PhD Program: Bioinformatics & Computational Biology
| Name | PhD Program | Research Interest | Publications |
|---|---|---|
| Miao, Yinglong WEBSITE PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
Our research is focused on the development of novel theoretical and computational methods and AI techniques, which greatly enhance computer simulations and facilitate simulation analysis, and the application of these methods, making unprecedented contributions to biomolecular modeling and drug discovery. In collaboration with leading experimental groups, we combine complementary simulations and experiments to uncover functional mechanisms and design drugs of important biomolecules, including G-protein-coupled receptors (GPCRs), membrane-embedded proteases, RNA-binding proteins, and RNA. At the interface of computational biology, chemistry, biophysics, bioinformatics and pharmacology, our research aims to address three major topics: (i) development of biomolecular enhanced sampling and AI techniques, (ii) multiscale computational modeling of critical cellular signaling pathways, and (iii) AI-driven drug discovery of medically important proteins and RNA for treatments of neurological disorders, heart failure and cancers. |
| Leiderman, Karin WEBSITE PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
I am a mathematical biologist interested in the biochemical and biophysical aspects of blood clotting and emergent behavior in biological fluid-structure interaction problems. I especially love mathematical modeling, where creativity, biological knowledge, and mathematical insight meet. My goal is to use mathematical and computational modeling as a tool to learn something new about a biological system, not just to simply match model output to experimental data. My research paradigm includes an integration of mathematical and experimental approaches, together with statistical analyses and inference, to determine mechanisms underlying complex biological phenomena. This paradigm culminates in the contextualization of my findings to both the mathematical and biological communities. My research program is focused mainly on studying the influence of biochemical and biophysical mechanisms on blood coagulation, clot formation, and bleeding. |
| Stanley, Natalie WEBSITE PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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 PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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 PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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. |
| Raffield, Laura WEBSITE PUBLICATIONS |
PHD PROGRAM |
Keywords: genetic epidemiology, human genetics, genome-wide association studies, precision medicine, multi-omics, cardiovascular disease, inflammation, hematological traits In my research program, I use human genomics and multi-omics to understand inherited and environmental risk factors for cardiometabolic diseases and related quantitative traits. I work to link genetic variants to function through integration with multi-omics data, including transcriptomic, methylation, proteomic, and metabolomic measures. This work has important implications for cardiometabolic risk prediction across diverse populations and improved understanding of disease biology. A focus on understudied African American and Hispanic/Latino populations is a central theme of my research; human genetics research is dramatically unrepresentative of global populations, with ~95% of genome-wide association study participants of European or East Asian ancestry. As complex trait genetics moves into the clinic, increasing diversity is essential to ensure that all populations benefit from the promise of precision medicine. I play a leadership role in collaborative efforts in human genetics, for example serving as a Genetics Working Group co-chair for the Jackson Heart Study (JHS), one of the largest population based studies of African Americans, and an Inflammation/Hematology working group co-chair for the Population Architecture Using Genomics and Epidemiology (PAGE) consortium. I am also a co-convener of the Multi-Omics working group for the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. |
| Brunk, Elizabeth WEBSITE PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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 PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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 PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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 PUBLICATIONS |
PHD PROGRAM RESEARCH INTEREST |
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. |