FACULTY & RESEARCH

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.

​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

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.

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.

We are interested in how populations of neurons generate sensory perceptions. We use quantitative psychophysics, in vivo electrophysiology and neurostimulation, in vivo imaging, circuit tracing, optogenetics, and computational methods to study and manipulate the dynamics of populations of single neurons. We are also focused on how neural interactions specifically - within areas and distributed across the brain - generate successful perceptions.

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.

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.

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.

The Greene lab focuses on performing open, reproducible, and inclusive research on topics at the intersection of machine learning, public data, and the transcriptome.

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.

Our group develops machine learning (ML)- and AI-based methods and tools that take advantage of diverse and massive public data collections to gain nuanced insights into the genetic, molecular, and cellular basis of complex diseases. The data collections we work with include genotype-phenotype associations, bulk and single-cell omics profiles, genome-scale molecular networks, unstructured text corpora, and ontologies/knowledgebases. Our methods and tools enable biomedical researchers to reuse these public data effectively, seamlessly, and at scale to generate novel data-driven hypotheses about a variety of biomedical contexts. We are also passionate about rapid and open science, well-rounded professional training, and creating diverse and inclusive learning environments.

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

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.​

Dr. James Mitchell's research encompasses AI, HCI, UCD, mobile applications, and wearables. His studies investigate augmented reality applications for public engagement and user-centered design for clinical guidelines on mobile devices. He measures healthcare professionals' experiences with AI technologies and leverages allied health data to improve AI applications, focusing on social determinants of health and intersectionality. Additionally, Dr. Mitchell designs user-centered solutions and explores the potential of large language models (LLMs) in both clinical settings and user-centered design studies. His work aims to advance healthcare technology through innovative AI solutions and user-centered design methodologies.

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.

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.

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.

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.

We utilize and develop systems genetics tools to explore biological mechanisms responsible for disease.

The overarching theme of my research program is to develop clinical and patient decision support tools to aid the decision-making process of healthcare professionals and patients in disease management. I aim to develop such tools in several settings, including the glycemic management of type 2 diabetes patients and intensive care unit patients and treatment outcome prediction for cancer patients. For these purposes, I use many tools, from knowledge-based physiological mechanistic models to purely data-driven machine-learning models.

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.

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).

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.

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

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.