FACULTY & RESEARCH
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
- Cell Image Analysis
- 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
| Name | Research |
|---|---|
| David Albers, PhD | I develop and use computational methods including machine learning, data assimilation, and control theoretic methods to solve clinically motivated problems such as computational phenotyping, clinical decision support, human-centered AI for decision support, and basic physiological mechanics related to clinical problems. My work is primarily focused within endocrinology, acute respiratory distress syndrome and lung-ventilator dynamics, neurology and brain injury in the acute care setting, female reproductive endocrinology, nursing documentation, and the use of electronic health record data. |
| Mazen Al Borno, PhD | Computational modeling and simulation of animal movement to understand how the brain generates movement and to use this understanding to develop technology to help patients with movement disorders achieve a better quality of life. |
| Tell Bennett, MD, MS | Clinical decision-making in high-risk environments such as intensive care units and the development and implementation of informatics and data science methods and tools to improve outcomes. |
| Kevin Cohen, PhD | Entity identification and normalization, information extraction, corpus linguistics, and computational lexical semantics, particularly in the molecular biology domain. |
| Jim Costello, PhD | Systems and network biology approaches to disentangle signaling pathways in cancer development; Computational modeling of how therapeutic compounds function across different genomic backgrounds. |
| Sean Davis, MD, PhD | Biomedical Informatics & Data Science; Software Development; Open Data; Reproducible Research; Education; Cloud & Distributed Computing |
| Robin Dowell-Deen, PhD | Application of machine learning, probabilistic models, and pattern recognition to large high throughput genomic datasets. |
| Debashis Ghosh, PhD | 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 Görg, PhD | 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 | We develop machine learning approaches that integrate large collections of public data to model and understand complex biological systems. We investigate many different biological conditions, including those with limited data, such as rare diseases. Our approach to research prioritizes transparency and reproducibility. |
| Melissa Haendel, PhD | While many biological and clinical disciplines create a plethora of data, few do so with their combined use in mind. The Translational and Integrative Science Laboratory (Tislab) has a belief that societal problems can be solved by bringing together people and their data across disciplinary divides. Towards these ends, our laboratory is a diverse group spanning fields such as medicine, cell biology, computer science, bioinformatics, oceanography, comparative genomics, and public health. Tislab develops semantic engineering methods that allow data integration and inference of new knowledge across disciplines and heterogeneous data. We work on many standards and open science initiatives with the idea that such collaborations lead to innovation and help expedite science. Our research is used for rare disease diagnostics, implementation of platforms and tools for translational research, and open and reproducible science to help weave together healthcare, basic research, and patient engagement to make science go faster, better, together. |
| Lawrence Hunter, PhD | Development 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, PhD | Development and application of statistical methods for analyzing molecular sequences and high throughput genomic data. |
| Ryan Layer, PhD | Interests: 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, PhD | Research 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, PhD | Microbiology of the human gut and impacts on health. The development of bioinformatics techniques for analysis of marker gener and genomic sequence data. |
| Christopher Miller, PhD | Genome-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, PhD | Developing 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, PhD | Evolutionary genomics and molecular evolution, particularly the interaction between protein sequence, structure, function, and molecular co-evolution within and between proteins. |
| Antonio Porras, PhD | Development of medical image computing, machine learning and statistical modeling methods for the study and diagnosis of medical conditions, with special interest on pediatric and developmental pathologies. |
| Laura Saba, PhD | My research interests focus on developing and implementing systems genetics statistical models to complex traits/diseases. |
| Michael Strong, PhD | Systems 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. |
| Gregory P. Way, PhD | We are a biomedical data science lab with a mission to reduce human suffering. We develop methods and approaches using data science, machine learning, software engineering, cell image analysis, and multi-modal data integration to improve clinical decision making and enhance drug discovery. We are currently building cell biology as the next generation systems biology readout of cell state. |
| Laura Wiley, PhD | Computational phenotyping and data science using electronic medical records. Pharmacogenomics discovery, translation, and clinical implementation. |
| Fuyong Xing, PhD | Development and application of machine/deep learning algorithms for biomedical image analysis. |
Associated Faculty
| Name | Research |
|---|---|
| Dan Denman, PhD | The 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, MD | Translational bioinformatics, Personalization of progonsis and treatment plan, Heart failure/cardiomyopathy, Integration of public data resources, Pharmacovigilance. |
| Alon Poleg-Polsky, PhD | We seek to describe the mechanisms that enable neural circuits to efficiently detect, amplify and transmit relevant information under diverse physiological conditions. |
| Michael Rosenberg, MD | I 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, PhD | Professor 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. |