معرفی
Teo Deveney is a Researcher in the Department of Computer Science at the University of Bath, actively contributing to mathematical and computational research since completing his PhD in 2022. His work bridges theoretical mathematics with practical applications across medical imaging and climate science domains.
His doctoral research focused on accelerating Bayesian inference using deep learning for physics-governed likelihoods, supervised by T. Shardlow and E. Mueller. Educational background centers on computational mathematics with strong foundations in differential equations and probabilistic modeling.
Research interests prioritize mathematical frameworks for complex systems: Partial Differential Equations (100% fingerprint match), Bayesian Statistics (85%), and Stochastic Differential Equations (75%). Secondary emphases include Neural Networks (60%), Climate Modeling, and Medical Imaging applications. His methodology consistently integrates uncertainty quantification with deep learning architectures.
Recent publications (2024-2025) reveal converging trends: applying score-based diffusion models to ODE-SDE gaps via Fokker-Planck equations, developing graph-adaptive finite element methods, and creating machine-learned climate parameterizations. Medical imaging work focuses on Bayesian MRI reconstruction with structured uncertainty distributions using autoencoders.
- No scientific awards documented in current records
Collaborative networks include C. Budd, C. B. Schönlieb, and L. Kreusser across Bath's computational mathematics groups. Current projects involve physics-informed neural networks for atmospheric modeling and uncertainty-aware medical image reconstruction, leveraging the university's high-performance computing infrastructure.
