Dr. Kang's primary research interests are in developing statistical methods for large-scale complex biomedical data with application in precision medicine, imaging, epidemiology and genetics. Areas of Expertise: Aging, Biostatistics, Child Health, COVID-19, Health Informatics, Infectious Disease, Mental Health, Precision Health, Substance Use.

Jian Kang joined the University of Rochester in August 2023. He received Ph.D. in Computer Science at the University of Illinois at Urbana–Champaign. His research interests lie in data mining and machine learning, trustworthy artificial intelligence, and computational social science.

Effect of antiplatelet therapy and platelet function testing on hemorrhagic and thrombotic complications in patients with cerebral aneurysms treated with the pipeline … SL Skukalek, AM Winkler, J...

Associate Chair for Research. Professor, Biostatistics.

Jian Kang. Address: 3011 Wegmans Hall, University of Rochester, Rochester, NY 14627. Email: jian [dot] kang [at] rochester [dot] edu. I am an Assistant Professor in the Department of Computer Science at the University of Rochester (UR). I am also …

Jian Kang is a Professor in the Department of Biostatistics and is a faculty member of the Kidney Epidemiology and Cost Center (KECC) at the University of Michigan. He received his Ph.D. in Biostatistics from the University of Michigan in 2011.

Jian Kang. View Jian Kang's Faculty Profile. Summary. Dr. Kang is interested in developing new statistical methods for analysis of big data in global health. My particular areas of expertise include imaging, metabolomics and precision medicine. Topics. big data; statistical methods/applications; teaching; Countries. Australia; China; Browse/Search

Jian Kang. Associate Professor, Biostatistics. Bayesian methods, composite likelihood approach and missing data problems, efficient statistical computation algorithms, graphical models, latent source separation methods, network inference, ultrahigh-dimensional feature selection

Albert Arendt Hopeman Professor of Engineering, University of Rochester. Proceedings of the 26th ACM SIGKDD international conference on knowledge … Do we really need complicated model architectures...