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NameEmailPhD ProgramResearch InterestPublications
Vincent, Benjamin
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Microbiology & Immunology

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Computational Biology, Genomics, Immunology, Systems Biology, Translational Medicine

The Vincent laboratory focuses on immunogenomics and systems approaches to understanding tumor immunobiology, with the goal of developing clinically relevant insights and new cancer immunotherapies.  Our mission is to make discoveries that help cancer patients live longer and better lives, focusing on research areas where we feel our work will lead to cures. Our core values are scientific integrity, continual growth, communication, resource stewardship, and mutual respect.

Won, Hyejung
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Genetics & Molecular Biology, Neuroscience

RESEARCH INTEREST
Bioinformatics, Genetics, Genomics, Molecular Biology, Neurobiology

We try to bridge the gap between genetic risk factors for psychiatric illnesses and neurobiological mechanisms by decoding the regulatory relationships of the non-coding genome. In particular, we implement Hi-C, a genome-wide chromosome conformation capture technique to identify the folding principle of the genome in human brain. We then leverage this information to identify the functional impacts of the common variants associated with neuropsychiatric disorders.

Griffith, Boyce
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Cardiovascular Biology, Computational Biology, Organismal Biology, Physiology, Quantitative Biology, Systems Biology, Translational Medicine

My group develops and deploys computational tools to predict physiological function and dysfunction. We are interested in a range of applications in medicine and biology, but our primary focus is the cardiovascular system. My group is actively developing fluid-structure interaction (FSI) models of the heart, arteries, and veins, and of cardiovascular medical devices, including bioprosthetic heart valves, ventricular assist devices, and inferior vena cava filters. We are also validating these models using in vitro and in vivo approaches. We also model cardiac electrophysiology and electro-mechanical coupling, with a focus on atrial fibrillation (AF), and aim to develop mechanistically detailed descriptions of thrombosis in AF. This work is carried out in collaboration with clinicians, engineers, computer and computational scientists, and mathematical scientists in academia, industry, and regulatory agencies.

Schrider, Daniel
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Evolutionary Biology, Genetics, Genomics

The Schrider Lab develops and applies computational tools to use population genetic datasets to make inferences about evolutionary history. Our research areas include but are not limited to: characterizing the effects natural selection on genetic variation within species, identifying genes responsible for recent adaptation, detecting genomic copy number variants and other weird types of mutations, and adapting machine learning tools for application to questions in population genetics and evolution. Study organisms include humans, the fruit fly Drosophila melanogaster and its relatives, and the malaria vector mosquito Anopheles gambiae.

Hoadley, Katherine A.
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Genetics & Molecular Biology

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Computational Biology, Genetics, Genomics

My research interest is in genomic characterization and integrative genomic approaches to better understand cancer. My group is part of the NCI Genome Data Analysis Center focused on RNA expression analysis. We have a number of ongoing projects including developing molecular classifications for potential clinical utility, developing methods for deconvolution to understand bulk tissue heterogeneity, analysis of driver negative cancers, and analysis of ancestry markers with cancer features.

Ward-Caviness, Cavin
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Toxicology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genomics, Systems Biology, Translational Medicine

We are actively engaged in multiple research arenas centered around understanding the associations between environmental exposures (primarily air pollution) and health outcomes. We use large clinical cohorts and electronic health records to understand associations between air pollution and health outcomes such as cardiovascular disease, metabolic disease, and aging. We use metabolomics and epigenetic data (primarily DNA methylation) to investigate molecular mechanisms, and highlight the integration of ‘omics data in a systems biology framework to better understand dysregulated pathways. Finally, we have projects centered around methods development and causal analyses to improve our understanding of the biology central to environmental health effects.

Liu, Yufeng
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology

Statistical machine learning and data mining, nonparametric statistics and functional estimation, bioinformatics, design and analysis of experiments

 

Wu, Di
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Computational Biology, Genomics, Translational Medicine

Our group develops novel statistical bioinformatics tools and applies them in biomedical research to help understanding the precision medicine for cancer (e.g., breast cancer and lung cancer) subtypes, the disease associated integrative pathways across multiple genomic regulatory levels, and the genetics based drug repurposing mechanisms. Our recent focus includes pathway analysis, microbiome data analysis, data integration and electronica medical records (EMR). Our application fields include cancer, stem cell, autoimmune disease and oral biology. In the past, we have developed gene set testing methods with high citations, in the empirical Bayesian framework, to take care of small complex design and genewise correlation structure. These have been widely used in the microarray and RNAseq based gene expression analysis. Contamination detection for data analysis for Target DNA sequencing is work in progress. Recently, we also work on single cell sequencing data for pathway analysis with the local collaborators.

Gordon-Larsen, Penny
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Nutrition, Pathobiology & Translational Science

RESEARCH INTEREST
Behavior, Bioinformatics, Cardiovascular Biology, Genetics, Molecular Biology

Gordon-Larsen’s work integrates biology, behavior, and environment to understand, prevent and treat obesity, cardiovascular and cardiometabolic diseases. She works with biomarker, microbiome, metabolome, genetic, weight, diet, and environment data using multilevel modeling and pathway-based analyses. She works with several longitudinal cohorts that span more than 30 years. Most of her work uses data from the US and China. Her research teams include a wide variety of scientists working in areas such as genetics, medicine, bioinformatics, biostatistics, microbiology, nutrition, and epidemiology.

Gomez, Shawn
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Cancer Biology, Cell Signaling, Computational Biology, Systems Biology

Our primary research is in the area of computational systems biology, with particular interest in the study of biological signaling networks; trying to understand their structure, evolution and dynamics. In collaboration with wet lab experimentalists, we develop and apply computational models, including probabilistic graphical and multivariate methods along with more traditional engineering approaches such as system identification and control theory, to current challenges in molecular biology and medicine. Examples of recent research projects include: prediction of protein interaction networks, multivariate modeling of signal transduction networks, and development of methods for integrating large-scale genomic data sets.