A Short Biography
Assistant Professor Kuang joined the department in the fall of 2006 and specializes in computational biology, biomedical informatics and machine learning. Kuang is interested in formulating novel machine-learning algorithms to extract and integrate subtle and elusive information hiding in high-throughput biological data for understanding the association between genomic characteristics and phenotypes. Dr. Kuang's lab has designed several novel theoretically principled graph-based learning algorithms and kernel methods for a unified analysis of the high-throughput data in a data-driven perspective: learn accurate predictive models and essential key elements to characterize and predict phenotypes. His current projects center around cancer genomics, comparative genome annotation and protein analysis. 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, 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 2006.