Members

Barbara Mann is an Associate Professor in the Division of Infectious Diseases and International Health, Department of Medicine. Dr. Mann’s research expertise is in the pathogenicity of bacteria and parasitic diseases. Dr. Mann has worked extensively on the virulence mechanisms of the tropical enteric parasite Entamoeba histolytica. Her work on this parasite has included characterization of the major adhesin that mediates host attachment, vaccine studies, and work that led to the development of a diagnostic test. Dr. Mann, along with Dr. William Petri, received UVA’s Inventor of the year award for the invention that led to the production of a commercially available diagnostic kit for E. histolytica infection. Dr. Mann is co-director of two NIH training grants, Infectious Diseases and Biodefense and Emerging Infections, which support predoctoral and post-doctoral trainees. Dr. Mann is also chair of the Institutional Biosafety Committee, which is responsible for approving all research involving biohazardous material at UVA and has expertise in biosafety level three protocols and containment.

Achla Marathe is a Professor at the Biocomplexity Institute, and at the Department of Public Health Sciences, School of Medicine, at the University of Virginia. She is a member of the Networks, Systems Science and Advanced Computing Division at the Biocomplexity Institute. Achla Marathe received her BA (Honors) in Economics from Delhi University, India, and a MS and PhD in Economics from the University at Albany, New York. Her research provides insights in controlling infectious disease outbreaks, identifying vulnerabilities and dependencies in infrastructural networks, and in planning for natural and human-initiated disasters. She has been using data analytics and data mining tools to address a variety of social and economic problems that include designing interventions to reduce health disparities; understanding feedback and coevolution between health behaviors and disease spread; social and economic effectiveness of interventions; and allocation of scarce medical resources. She develops computational models that measure economic and social impact of actions by decision-makers, individuals or cohorts, and provide ways to predict how policy will influence human behavior and vice versa. Before joining UVA, she was a Professor at Virginia Tech and the Lead Economist and Social Scientist at the Network Dynamics and Simulation Science Laboratory at the Virginia Bioinformatics Institute. Prior to her tenure at Virginia Tech, she worked at the Los Alamos National Laboratory for ten years, initially as a postdoctoral fellow and then as a technical staff member. She also served as a consultant at the World Bank.
 

Joann M. McDermid is an Assistant Professor in the Division of Infectious Diseases and International Health in the School of Medicine. As a registered dietitian, Dr. McDermid's research interests are focused on understanding complex relationships between malnutrition and infection diseases in a global context. Dr. McDermid has lived in The Gambia where she conducted research on host iron metabolism as a determinant of HIV-1 and HIV-2 mortality and tuberculosis susceptibility. Dr. McDermid has led studies characterizing the evolution of mature breast milk immunology, the consequences of maternal and fetal inflammation on growth outcomes, as well as identifying nutrition-related and other risk factors for maternal and early infancy Cryptosporidium infection using a maternal-infant mixed HIV-status cohort she established in the Kisesa region of northwestern Tanzania in conjunction with collaborators at the Mwanza Research Centre of the Tanzanian National Institute for Medical Research and the London School of Hygiene & Tropical Medicine. Since joining UVA in 2015, Dr. McDermid has expanded her research directions in infant health and development as part of the Bill and Melinda Gates-funded ELICIT (Early Life Interventions for Childhood Growth and Development in Tanzania) clinical trial set in rural Tanzania, and is developing a new line of inquiry examining breast milk immune cell transfer and infant outcomes with collaborators at INSERM (Institut national de la santé et de la recherche médicale) in Montpellier, France.

Christian McMillen is professor of history and the author of three books, Making Indian Law, Discovering Tuberculosis: A Global History, 1900 to the Present, and Pandemics: A Very Short Introduction. He regularly offers classes on the history of epidemics and pandemics, as well as American Indian history. He is currently working on a book on the history of global efforts to provide clean water and sanitation to the billions without either.

Wladek Minor is a Professor in the Department of Molecular Physiology and Biological Physics. Dr. Minor’s laboratory investigates the structures of many protein targets that are important in infectious diseases. Their long-standing experience in developing tools for macromolecular crystallography tremendously benefits this endeavor. Dr. Minor is a member of NIAID Center for Structural Genomics of Infectious Diseases (CSGID) http://www.csgid.org/. Dr. Minor has developed and commercialized multiple large software packages for processing x-ray diffraction data and managing molecular structural databases, which have become the most frequently used software systems in protein crystallography. The papers describing these methods have now been cited over 30,000 times and the 1997 Methods in Enzymology paper is the 23rd most cited paper of all time, and the 7th most cited publication of the last 20 years. The data management systems LabDB and Unitrack, which track experimental details of protein production, structure determination and biochemical characterization pipelines from clone to refinement, may have tremendous long-term impacts on structural biology. Both systems may play a critical role in improving experimental reproducibility—the cornerstone of scientific research upon which all progress rests. The system tracks millions of experiments on tens of thousands of targets and uses methodologies that have more recently become known as “Big Data” techniques. Metal coordination in protein structures are analyzed in “CheckMyMetal”, which validates metal identification and refinement.

Sean R. Moore is an Associate Professor of Pediatrics and Director of Research for the Division of Pediatric Gastroenterology. Dr. Moore returns to the University of Virginia following 7 years on faculty at the University of Cincinnati, where he served as Associate Director for the Global Research Office at Cincinnati Children's Hospital Medical Center, the largest pediatric research facility in the nation. The long-term goals of Dr. Moore's research are to uncover underlying mechanisms of child undernutrition and enteric infections in developing countries and improve therapies to break their vicious cycle. Current global projects include: 1) IMAGINE, a collaboration with Dr. Aldo Lima of the Federal University of Ceará in Fortaleza, Brazil to define the dosing, efficacy, and mechanisms of a glutamine-based therapy for undernutrition in at risk children and 2) SEEM a collaboration with Dr. Asad Ali at the Aga Khan University in Karachi, Pakistan to define the etiologies and histopathology of environmental enteropathy among a birth cohort in Matiari, Pakistan. In complementary work, Dr. Moore's laboratory is developing murine models of environmental enteropathy and human intestinal organoid models of host-microbe interactions.

Cameron Mura is a Senior Scientist in Phil Bourne's Computational Biosciences Lab, based in the Dept of Biomedical Engineering and School of Data Science at UVA.  Mura's scientific background is mostly in biochemistry, crystallography and molecular simulations, and his main research interests are in the areas of structural biology, molecular biophysics and computational biology, particularly as regards the evolution of RNA-associated molecular machines, biomolecular assemblies, protein folds, and protein-protein interactions. More recent interests have included the data sciences and its attendant approaches (e.g., deep learning and other machine learning paradigms), chiefly as applied to the aforementioned scientific domains. Further information is available at http://bournelab.org.