About
Dr. Georgios Kissas is a Lecturer at the Department of Computer Science at ETH Zurich, affiliated with the ETH AI Center. His research focuses on integrating machine learning with computational science and engineering, particularly in neuro-symbolic AI, operator learning, and cardiovascular modeling. He explores applications in medical biophysics, computational fluid dynamics, and scientific computing. Recent work emphasizes hybrid neural operator networks, differentiable simulations, and digital twin technologies for personalized medicine.
Key research themes include:
- Development of neuro-symbolic systems for analytical solutions of differential equations
- Machine learning approaches for hemodynamic simulations and vascular parameter estimation
- Reduced-order modeling for cardiovascular disease analysis using biobank data
His publications demonstrate contributions to operator learning frameworks, uncertainty quantification methods, and biomedical engineering applications. Current efforts bridge AI innovation with practical clinical and engineering challenges.
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