Our Expertise and Leadership

Our outstanding faculty, staff, trainees and alumni have come to Colorado from around the world. Before joining our program, they trained at Harvard, Yale, Stanford, Johns Hopkins, and other top universities.

The Computational Bioscience core and affiliated faculty work in a wide range of areas, including biomedical text mining, protein structure simulations, RNA sequence and structure analysis, graphical models of protein interactions, and statistical analysis of regulatory sequences. They have appointments in numerous departments on three CU campuses, including Medicine, Pharmacology, Biometrics, Biochemistry & Molecular Genetics, Computer Science, CCTSI; and we have faculty from National Jewish Health.

Research Areas

  • Biological Visualization Data
  • Biomedical Ontology
  • Clinical Research Informatics
  • Computational Pharmacology
  • Gene Expression
  • Genomics
  • Metagenomics
  • Microbiome
  • Molecular Evolution
  • Natural Language Processing
  • Network Analysis
  • Neuroscience
  • Next Generation Sequence Analysis
  • Personalized Medicine
  • Statistical Methods in Genomics
  • Biostatistics
  • Text Mining
  • Translational Bioinformatics
  • Visual Analytics
  • Physical simulations of biological macromolecules and their dynamics

Core Faculty

Kevin Cohen, PhDEntity identification and normalization, information extraction, corpus linguistics, and computational lexical semantics, particularly in the molecular biology domain.
Jim Costello, PhDSystems and network biology approaches to disentangle signaling pathways in cancer development; Computational modeling of how therapeutic compounds function across different genomic backgrounds.
Robin Dowell-Deen, PhDApplication of machine learning, probabilistic models, and pattern recognition to large high throughput genomic datasets.
Debashis Ghosh, PhDMy research interests focus on the use of machine learning, multiple comparisons and multivariate statistical methods for the analysis of high-throughput data. I have focused most of my research on combining genomic data from either multiple datasets or from multiple studies. I have worked with transcriptomic, copy number, methylation and more recently, metabolomics and imaging data. Application areas I have worked in include cancer, diabetes, and ophthalmology. I have advised 12 Ph.D. students and one postdoctoral fellow in Biostatistics and Statistics on these topics. Students who would work with me in the future could expect to work on new analytical methods for large-scale datasets as well as begin to examine computational issues to enhance scalability.
Carsten Görg, PhDDesign, development, and evaluation of visualizations and visual analytics tools for supporting biologists in analyzing large and complex datasets. Also, application of text analytics research approaches to the biomedical literature.
Lawrence Hunter, PhDDevelopment and application of advanced computational techniques for biomedicine, particularly the application of statistical and knowledge–based techniques to the analysis of high–throughput data and of biomedical texts. Also, neurobiologically and evolutionarily informed computational models of cognition, and ethical issues related to computational bioscience.
Katerina Kechris, PhDDevelopment and application of statistical methods for analyzing molecular sequences and high throughput genomic data.
Ryan Layer, PhDInterests: Algorithms for human genome interpretation; Parallel and distributed architectures; Succinct data structures; Structural variation; Cancer genomics; Population genetics; Applications of genomics to clinical care
Sonia Leach, PhDResearch in the area of probabilistic graphical models and genomic data fusion, particularly for the tasks of gene prioritization, developing protein interaction networks and discovery of novel disease-gene associations.
Catherine Lozupone, PhDMicrobiology of the human gut and impacts on health. The development of bioinformatics techniques for analysis of marker gener and genomic sequence data.
Christopher Miller, PhDGenome-enabled and bioinformatic approaches to studying microbial communities. Novel experimental methods and computational algorithms for assembly and analysis of high-throughput sequencing data.
Tzu Phang, PhDDeveloping computational and statistical algorithms for analyzing microarray data, with special emphasis on time-course experimental design and alternative splicing detection. He works directly with scientists to translate biological concept into algorithm for rapid exploration and discovery. He is also interested in how best to share the vast bioinformatics and genomics resources between multi-disciplinary scientists.
David Pollock, PhDEvolutionary genomics and molecular evolution, particularly the interaction between protein sequence, structure, function, and molecular co-evolution within and between proteins.
Laura Saba, PhDMy research interests focus on developing and implementing systems genetics statistical models to complex traits/diseases.
Michael Strong, PhDSystems biology, genomics, and structural bioinformatics, particularly developing computational strategies to integrate genome sequence, gene expression, protein network, and protein structure information, with applications to respiratory diseases, including tuberculosis.
Laura Wiley, PhDComputational phenotyping and data science using electronic medical records. Pharmacogenomics discovery, translation, and clinical implementation.


Associated Faculty

Dan Denman, PhDThe Denman Lab's research is focused on elucidating the mechanisms of computation in the mammalian visual system. Towards this goal, the lab also develops neurotechnologies for recording from and modulating the mammalian brain at the spatial and temporal scales relevant to neural coding. To use these technologies, we develop movel algorithmic approaches to extracellular electrophysiology pre-processing, real-time data processing, and cross-modal image registration. We then apply these, and other, methods to studying distributed rodent visual system to attempt to understand the algorithm the brain is using to generate visual perception.
David Kao, MDTranslational bioinformatics, Personalization of progonsis and treatment plan, Heart failure/cardiomyopathy, Integration of public data resources, Pharmacovigilance.
Alon Poleg-Polsky, PhDWe seek to describe the mechanisms that enable neural circuits to efficiently detect, amplify and transmit relevant information under diverse physiological conditions.
Michael Rosenberg, MDI have research experience in basic science and epidemiology of cardiac arrhythmias, with a current focus on applications of quantitative methods to clinical care and health. My ongoing projects are related to use of genetics and machine learning to guide clinical decision making, as well as smartphone applications for individualized medicine.
James Sikela, PhDProfessor Sikela’s research interests are in the development and application of advanced genome technologies, particularly as they apply to understanding of human evolution and human disease.