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.
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.
Andrew Bartels received his B.S. and M.S. in Mechanical Engineering at California State University, Fullerton, and is currently pursuing an M.S. in Data Analytics from Georgia Institute of Technology. Andrew has worked as a Researcher and Teaching Assistant at CSU Fullerton and a Mechanical Engineer in industry.
Andrew brings expertise in unique subsets of mechanical engineering and data science including software development, sensors and systems architecture, automation, edge processing/real time data streaming, deep learning/reinforcement learning, HPC computing, and fluids research. Andrew also has thousands of hours of data processing and assimilation, Bayesian Statistics analysis, model creation, testing, and validation experience.
Director of Business Development
Dr. Ashlee Valente is a computational biologist by training, with extensive experience in stakeholder engagement. Her graduate and postdoctoral work focused on applied biomarker discovery working closely with clinicians to design fieldable diagnostic tests. 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, including engaging with early customers and co-inventing a fermentation monitoring device now on the market. This early prototype included a cloud deployed monitoring and analytics service deployed on AWS. At GDA, Dr. Valente has participated in a comprehensive, selective, local startup accelerator program for business development training, networking, and assistance, and led customer development efforts on multiple SBIRs, and currently leads customer development and business development efforts.
Francis Motta is an Assistant Professor at Florida Atlantic University. He received his Ph.D. from Colorado State University where his research focused on applied dynamical systems, pattern formation, and topological data analysis (TDA). After completing his Ph.D., Dr. Motta worked as a visiting assistant professor at Duke University where he developed computational methods at the interface of TDA and machine learning and worked closely with systems biologists modeling the dynamics of gene regulatory networks. Dr. Motta continues to advance methods that strengthen the applicability of TDA tools to complex, large-scale, and dynamic data; and develop mathematical models of gene regulation to better understand the genetic mechanisms driving dynamic phenotypes, including in host-pathogen interactions.
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.
Senior Data Scientist and Data Visualization Engineer
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.
James Polly received his Ph.D. in 2016 from the City College of New York, where his research focused on midlatitude cyclones and the role these storms play in Earth's atmospheric energy budget. His research experience also includes numerical methods and simulation of fluids, and he has aerospace industry experience in propulsion and structural analysis.
Dr. Polly is able to leverage multiple sources of remote sensing, reanalysis, and model data to inform a variety of research questions, and problems relating to the atmosphere and ocean are among his primary research interests.
Chief Solutions Architect
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.
Facility Security Officer
John “Ken” Roth is GDA's FSO. His 38 years of Air Force and Industry security-related work experience has taken him to most regions of the world, with the latest prior stint in Austin, Texas. In addition to his Master’s in Education and Bachelor’s in History, he also holds NCMS Industrial Security Professional certification and is a member of the NCMS National ISP board.
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.
Director of Operations
Megan creates processes and policies to help the team work as productively and effectively as possible. She is the administrative point of contact for contracts, ensuring reporting, security, and financial requirements are met. Megan also directs human resources activities including performance reviews, employer-sponsored benefits administration, and payroll processing.
Email Megan with questions about careers, business opportunities, or the website: email@example.com.
Michael Catanzaro received his Ph.D. in Mathematics from Wayne State University in 2016, where his doctoral research involved stochastic and applied topology. He was an assistant professor at Iowa State University following a postdoctoral research position at the University of Florida. Throughout these periods, his research focused on solving scientific problems through the lens of algebraic topology.
Dr. Catanzaro's recent work focuses on theoretical and applied aspects of topological data analysis (TDA). He has successfully applied TDA to the study of task modulation in fMRI data, iterated composition and fractals, and geometric aspects of multi-parameter persistent homology. He has also used topology to develop novel methods of studying exciton scattering in physical chemistry and stochastic currents in thermodynamics. At GDA, he focuses on developing algorithms for determining stratifications within data.
Nirav Patel received a B.S. in Economics with a specialization in Advanced Mathematical Methods from New York University and a M.S. in Economics and Computer Science from Duke University. Before starting graduate school, he worked as an applied econometrics research analyst at the Brookings Institution in Washington, DC. At Duke, Nirav built mathematical economic models to study the reciprocal relationship between economic inequality and political representation. His research has spanned areas such as spatial and development economics within the context of Sub-Saharan Africa. Broadly, Nirav has extensive experience applying causal inference and machine learning techniques to generate insights from complex data including large-scale survey microdata, ultra high-resolution satellite data, and unstructured time-series data, among others. Nirav's current research interests are in computational game theory and deep learning.
Paul Smith received his Ph.D. in Molecular Biophysics from Columbia University in 2005. During his postdoctoral work at the Memorial Sloan-Kettering Cancer Center, he investigated structure-function relationships in RNA/DNA metabolizing enzymes and carried out studies aimed at discovering novel anti-parasitic drugs. As an Assistant Professor of Chemistry at Fordham University, he studied the structural properties of various viral proteins and applications of machine learning to macromolecular crystallography. Since 2019, he has worked as a Data Scientist and Software Engineer in both the FinTech and Digital Healthcare sectors. Throughout his scientific career, he has been an active software developer and systems administrator.