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, Berkeley, Johns Hopkins, and other top universities.

The Computational Bioscience core and affiliated faculty work in a wide range of areas, including genomics, metabolomics, microbiome research, systems genetics, cancer research, image analysis, clinical research informatics, and many other fields that span the spectrum of biomedical research from the molecular to the clinical. They have appointments in numerous departments on three CU campuses, including Medicine, Pharmacology, Biostatistics and Informatics, Biochemistry & Molecular Genetics, Computer Science, and Bioengineering; including faculty from National Jewish Health.

Research Areas

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



Mazen Al Borno PhD

Assistant Professor
Without being conscious of it, our motor system is constantly solving computationally challenging problems in ways that astonish both roboticists and neuroscientists. We develop computational models of animal movement to understand how the brain generates movement and design novel rehabilitation therapies and assistive devices for patients with movement disorders. We collaborate closely with neuroscientists, clinicians and roboticists to study the brain and help patients achieve a better quality of life.

David Albers PhD

Associate Professor
​Dr. David Albers lab focuses on advancing biomedicine using: -data assimilation of both clinician and patient collected data to forecast physiology and compute new phenotypes -health care process modeling and analysis -temporal-focused spectral analysis and information theoretic tools -mechanistic physiologic models using clinical data -discovering phenotypes using temporal information within clinical data -visualizing patient health evolution in an intensive care setting -deconvolving biases present in clinical data -computational machinery based on variational inference to discover features that can be used to define phenotypes and other clinically actionable quantities

Tell Bennett MD

We focus on clinical decision-making, particularly in high-risk environments such as intensive care units, and on the development and implementation of informatics and data science methods and tools to improve outcomes.

James Costello PhD

Associate Professor Director, Pharmacology Program; Completed Upstander/Bystander Training, Mentor Training Course
Within the broad scope of systems biology, my lab focuses on 3 research areas: 1) Network inference for identifying drug targets, 2) Predicting drug sensitivity from -omics datasets, and 3) Modeling temporal effects of drug combinations.

Robin Dowell DSc

Professor Completed Mentor Training Course
Our primary interest is in understanding transcriptional regulation: how does it work, evolve, and respond to perturbations? To this end, we are leveraging a variety of experimental and computational approaches. In general, we seek to develop principled, biologically informed machine learning approaches to identify causal relationships within transcriptional regulatory networks.

Debashis Ghosh MS, PhD

Professor Completed Upstander/Bystander Training
My 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 Goerg PhD

Assistant Professor
Design, 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.

Casey Greene PhD

Professor Completed Upstander/Bystander Training, Maximizing Mentoring
The Greene lab focuses on performing open, reproducible, and inclusive research on topics at the intersection of machine learning, public data, and the transcriptome.

Katerina Kechris PhD

Professor Director, Computational Bioscience Program
Dr. Kechris’ research is focused on the development and application of statistical and computational methods for analyzing omics data sets through multiple stages of the data life cycle including processing, storage, analysis, modeling, and visualization. Specific areas include: (1) analyzing transcription factor binding and miRNA data to study regulation, (2) examining the genetic and epigenetic factors controlling gene expression, (3) exploring the metabolome and (4) integrating multi-omics data. She collaborates with investigators studying chronic obstructive pulmonary disease in the COPDGene genetic epidemiology study, substance use disorders using animal models, and early life determinants of diabetes and obesity in children.

Arjun Krishnan PhD

Associate Professor Completed Mentor Training Course, Upstander/Bystander Training
Our goal is to enable biomedical researches to effectively reuse massive collections of publicly-available data — e.g., omics, knowledgebases, unstructured text, genetic associations — to gain nuanced insights into the molecular mechanisms underlying heterogeneous traits and disease.

Ryan Layer PhD

Assistant Professor
The lab focuses on developing methods that can leverage population-scale datasets to understand better how genetic variation affects human health.

Catherine Lozupone PhD

Associate Professor
We combine bioinformatics and experimental work to understand the driving factors of human microbiota composition, host:microbe interactions, and the intersection with diet in a variety of disease contexts. Our work has a particular focus on HIV-positive and high HIV-risk populations, cancer and Clostridioides difficile infection.​

Christopher Miller PhD

Associate Professor
We develop and use bioinformatic and genome-enabled approaches to study microbial communities. With the advent of new sequencing technologies, one can sequence billions or trillions of base pairs of DNA for relatively little money. One of the most powerful applications of this new economy is the direct sequencing of microbial DNA and RNA from the environment. The combination of deep sequencing and bioinformatics reveals that even “simple” natural microbial communities are in fact quite complex. We are interested in understanding this complexity at a systems level for microbial communities relevant to study of the environment, health, and disease.

Milton Pividori PhD

Assistant Professor Completed Upstander/Bystander Training, DEI Training
Our ultimate goal is to advance precision medicine by developing a comprehensive, multi-omics approach to understanding the complex interplay between genetics and disease. Our research focuses on developing novel machine-learning methods to advance key computational aspects of precision medicine. We have a particular interest in the integration of genetic studies with relevant molecular patterns extracted from multi-omics data. For this, we aim to develop the next generation of methodologies that will consolidate large and heterogeneous sources of biomedical information to extract biological insight to improve human health.

David Pollock PhD

Protein, RNA, and other functional molecules that exist in living organisms are the product of millions of years of evolution. The substitutions that have occurred over the years had to have been compatible with the constraints of structure and function, and thus the evolutionary record provides critical data for understanding macromolecular structure/function/sequence relationships. In our laboratory, we use the techniques of molecular evolution, computational biology, and evolutionary genomics to exploit this record to make inferences about past biological events and to make testable predictions about the effects of mutations.

Antonio Porras Msc, PhD

Assistant Professor
Our team is working on the creation of normative statistical models of cranial growth that can be used as references to study developmental abnormalities quantitatively. Based on such models, we are creating new methods to identify and quantify abnormal developmental patterns associated with specific pathologies and genetic factors. Those methods will help us better understand developmental cranial disorders and create more informed and targeted treatments.

Janani Ravi PhD

Assistant Professor Completed Mentor Training Course, Equity Certificate, Upstander/Bystander Training, DEI Training
Dr. Janani Ravi is an Assistant Professor in the Dept. of Biomedical Informatics with ties to Dept. of Immunology and Microbiology. She completed her PhD in Computational Biology at Virginia Tech, postdoctoral research at the Rutgers Public Health Research Institute, and started her research group at Michigan State University prior to moving to CU in late 2022. Dr. Ravi’s research group, JRaviLab, develops general-purpose computational approaches that integrate large-scale heterogeneous public datasets for mechanistic understanding of microbial genotypes, phenotypes, and diseases. The JRaviLab asks: How do we link pathogen genotypes to phenotypes? How can host responses to infection inform disease mechanistics and therapeutics? Her group provides open data/software and easy-to-use web applications for biomedical researchers, and the methods are developed to be pathogen- and disease-agnostic. Dr. Ravi is currently supported by an NIH NIAID U01 (antimicrobial resistance prediction) and R21 (host responses, host-directed therapeutics), two CU CPMR-DBMI dyads (bone health, implant corrosion), Colorado Translational Research Scholar Program, and an NIH NLM T15 supporting her postdoc. Dr. Ravi is engaged and committed to mentoring/training, education, and outreach, and creating and sustaining diversity and inclusivity in data science for learners and professionals alike, focused on increasing the participation of underrepresented minorities in the field. She founded R-Ladies East Lansing and R-Ladies Aurora, and co-founded Women+ Data Science and AsiaR. She also co-chairs the R/Bioconductor Community Advisory Board.

Laura Saba PhD

Associate Professor Completed Mentor Training Course, Upstander/Bystander Training
We utilize and develop systems genetics tools to explore biological mechanisms responsible for disease.

Michael Strong PhD

Associate Professor
Dr. Strong’s research focuses on synergistic genomic, computational and molecular strategies to disease and disease pathogenesis. I am particularly interested in developing and applying computational methods to better generate, integrate and analyze genomic and proteomic information, with a focus on respiratory disease and disease pathogens, including Mycobacterium tuberculosis.

Anne Thessen PhD

Associate Professor
Linking phenomes, genomes, and exposomes using semantic technology.

Gregory Way PhD

Assistant Professor Completed Upstander/Bystander Training, Mentor Training Course
The mission of our lab is to reduce human suffering by integrating biomedical data science and software engineering into drug discovery by developing new computational methods, innovative approaches, assays, and software for analyzing high-dimensional genomic, molecular, and microscopy data with a focus on pediatric diseases, including pediatric cancer and Neurofibromatosis Type 1 (NF1).

Laura Wiley PhD

Assistant Professor
The Wiley Lab develops methods for using electronic health record data for clinical evidence generation and biomedical research in support of precision medicine. Ongoing projects include building and mining a clinical data repository to inform clinical management of intracranial aneurysms, developing tools to support abstraction of medical charts, and supporting informatics and data science needs of other collaborative projects in genomics and precision medicine. The Lab also has a strong emphasis on education, developing content to train clinical data scientists and research informaticians.

Fuyong Xing PhD

Assistant Professor
His research focuses on medical image computing, imaging informatics, and machine learning.

Fan Zhang PhD

Assistant Professor
The Zhang lab develops advanced statistical machine learning methods and systems immunology approaches for translational medicine. Specifically, we focus on 1) novel computational method development, 2) large single-cell multi-modal sequencing data integration, 3) cutting-edge systems immunology approaches, and 4) disease association modeling for translational medicine.
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