Joe Eaton is a co-Owner of Actionable Intellectual Property Intelligence LLC.
He holds a Ph.D. in Computational and Applied Mathematics from UT Austin, a Masters of Mechanical Engineering from Stanford,
and a Bachelors in Mechanical Engineering from Rice University.
He leads knowledge graph applications for AiPi, including Graph Neural Networks and analytics applied to patents, litigation, investment opportunities and new technologies.

Previously he worked as a Distinguished System Engineer for Data and Graph Analytics at NVIDIA,
and led the company strategy for Graph Neural Networks and Graph-RAG.

Joe leads teams for operations research, graph analysis, framework development and optimization,
as well as interacts with prospective partners and customers in industry.
Key areas of interest are knowledge graphs and graph neural networks, operations research and decision science, financial services, cyber security, retail recommenders, and molecular generation for drug discovery.

He has spent the last 13 years at NVIDIA working on applications of sparse
linear algebra; he formerly lead CUDA Libraries such as cuSOLVER, cuSPARSE, nvGRAPH and an iterative linear solver package
including Algebraic Multi-Grid called AmgX, now open-source.

Joe was a founding member and architect of the NVIDIA RAPIDS, developing
Python APIs, cuDF, cuML and cuGRAPH.
RAPIDS is an end-to-end open source platform for data science, including IO, ETL, analytics,
model training, inference and visualization.

Previously he spent 18 years in Oil & Gas process and reservoir simulation, database development,
and cloud services. He is a frequent speaker at SuperComputing, NVIDIA’s GPU Technology Conference,
SciPy, HPEC, and directly interfaces with engineers and mathematicians across many industries,
from financial services to high-energy physics and drug discovery.