Joe Schoonover is the founder and wearer of many hats at Fluid Numerics. He received a Bachelor's of Science degree in Applied Mathematics & Physics in 2011 and a PhD in Geophysical Fluid Dynamics in 2016 from Florida State University.

From 2016–2017, Dr. Schoonover held a post-doctoral research position at Los Alamos National Laboratory's Center for Nonlinear Studies, where he studied topographic wave arrest as a mechanism for western boundary current separation, transport operator diagnosis through impulse functions and graph theory, climate model equilibration acceleration strategies, and high performance computing. He spent a 2017-2018 at the Space Weather Prediction Center in Boulder, CO porting research applications into operations within the SWPC forecast office.

Joe's current research focus is in spectral element methods, physical oceanography, and environmental fluid dynamics and he has a continued interest in portable GPU programming, scientific application modernization, and cluster design and operation with a focus on AI/ML.

Elizabeth Simons is a scientist at Fluid Numerics. She received her Bachelor's of Science degree in Applied Mathematics & Physics in 2011 and a PhD in Geophysical Fluid Dynamics in 2019 from Florida State University.

During her graduate studies, Dr. Simons participated in numerous oceanographic cruises focusing on repeat hydrography to study our ocean's place in climate change.

Elizabeth enjoys planning and engaging in field campaigns that help shed light on the physical dynamics of coastal and estuarine environments and the impacts this has for local communities.

Her current interests are in designing and deploying solar powered instrumentation that can be remotely deployed to monitor our environment and can engage artists in the communication process of scientific research.

Garrett Byrd is a research software engineer at Fluid Numerics. He received his Bachelor of Science and Master of Science degrees in Applied Mathematics from the University of Tennessee in 2022 and 2024, respectively.

Garrett's academic research focused on computational fluid dynamics and ground-up implementations for numerical partial differential equations. He has an active interest in many application areas, including scientific/cloud computing, machine learning, and GPU programming.

His current research focuses on the development and benchmarking of GPU kernels as well as the development and communication of emerging machine learning techniques. Garrett is an advocate for free and open-source software.