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 (now Radboud University) 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. He and his colleagues developed statistical approaches for cancer classification, diagnosis, and prognosis, and applied them to the study of metastasis, cancer progression, and tumor heterogeneity for multiple different cancer types. He co-developed the model class of probabilistic Boolean networks (PBNs), which has been applied to the study of gene regulatory networks in cancer.
Dr. Shmulevich joined the ISB faculty in 2005 where he is currently a Professor. Dr. Shmulevich directed a Genome Data Analysis Center within The Cancer Genome Atlas (TCGA) consortium. He also directed one of three NCI Cancer Genomics Cloud Pilots, which is now operating as an NCI Cancer Genomics Cloud Resource (isb-cgc.org).
Dr. Shmulevich’s research interests 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. His main research interest is multiscale modeling for cancer therapy.
Dr. Shmulevich is a co-author or co-editor 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 and has held affiliate appointments in the Department of Signal Processing in Tampere University of Technology, Finland and in the Department of Electronic and Electrical Engineering in Strathclyde University, Glasgow, UK.
- Computational Systems Biology
- Cancer Research, Immuno-oncology
- Signal and Image Processing
- Complex Systems Theory
- Computational Learning Theory, Machine Learning
- Multiscale Theory and Analysis
- Complexity of Algorithms
- Music Pattern Recognition
- Cognition, Perception, and Mathematical Psychology
PhD, Electrical and Computer Engineering, Purdue University, 1997
W. Zhang and I. Shmulevich, Eds., Computational And Statistical Approaches To Genomics, Kluwer Academic Publishers, Boston, 2002; 2nd Edition, Springer, 2006.
W. Zhang, I. Shmulevich, J. Astola, Microarray Quality Control, Wiley and Sons, March 2004.
E. R. Dougherty, I. Shmulevich, J. Chen, Z. J. Wang, Eds. Genomic Signal Processing and Statistics, EURASIP Book Series on Signal Processing and Communications, Hindawi Publishing Corp., 2005.
I. Shmulevich and E. R. Dougherty, Genomic Signal Processing, Princeton University Press, 2007.
I. Shmulevich and E. R. Dougherty, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks, SIAM Press, 2009.