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Jakob Zech is a Professor at Heidelberg University, affiliated with the Institute of Mathematics and the Interdisciplinary Center for Scientific Computing. His research primarily focuses on the mathematical foundations of Scientific Machine Learning (SciML), where he integrates rigorous analysis, probability theory, and deep learning to develop efficient and provably convergent algorithms for high-dimensional problems. Key areas of his investigation include Operator Learning, Bayesian Inverse Problems, Structure-Preserving Machine Learning, and Transport methods for particle-based optimization and sampling. Zech has a strong academic background, having completed his PhD at ETH Zurich under the supervision of Christoph Schwab in 2018. Before his current appointment, he held a postdoctoral position at MIT where he was part of Youssef Marzouk's research group. Since April 2020, he has progressed to the rank of Professor at Heidelberg University and continues to contribute to advancements in the field of mathematics and machine learning.
Heidelberg University • Heidelberg, Germany
Heidelberg University • Heidelberg, Germany
MIT • Cambridge, MA, USA
Conducted research as part of the SNSF fellowship.
ETH Zurich • Zurich, Switzerland
Completed thesis under Christoph Schwab.
Administered by the Faculty of Mathematics and Computer Science; covers Department of Mathematics and Department of Applied Mathematics.