What we do
GDA was founded in 2012 by Dr. John Harer while he was a Professor of Mathematics at Duke University. GDA grew out of research performed at Duke, funded through a variety of sources including DARPA, AFOSR, NSF, NIH, and DTRA.
We apply rigorous mathematical principles to understanding big data problems. With a strong team of mathematicians and data scientists that have deep expertise in Topological Data Analysis, graph theory, and applied mathematics, we provide accurate solutions with known uncertainty to drive informed, confident decisions.
Information Analysis
Data Assimilation
We leverage our rigorous mathematical approaches to incorporate multi-model data sources into models for various environmental and agricultural applications.
Learning Algorithms
With widespread impact of machine learning approaches, we enhance existing methodologies with computationally efficient features and explainable models, and specialize in active and reinforcement learning frameworks.
Confidence Management
Planning Under Uncertainty
Our tools account for uncertain information, and enable decision-makers to optimize for success in dynamic scenarios.
Confidence Estimation
Our approaches determine robustness of algorithm results, underlying data, and the appropriateness of algorithm applications.