Artificial Intelligence and Machine Learning (AI/ML) | School of Physics

Artificial Intelligence and Machine Learning (AI/ML) | School of Physics
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Artificial Intelligence and Machine Learning (AI/ML)
The Artificial Intelligence/Machine Learning (AI/ML) research activities at the School of Physics involve designing machine learning and reasoning algorithms to accelerate physics research, as well as using techniques from physics to develop reliable AI algorithms. Faculty in the School design AI/ML algorithms applicable to a variety of settings, including astrophysics, cosmology, dynamical systems, hydrodynamics, fundamental physics, and neuroscience. These algorithms incorporate first-principles physics concepts, uncertainty quantification, and interpretability tools, ingest data to discover new dynamical equations, or use Large Language Models (LLMs), leveraging AI supercomputers at scale.
AI research in SoP benefits from interdisciplinary connections through the
AI4Science Center
, the
Institute for Data Engineering and Science (IDEaS)
Tech AI
, and the
ML Center
at Georgia Tech.
Faculty Members
Flavio Fenton
Professor
Research Interests:
Theory and simulation of complex cardiometrics.
Aishik Ghosh
Assistant Professor
Research Interests:
Neuro-symbolic AI, high-dimensional statistics, uncertainty quantification, fast simulation, fast inference & experiment design in the context of fundamental physics and astrophysics
Roman Grigoriev
Professor
Research Interests:
Model discovery, fluid dynamics, nonlinear systems
Feryal Ozel
Professor and Chair
Research Interests:
Interferometric imaging, multi-modal data, black holes
Dimitrios Psaltis
Professor and Director of the AI4Science Center
Research Interests:
AI methods in astrophysics, HPC, general relativity and black holes
Audrey Sederberg
Assistant Professor
Research Interests:
Theoretical neuroscience, artificial and biological neural network models
Ignacio Taboada
Professor
Research Interests:
Neutrino Astrophysics with IceCube and P-ONE. AI methods in neutrino event reconstruction; AI methods on event selection for neutrino telescopes.
John Wise
Professor
Research Interests:
HPC cosmological simulations, radiation transport, black holes, AI methods in simulations – physics emulators, physics-informed NNs, multi-modal models
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