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Dr. Rui Kuang is Assistant Professor in Computer Science and Engineering. His research interests span computational biology, biomedical informatics, machine learning and health informatics. He specilizes in machine learning algorithms and network analysis methods for understanding the association between various genomic characteristics and phenotypes from high-throughput genomic sequences or array data. Dr. Kuang's lab has designed several graph-based learning algorithms and kernel methods to learn accurate predictive models and essential features to characterize and predict phenotypes. His current projects center around cancer genomics, disease phenome-genome associaiton, and analysis of protein structures and functions. He has co-authored refereed publications for various journals and conferences including Bioinformatics, BMC Bioinformatics, Journal of Bioinformatics and Computational Biology, Journal of Machine Learning Research, Genetica, The FEBS Journal, Pacific Symposium on Biocomputing (PSB), Computational Systems Bioinformatics Conference (CSB), Conference on Learning Theory and Kernel Workshop (COLT), SIAM International Conference on Data Mining (SDM) and IEEE International Conference on Data Mining (ICDM). Dr. Kuang received his PhD in computer science from Columbia University in 20
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Charles Broadbent, Sharada Kadaba Sridhar, Rishabh Gupta, Brett Sterk,Michael Koller,Rui Kuang,Uzma Samadani
Neurosurgeryno. Supplement_1 (2024): 67-68
Nature Communicationsno. 1 (2023): 1-17
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Plant phenomics (Washington, D.C.) (2020): 1969142-1969142
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