Meet our team
GDA is built around a core team of mathematicians, data scientists, and professional software developers. Our team is deeply curious about a diverse set of scientific and technical questions in mathematics, physics, biology, data analysis, statistics, computer science, and engineering. Our work requires broad horizons, intellectual flexibility, and passion.
John L. Harer
CEO & Subject Matter Expert
John Harer is CEO and Subject Matter Expert at Geometric Data Analytics (GDA) and Professor of Mathematics, Computer Science, and Electrical and Computer Engineering at Duke University. He founded GDA to support the application of methods of geometric and topological data analysis to a wide variety of problems in the commercial and government sectors.
Dr. Harer is an expert in the application of methods ranging from geometry and topology to data of various types. He is one of the creators of Topological Data Analysis (TDA), a new field of applied mathematics that is revolutionizing how “big data” can be analyzed and managed. TDA has been applied to problems in agent tracking, robust network design, gene regulatory network discovery, cybersecurity, intelligence analytics, and many others.
Paul Bendich is an Associate Research Professor of Mathematics at Duke University and the Associate Director for Undergraduate Research in the Information Initiative at Duke. He received his Ph.D. in Mathematics from Duke in 2008 and held postdoctoral positions at the Institute for Science and Technology Austria and Penn State.
Dr. Bendich’s doctoral work laid some of the early theoretical foundations for topological data analysis (TDA). Since then, he has been at the forefront of the integration of TDA with more standard machine learning and statistical techniques. This work has found wide application in vehicle tracking, brain imaging, and image simplification, among many other areas.
Dr. Bendich oversees all scientific efforts at GDA. Through his affiliation with the Information Initiative at Duke, Dr. Bendich has developed broad and deep expertise across the field of modern data analysis, and he has frequently been the leader of interdisciplinary and vertically integrated teams.
Subject Matter Expert
Henry Pfister is a Professor of Electrical and Computer Engineering at Duke University. He received his Ph.D. from the University of California, San Diego, and he was a postdoctoral fellow at the Swiss Federal Institute of Technology in Lausanne, Switzerland. His research interests include information theory, error-correcting codes, probabilistic graphical models, quantum computing, and machine learning.
Abraham Smith is an Associate Professor in the Department of Mathematics, Statistics and Computer Science at University of Wisconsin-Stout, Wisconsin’s Polytechnic University. He received his Ph.D. in Mathematics from Duke University in 2009 and held postdoctoral research positions at Fordham University and McGill University.
Dr. Smith specializes in using geometric insight to reformulate open-ended data-analysis and machine-learning questions into firm mathematical theories, and then optimize those mathematical theories into concrete and efficient code. Dr. Smith’s approach stems from a background in geometric differential equations and integrable systems—systems that predict changes and interactions in the physical world. At McGill University, Dr. Smith explained the high-dimensional structure of these systems through the support of the National Science Foundation (NSF) and the Mathematical Sciences Research Institute (MSRI). Dr. Smith is an avid scientific programmer and Linux administrator with long-time expertise across the entire software stack, and he uses these skills to design and implement parallel computing infrastructure for research code and production workflows at GDA.
Senior Scientist & Systems Architect
Dr. Deckard received her B.S. in Computer Science from CSU Fullerton in 2008 and her Ph.D. in Computational Biology & Bioinformatics from Duke University in 2014. She has worked as an application and database developer, a researcher in a computational biology lab, a Visiting Assistant Professor in the Mathematics Department at Duke University, and a computational scientist at a biotech startup.
Anastasia Deckard’s projects at GDA have included early detection of public health incidents, constructing complex adaptive systems, and data-driven discovery in complex domains. This work has led her through diverse fields such as mathematical modeling, network theory, optimization algorithms, formal languages, bioinformatics, and systems biology. She has also worked extensively on building software for algorithm validation, analysis pipelines, and data collection, integration, validation, and management.
Ashlee Valente earned her B.S. and M.S. in Bioinformatics at Rochester Institute of Technology and completed her Ph.D. in Computational Biology and Bioinformatics at Duke University in 2013. Her doctoral thesis focused on the development of statistical models to improve computational pre-processing of transcriptomics and proteomics data. Dr. Valente’s postdoctoral work focused extensively on applied biomarker discovery, gaining her experience building predictive models for clinical outcomes in infectious disease and biopolymer exposure in transcriptomics, proteomics, and metabolomics data.
Following her postdoctoral work, Dr. Valente joined the industrial computational biology and data science community, leveraging genomics and IoT data in agriculture, biofuel, and food and beverage fermentation. Dr. Valente’s work at GDA focuses on the development of epidemiological monitoring tools, and the translation of research products to commercially viable solutions.
Jay Hineman received his Ph.D. in Mathematics from the University of Kentucky in 2012. He has worked as a researcher and instructor at the University of Kentucky and Fordham University. Dr. Hineman has extensive knowledge of numerical simulation and analysis of liquid crystals, ion electrochemistry, and biomembranes; and holds a graduate certificate in computational fluid dynamics from the University of Kentucky. Many of these topics have rich geometric interpretations (e.g., harmonic maps and curvature flow) applicable to broader questions about data. In addition, he is experienced in configuring OS and hardware to build and run large scale scientific code.
At GDA, Dr. Hineman has applied his mathematical and computational background to integrating topological data analysis tools with machine learning techniques. He has focused on the domains of data fusion for targeting and control of system of systems for agile logistics and military medicine. He also serves as an adjunct instructor in the ECE Department at Duke University, where he leads classes about the implementation of machine learning and reinforcement learning at scale.
Dr. Ball completed his Ph.D. in Mathematics in 2013 at North Carolina State University, where he studied numerical simulation of mechanical systems with variational integrators. He has held postdoctoral positions at the US Army Research Lab (in coordination with the University of Texas at San Antonio) and the US EPA where he researched machine learning for brain-computer interfaces and computational toxicology, respectively.
Dr. Ball offers expertise in mathematical modeling, simulation, and machine learning in a variety of problem domains, along with formal expertise in manifold theory, differential geometry, and dynamical systems. He has developed analytical tools to process and interpret behavioral and physiological responses. He has applied mathematical tools to the analysis of complicated logistics and systems-of-systems problems, behavior predictions, signal/detection modelling, and food/beverage industry processes. He leads a research team investigating the use of topological and image processing tools to support experimental analyses in the agricultural domain. He is especially interested in the interpretation of meaningful and useful features in complicated real-world datasets.
Kevin McGoff is an Associate Professor of Mathematics at the University of North Carolina at Charlotte. He received his Ph.D. in Mathematics at the University of Maryland, where he specialized in dynamical systems and probability. During his time as a Visiting Assistant Professor at Duke University, he diversified his portfolio to include research in statistics and systems biology, including gene regulatory networks and epidemiology.
Dr. McGoff’s research interests involve understanding dynamical systems from several perspectives. From the probabilistic perspective, he analyzes the long-term behavior of systems whose rules of evolution are drawn at random. Taking a more statistical point of view, he seeks to provide rigorous performance bounds on statistical procedures for data with long-range dependence. At GDA, he focuses on control of stochastic dynamical systems.
Data Scientist & Lead UI Developer
Gary Koplik received a B.A. in Economics and Mathematical Science from Colby College and a M.S. in Economics and Computer Science from Duke University. His research has included topics such as historical market responses to unemployment reports, summarizing variable interactions in large databases, and the incidence of rare diseases in health systems. At GDA, he focuses on the geometry and coverage of non-stationary sensor networks, as well as on building company-wide static and dynamic visualization skills.
Gabrielle Angeloro received a B.S. in Mathematics from SUNY Geneseo and a M.S. in Mathematics from Iowa State University. While at Iowa State, she developed a Python package implementing persistence landscapes: a vectorization scheme for persistent homology. Gabrielle's current research interests are in the intersection of topological tools and deep learning.
Data Scientist & Machine Learning Engineer
Ryan Peters holds a B.S. and a M.A. in Mathematics from the University of Kentucky, where his interests ranged from algebraic topology and geometry to numeric analysis and deep learning. During Ryan’s graduate studies, he investigated the learning effects of various orthogonalization schema in convolutional neural networks. At GDA, he is a core developer for a variety of machine learning projects, performing ongoing research in multi-agent reinforcement learning methods, hyper-network training, and meta model factorization.
Sam Voisin received his B.S. in Financial Management from Clemson University and his M.S. in Statistical Science from Duke University. While at Duke, he researched methods for pre-processing sEMG signals as a means to classify physical gestures. Sam’s current research interests include topological and geometric data analysis techniques as well as graph theoretic methods for data analysis.
Tessa Johnson received a B.S. in Applied Mathematics and Statistics from Texas A&M University and a M.S. in Statistical Sciences from Duke University. She has broad research interests including applications of statistical modeling for forensics data and development of improved feature selection methodology for complex feature sets. During her graduate studies, she worked on applying Bayesian methodology to Bioinformatics data and exploring the evolution of Dynamic Social Networks in a Bayesian framework. At GDA, her research focuses on the implementation and application of novel topological algorithms and fusion techniques for high-dimensional data arising from multiple sensing modalities.
As Business Manager, Megan creates sustainable processes and policies to help the team work as productively and effectively as possible. She is the administrative point of contact for contracts, ensuring any reporting, security, and financial requirements are met. Megan also performs human resources functions including performance reviews, employer-sponsored benefits administration, and payroll processing.
Email Megan with questions about careers, business opportunities, or questions about the website: email@example.com.