Ilya Shmulevich received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1997. His graduate research was in the area of nonlinear signal processing, with a focus on the theory and design of nonlinear digital filters, Boolean algebra, lattice theory, and applications to music pattern recognition. From 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition, focusing on tonality induction and rhythm complexity. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. While in Tampere, he did research in nonlinear systems, image recognition and classification, image correspondence, computational learning theory, multiscale and spectral methods, and statistical signal processing.
This background proved to be fruitful for undertaking problems in computational biology at a time when genomic technologies were beginning to produce large amounts of data. In 2001, he joined the Department of Pathology at The University of Texas M. D. Anderson Cancer Center as an Assistant Professor and held an adjunct faculty appointment in the Department of Statistics in Rice University. His work in cancer genomics research spans multiple cancers, with published work in glioma, lymphoma, leukemia, breast cancer, ovarian cancer, and sarcoma. He and his colleagues developed statistical approaches for cancer classification, diagnosis, and prognosis, and applied them to the study of of metastasis, cancer progression, and tumor heterogeneity. Together with long-standing collaborators Edward R. Dougherty (Texas A&M University) and Wei Zhang (M.D. Anderson Cancer Center), he co-developed the model class of probabilistic Boolean networks (PBNs), which was applied to the study of gene regulatory networks in cancer.
Dr. Shmulevich joined the ISB faculty in 2005 where he is currently a Professor. He directs a Genome Data Analysis Center as part of The Cancer Genome Atlas (TCGA) project, a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. He also directs the Computational Core of the Systems Approach to Immunity and Inflammation consortium, which consists of a large multidisciplinary team of investigators working in the fields of immunology and systems biology. These projects entail the development of computational and mathematical approaches for modeling biological systems and analyzing large-scale measurement data sets. Dr. Shmulevich’s research interests also include theoretical studies of complex systems, including information theoretic approaches, as well as the application of image processing and analysis to high-throughput cellular imaging.
Dr. Shmulevich is a co-editor or co-author of six books in the areas of computational biology. He holds Affiliate Professor appointments in the Departments of Bioengineering and Electrical Engineering at the University of Washington, Department of Signal Processing in Tampere University of Technology, Finland, and Department of Electronic and Electrical Engineering in Strathclyde University, Glasgow, UK.
Computational biology, signal and image processing
PhD, Electrical and Computer Engineering Purdue University, 1997