Runner-ups at SDM'24, along with two of his works being selected among the top-10 Most Influential CIKM'22 and WWW'23 Papers by Paper Digest. He actively contributed to top conferences/journals in the field of data mining and machine learning, both in terms of publishing such as ICLR, AAAI, KDD, WWW, CIKM, WSDM, TKDD, TIST and serving as a PC member/reviewer/organizer such as KDD, ICML, AAAI (ICWSM), WWW, WSDM, CIKM, TKDD, and TNNLS. He has contributed to the organization of workshops in WSDM'22/24 and the tutorial in SDM'24. During his Ph.D. study, he had two internships at The Home Depot and Adobe Research, focusing on building knowledge graphs to enhance the information retrieval.