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Dr. Alison Motsinger-Reif
(Opening Keynote)
Dr. Alison Motsinger-Reif is the Branch Chief and a Senior Investigator in the Biostatistics and Computational Biology Branch at the NIEHS. She received her M.S. in Applied Statistics and PhD in Human Genetics – both from Vanderbilt University in 2006 and 2007 respectively. She was a faculty member at North Carolina State University from 2007-2018, where she built a research program to address important challenges in the “Big Data” space, and received a mid-career endowment. The primary goal of her research is the development of computational methods to detect genetic risk factors of complex traits in human populations. As environmental health increasingly accepts a complex model of phenotypic development that involving many genetic and environment factors, her methods development is focused on strategies that incorporate this complexity. The methods she develops include artificial intelligence methods such as genetic algorithms, and machine learning tools like neural networks, etc., Her methods and corresponding software tools support are designed to detect gene-gene and gene-environment interactions. She has published over 200 peer-reviewed publications as a result of this work, in a broad range of journals that reflect the interdisciplinary nature of her work.
Dr. Rebecca Nugent
(Closing Keynote)
Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Head of the Carnegie Mellon Department of Statistics & Data Science. She received her PhD in Statistics from the University of Washington, her M.S. in Statistics from Stanford University, and her B.A. in Mathematics, Statistics, and Spanish from Rice University. Dr. Nugent is currently on the leadership team for the NSF AI Institute for Societal Decision Making and has expertise in designing and implementing data science/AI professional development programs for business leaders in industries including health care, finance, automotive/manufacturing, and life sciences. She was the faculty co-Director of the Moderna AI Academy and the Founding Director of the Statistics & Data Science Corporate Capstone program, an experiential learning initiative that engages faculty and students with data science problems in industry, non-profits, and government organizations. Dr. Nugent is one of the co-founders of the Carnegie Mellon Sports Analytics Center, now in its seventh year of supporting cutting edge research, sports analytics training and educational programming, and the development of diverse pipelines into related industries and graduate school programs. She also serves as the PI for the NSF-funded SCORE with Data project, a national initiative incorporating best pedagogical practices in data science and sports analytics. She has won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics. She recently served as the co-chair for the National Academy of Sciences study on Improving Defense Acquisition Workforce Capability in Data Use and served on the NAS study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Dr. Nugent has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications. Her current research focus is the development and deployment of low-barrier data analysis platforms that allow for adaptive instruction and the study of data science as a science.
Dr. Sarah Ratcliffe
(Career Panel )
Dr. Ratcliffe is a Professor of Biostatistics at the University of Virginia. She currently serves as the Senior Vice Chair for Research and Director of the Division of Biostatistics in the Department of Public Health Sciences, Director of the Research Methods Core for iTHRIV, and Director of the Data Management and Statistics Core for the Virginia Alzheimer’s Disease Center. Dr. Ratcliffe has over 20 years of experience as a biostatistician with expertise in the analysis of correlated data, especially longitudinal and functional data, in predictive modeling, as well as expertise in modeling informative missing data / dropout. She was elected the 2024-2025 International Biometric Society (IBS) Treasurer, having previously served on the IBS Executive Board (2021-2023) and as the 2019 ENAR President, and was elected a Fellow of the American Statistical Association in 2020.
Dr. Gina Maria Pomann
(Career Panel)
Dr. Pomann is a biostatistician and educator with extensive research, leadership, and administrative experience. Her methodological contributions include the development of novel statistical methodology and predictive modeling techniques with applications to functional data and brain imaging.
Collaborating with investigators across biomedical fields, Dr. Pomann has published in areas such as Surgery, Urology, and Hospital Medicine, among others. To support the development of efficient and effective data-intensive research across the biomedical sciences, Dr. Pomann’s primary research focuses on the science of team science. She works with collaborators with diverse scientific expertise to develop administrative structures, scientific operational processes, methods for organizing quantitative collaboration units, and workforce development programs.
Dr. Pomann currently serves as an Associate Professor of Biostatistics and Bioinformatics and is the Director of the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core, a group of faculty and staff with diverse quantitative expertise (biostatisticians, data scientists, bioinformaticians, clinical informaticians, epidemiologists, etc.) contributing to groundbreaking research in clinical and translational domains. She has directed the development and management of 30 collaboration teams comprising biostatistics staff, faculty, and students. She has successfully developed collaboration teams across various medical fields, partnering with entities such as the Department of Pediatrics, the Global Health Institute, and the Department of Neurosurgery, to name a few. The programs she directs have helped over 1100 investigators identify appropriate collaborators with quantitative expertise and have led to the co-authorship of more than 550 collaborative manuscripts by quantitative staff and faculty within the BERD Core.
In addition to building innovative processes and organizational structures to facilitate collaborations to drive data-intensive research, Dr. Pomann leads numerous workforce development programs for quantitative scientists. She develops processes and best practices to accelerate science and meet the evolving needs of data-driven clinical and translational research. Dr. Pomann developed the BERD Core Training and Internship Program (BCTIP), to provide a hands-on collaboration experience for Masters of Biostatistics students at Duke. She also oversees the Duke AI Health Fellowship Program, a two-year post graduate training program in data science and AI in Healthcare. Dr. Pomann is also MPI of an R25 titled "Quantitative Methods for HIV/AIDS Research"; facilitating internships for quantitative students as well as a mentored scholars program and workshop series for biomedical investigators. Dr. Pomann holds a joint appointment at Duke National University of Singapore which allows her to develop training programs for the international workforce. The programs she directs have supported and trained more than 20 faculty, 40 staff, and 95 student interns to engage in data-intensive biomedical research.
Dr. Rebecca Medlin
(Career Panel)
Rebecca Medlin is a Research Staff Member in the Operational Evaluation Division (OED) at the Institute for Defense Analyses (IDA). OED researchers apply deep technical, analytical, and subject-matter expertise to support Department of Defense operational testing and evaluation, allocation of resources, weapon system sustainment decisions, combatant command- and service-level cybersecurity evaluations. Specifically, she leverages her statistical expertise to develop test and evaluation concepts, observe test events, employ modern data-driven analytical approaches, write reports, publish research papers, and present results and recommendations to senior government decision-makers. Additional activities include managing OED’s statistics and data science research portfolio and planning and organizing DATAWorks, an annual workshop for the defense and aerospace communities designed to strengthen their application of scientific approaches to test design and program evaluation. The event is the result of a multi-organization collaboration with DOT&E, NASA, IDA, and the ASA.
Rebecca received her MS in Statistics from Virginia Tech in 2010 and her PhD in Statistics from Virginia Tech in 2014. Her research focus was in the fields of Design of Experiments and Reliability.
Mckenna Magoffin
(Career Panel)
McKenna is a Data Scientist with Socially Determined. She works with community and individual data to help the Data Science team and Socially Determined as a whole produce risk scores that provide insight into different Social Determinants of Health to different clients (e.g., Uber Health, Blue Cross Blue Shield, Pfizer) to better serve different US groups and individuals. McKenna graduated from Virginia Tech in May 2022 with a B.S. in Mathematics as an Honors Laureate. Her research with the Honors College and the Mathematics Department in Emergency Management, Historic Preservation, and Carbon Emissions led her to the job she has today.
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