Jonas Latz

Jonas Latz is a Lecturer in Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, uncertainty quantification, stochastic processes, and their applications in computational mathematics and inverse problems. He has contributed to areas such as physics-informed neural networks, stochastic gradient methods, and medical imaging modeling.

Key research interests include developing robust algorithms for Bayesian inverse problems, analyzing stochastic dynamical systems, and advancing numerical methods for partial differential equations. His work bridges theoretical foundations with practical applications in fields like tumor growth modeling and medical image reconstruction.

• Recent Achievements:
• Recipient of the SIAM Activity Group Uncertainty Quantification Early Career Prize (2024)
• SIAM Student Paper Prize (2020)
• SIGEST Award (2023)

Dr. Latz collaborates internationally on topics such as adversarial machine learning, deep learning methods for PDEs, and stochastic sampling techniques. His research emphasizes rigorous mathematical analysis alongside computational innovation.