# About

## John Harer, Chief Scientist & CEO

John Harer is Chief Scientist and CEO 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, Senior Mathematician

Paul Bendich is an Assistant 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 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.

## 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 in data, and developing models to explain the 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 is also passionate about creating tools for data management, data integration, and process automation.

In addition to her work at GDA, she currently holds a position as a Visiting Assistant Professor in the Mathematics Department at Duke University. She also received her Ph.D. in Computational Biology & Bioinformatics from Duke in 2014, with a thesis focused on constructing mathematical models of gene regulatory networks for periodic processes. She has a B.S. in Computer Science and has also worked as a web application and database developer, a writing/research assistant for a software engineering textbook, and in a computational biology lab performing research and scientific programming.

## Abraham Smith, 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.

## Jay Hineman, Mathematician

Jay Hineman received his PhD in Mathematics from University of Kentucky in 2012. He has worked as a researcher and instructor at 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 computational fluid dynamics from University of Kentucky. In addition to this he is experienced in configuring OS and hardware to build and run large scale scientific codes.

At GDA Jay has worked on integrating TDA tools with machining learning techniques and more traditional signal processing techniques for target tracking and classification. More generally, he is interested in the interface between dynamical systems (broadly construed) and TDA tools and TDA tools for computer vision.

## Nathan Borggren, Physicist

Nathan Borggren is a physicist at GDA who also brings expertise in computation and statistics. His scientific journey has ventured to the moons of Saturn aboard the Cassini spacecraft and back to the nuclear furnace of particle collisions at the Relativistic Heavy Ion Collider. Eventually he did a PhD from Stony Brook University in New York on stochasticity in a genetic switch.

Nathan is intrigued by noise wherever he can find it, and can find it anywhere. From genetic networks, to financial markets and superconducting circuits he counts things and histograms them, starting at the beginning, going until the end and then stopping. Between the physics of information (bits from its) and the information of physics (its from bits), he counts entropy everywhere and leaves specialization to the insects.

## Kenneth Ball, Mathematician

Kenneth Ball completed his PhD 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.