John 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.
Professor Harer is an expert in the application of methods from geometry and topology to data of various types. He was one of the creators of Topological Data Analysis, a new field of applied mathematics that is revolutionizing how “big data” can be analyzed and managed. GDA has been applied to problems in agent tracking, robust network design, gene regulatory network discovery, cyber security, intelligence analytics, and many others.
Paul Bendich, Chief Scientist
Paul Bendich is an Associate Research Professor of Mathematics at Duke University, and is also the Associate Director for Undergraduate Research in the Information Initiative at Duke (iiD). He has held post-doctoral positions at the Institute for Science and Technology Austria and Penn State, and he received his Ph.D. in mathematics from Duke in 2008.
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: for example, in vehicle tracking and in brain imaging. Through his affiliation with iiD, Dr. Bendich has developed broad and deep expertise across the field of modern data analysis, and has frequently been the leader of interdisciplinary and vertically integrated teams.
Abraham Smith, Senior Mathematician
Abraham Smith is an Assistant Professor in the Department of Mathematics, Statistics and Computer Science at University of Wisconsin-Stout, Wisconsin’s Polytechnic University. He has held post-doctoral research positions at Fordham University and McGill University, and he received his Ph.D. in mathematics from Duke University in 2009.
Dr. Smith specializes in using geometric insight to understand differential equations and integrable systems—these are the 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 also an avid scientific programmer and Linux administrator with expertise across the entire software stack. At Fordham University, Dr. Smith designed and implemented a parallel computing infrastructure that supports research and education for the entire university.
Anastasia Deckard, Senior Scientist & Systems Architect
Anastasia Deckard’s work focuses on the study of large sets of time series data: discovering patterns in data, finding relationships between patterns, and developing models to study patterns. This work has lead her through diverse fields such as signal processing, mathematical modeling, network theory, optimization algorithms, inference algorithms, and systems biology. She also develops tools for data management, data integration, and process automation.
She received her B.S. in Computer Science from CSU Fullerton and her Ph.D. in Computational Biology & Bioinformatics from Duke University. Her thesis was on constructing mathematical models of gene regulatory networks for periodic processes. She has worked as an application and a database developer, a researcher/programmer in a computational biology lab, a postdoc in the Mathematics Department at Duke University, and a computational scientist at a bio-tech startup.
Jay Hineman, Senior Mathematician
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. Jay 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 (harmonic maps, 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, Jay has applied his mathematical and computational background to integrating TDA 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.
Nathan Borggren, Senior Physicist
Nathan Borggren is a physicist at GDA who brings expertise in computation and statistics. His scientific journey has led him to the moons of Saturn aboard the Cassini spacecraft and to the nuclear furnace of particle collisions at the Relativistic Heavy Ion Collider. He received his Ph.D. from Stony Brook University in New York, completing a thesis on stochasticity in a genetic switch.
Nathan is intrigued by noise wherever he can find it — from genetic networks to financial markets to superconducting circuits. He leads the blockchain and IoT related efforts at GDA.
Kenneth Ball, Mathematician
Kenneth 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.
Kenneth 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 is especially interested in the interpretation of meaningful and useful features in complicated real world datasets.
Kevin McGoff, Mathematician
Kevin McGoff is an Assistant 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 largely focuses on control of stochastic dynamical systems.
Kenneth Stewart, Computational Scientist
Creative musician and signal technologist Kenneth Stewart specializes in hardware and software solutions for IoT, hardware sensing, and real-time signal processing. Kenneth balances both emergent and historic technologies for creative and analytic applications. His research interests include general algorithms and their applications, human-computer interaction, and algorithmic/generative music.
Kenneth received his Ph.D. in Music Composition at Duke University, focusing on creating hardware, software, and musical designs applied to a variety of genres and traditions, from Contemporary Classical to Experimental Electronic music. More recently he’s focused on developing technologies in the Country, New American String, and Jazz traditions.
Gary Koplik, Data Scientist
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.
Lihan Yao, Data Scientist
Lihan Yao completed his B.S. in Mathematics at Fordham University and his M.S. in Data Science at New York University. During his graduate studies, Lihan applied traditional and modern machine learning algorithms to biological data, primarily protein sequences and microscopy cell images. Lihan also participated in mathematics research as a Fulbright Fellow at the Alfred Renyi Institute in Budapest, where he worked on graph algorithms and combinatorics.
Ryan Peters, Data Scientist
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.
Sayan Mukherjee, Subject Matter Expert
Sayan Mukherjee is a Professor of Statistical Science, Mathematics, Computer Science, and Biostatistics & Bioinformatics at Duke University. Dr. Mukherjee received his Ph.D. from MIT and held a post-doctoral position at the Broad Institute of Harvard and MIT. He is a Fellow of the Institute of Mathematical Statistics. His research covers Bayesian methodology, computational and statistical methods in statistical genetics, quantitative genetics, cancer biology and morphology, discrete Hodge theory, geometry and topology in statistical inference, inference in dynamical systems, machine learning, and stochastic topology.
Henry Pfister, 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 was a post-doctoral 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.