David Hall

Highly accomplished Senior Research Scientist and Developer Relations professional with extensive experience leveraging artificial intelligence (AI) and high-performance computing (HPC) to accelerate scientific discovery, particularly in environmental modeling and disaster prediction. Expertise in developing advanced AI models for simulating complex physical phenomena, including extreme weather, fluid dynamics, and helio-physics. Proven ability to architect GPU-accelerated solutions, lead strategic research partnerships, and bridge the gap between cutting-edge technology and scientific application. Passionate speaker and thought leader dedicated to harnessing machine intelligence to address critical global challenges.

• Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data., Artificial Intelligence for the Earth Systems / American Meteorological Society, 10/01/24
• Pushing the frontiers in climate modeling and analysis with machine learning., Springer Science and Business Media LLC, 08/01/24
• Digital twins of the Earth with and for humans., Springer Science and Business Media LLC, 08/01/24
• DCMIP2016: the tropical cyclone test case., Geoscientific Model Development, 04/01/24
• FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators., Association for Computing Machinery, 06/26/23
• From Super-Resolution to Downscaling - An Image-Inpainting Deep Neural Network for High Resolution Weather and Climate Models., Copernicus GmbH, 05/01/23
• Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook., Artificial Intelligence for the Earth Systems / American Meteorological Society, 07/01/22
• Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System., Journal of Advances in Modeling Earth Systems / American Geophysical Union (AGU), 06/01/22
• Artificial Intelligence and Earth System Modeling - revisiting Research of the Past and Future., Copernicus GmbH, 03/01/22
• Artificial intelligence reconstructs missing climate information., Copernicus, 03/01/21
• The Frontiers of Deep Learning for Earth System Modelling., Copernicus, 03/01/21
• An Energy Consistent Discretization of the Nonhydrostatic Equations in Primitive Variables., Journal of Advances in Modeling Earth Systems (JAMES), 01/01/20
• DCMIP2016: the splitting supercell test case., Geoscientific Model Development, 03/01/19
• DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models., Geoscientific Model Development, 12/01/17
• How predictable is the timing of a summer ice-free Arctic?, Geophysical Research Letters, 10/01/16
• Dynamical Core Model Intercomparison Project (DCMIP): Tracer Transport Test Results for CAM-SE., Quarterly Journal of the Royal Meteorological Society, 04/01/16
• Discontinuous Galerkin Transport on the Spherical Yin-Yang Overset Mesh., Monthly Weather Review, 01/01/13
• Non-Equilibrium Particle-Field Simulations of Polymer-Nanocomposite Dynamics., Chemical Engineering Science, 01/16/09
• Numerical Method for Hydrodynamic Transport of Inhomogeneous Polymer Melts., Journal of Computational Physics, 06/10/07
• Hydrodynamic self-consistent field theory: Computational fluid dynamics for inhomogeneous polymeric fluids and nano-composites (dissertation)., ProQuest Dissertations & Theses, 01/01/07
• Hydrodynamic Self-Consistent Field Theory for Inhomogeneous Polymer Melts., Phys. Rev. Lett., 09/11/06
• The Direct Numerical Simulation of Two-Phase Flows with Interface Capturing Methods., Houille Blanche, 01/01/05
• Gravitational Settling of 22Ne in Liquid White Dwarf Interiors., The Astrophysical Journal, 03/10/01