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NameEmailPhD ProgramResearch InterestPublications
Stanley, Natalie
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Immunology, Medical Imaging, Vaccine Development

We are a computational biology lab jointly located between the department of computer science and the computational medicine program. We develop new methods for automated, efficient, and unbiased analysis of immune profiling data, such as, flow cytometry, mass cytometry, and imaging mass cytometry. Our work specifically seeks to link particular immune cell-types and their functional responses to clinical or experimental phenotypes. Application areas of interest include, vaccine development, T-cell differentiation and designing more effective immunotherapies, neurodegenerative diseases, sexually transmitted diseases, and pregnancy. To design scalable and automated tools for these data, we develop and apply new methods using machine learning and graph signal processing.

Johri, Parul
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Computational Biology, Evolutionary Biology, Genomics

Our research interests broadly span population genetics, statistical inference, and evolutionary genomics. We are interested in how evolutionary processes like changes in population size, recombination, mutation, selection and factors such as genome architecture shape patterns of genomic variation. Work in the lab involves employing computational and theoretical approaches, statistical method development, or using an empirical approach to perform evolutionary inference and ask fundamental questions in population genetics.

Wang, Jeremy
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

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

Our research focuses on long-read (single-molecule) sequencing and informatics. We develop novel methods to enable more efficient *omic analysis and apply carefully architected high-performance computing approaches to improve the utility of genomics in studies of human diseases, including infectious disease, cancer, and GI. Ongoing work includes genomic epidemiology of SARS-CoV-2, MPXV, and antibiotic resistance; classification of pediatric leukemias and solid tumors in low-resource settings using nanopore transcriptome sequencing; and metagenomics/metataxonomics of mucosa-associated microbiota in inflammatory bowel diseases.

Nazockdast, Ehssan
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Applied Physical Sciences

RESEARCH INTEREST
Biomaterials, Biophysics, Cell Biology, Computational Biology

We are interested in the physics of soft and squishy materials, especially the organization and mechanics of living cellular materials. We use theory and simulation in close collaboration with experiments to understand the complex structural and mechanical behavior of these systems. These questions and our approach to them are interdisciplinary and intersect several traditional fields, including cell biology, biophysics, fluid dynamics and applied mathematics.

Superfine, Richard
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Applied Physical Sciences, Biomedical Engineering

RESEARCH INTEREST
Biomaterials, Biophysics, Cell Biology, Computational Biology, Systems Biology

Superfine’s group studies stimulus-responsive active and living materials from the scale of individual molecules to physiological tissues, including DNA, cells and microfluidic-based tissue models. We develop new techniques using advanced optical, scanning probe, and magnetic force microscopy. We pursue diverse physiological phenomena from cancer to immunology to mucus clearance in the lung. Our work includes developing systems that mimic biology, most recently in the form of engineered cilia arrays that mimic lung tissue while providing unique solutions in biomedical devices.

Brunk, Elizabeth
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Chemistry, Pharmacology

RESEARCH INTEREST
Biochemistry, Bioinformatics, Biophysics, Cancer Biology, Computational Biology, Genomics, Pharmacology, Structural Biology, Systems Biology, Translational Medicine

A growing body of work in the biomedical sciences generates and analyzes omics data; our lab’s work contributes to these efforts by focusing on the integration of different omics data types to bring mechanistic insights to the multi-scale nature of cellular processes. The focus of our research is on developing systems genomics approaches to study the impact of genomic variation on genome function. We have used this focus to study genetic and molecular variation in both natural and engineered cellular systems and approach these topics through the lens of computational biology, machine learning and advanced omics data integration. More specifically, we create methods to reveal functional relationships across genomics, transcriptomics, ribosome profiling, proteomics, structural genomics, metabolomics and phenotype variability data. Our integrative omics methods improve understanding of how cells achieve regulation at multiple scales of complexity and link to genetic and molecular variants that influence these processes. Ultimately, the goal of our research is advancing the analysis of high-throughput omics technologies to empower patient care and clinical trial selections. To this end, we are developing integrative methods to improve mutation panels by selecting more informative genetic and molecular biomarkers that match disease relevance.

Ferris, Marty
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Genetics & Molecular Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genetics, Genomics, Immunology, Pathogenesis & Infection, Systems Biology, Virology

In the Ferris lab, we use genetically diverse mouse strains to better understand the role of genetic variation in immune responses to a variety of insults. We then study these variants mechanistically. We also develop genetic and genomic datasets and resources to better identify genetic features associated with these immunological differences.

Love, Michael
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genetics, Genomics

The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in molecular and cellular phenotypes.

Rubinsteyn, Alex
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology

RESEARCH INTEREST
Bioinformatics, Computational Biology, Genomics, Immunology, Translational Medicine, Virology

I work on predicting the determinants of adaptive immune responses. Most of my work has focused on T-cell epitope prediction for mutant antigens derived from cancer. I have collaborated closely with clinical groups to translate this work in personalized cancer vaccine trials. More recently I have also been working on joint T-cell and B-cell prediction for viral pathogens. The technologies and techniques applied across all of my projects are at the intersection of computational immunology, genomics, and machine learning.

Rau, Christoph
WEBSITE
EMAIL
PUBLICATIONS

PHD PROGRAM
Bioinformatics & Computational Biology, Cell Biology & Physiology, Genetics & Molecular Biology, Pathobiology & Translational Science

RESEARCH INTEREST
Bioinformatics, Cardiovascular Biology, Computational Biology, Genetics, Genomics, Molecular Biology, Systems Biology, Translational Medicine

Heart failure is an increasingly prevalent cause of death world-wide, but the genetic and epigenetic underpinnings of this disease remain poorly understood. Our laboratory is interested in combining in vitro, in vivo and computational techniques to identify novel markers and predictors of a failing heart. In particular, we leverage mouse populations to perform systems-level analyses with a focus on co-expression network modeling and DNA methylation, following up in primary cell culture and CRISPR-engineered mouse lines to validate our candidate genes and identify potential molecular mechanisms of disease progression and amelioration.