Transforming the World
03/2021 – 02/2024
Statistical Inversion and Quantification of Uncertainties
Fabian Telschow was the head of the MATH+ Junior Research Group “Statistical Inversion and Quantification of Uncertainties” at HU Berlin from March 2021 until February 2024.
In his research he developes statistical methodologies for image analysis and functional data using random field theory. His focus lies on inferential methodologies such as confidence sets for random fields on irregular domains in arbitrary dimension and related topics such as quantification of stochastic uncertainties of level sets. With the group of Thomas Nichols (University of Oxford) he applies these methods to neuro-imaging data. Further applications comprise for example biomedical gait data and climate data.
Fabian Telschow studied mathematics and physics at the Georg-August-University Göttingen and received his Ph.D. in mathematical statistics from Georg-August-University Göttingen in September 2016, under supervision of Stephan Huckemann and Axel Munk. Afterward, he held a Postdoc position at University of California, San Diego, working with Armin Schwartzman.
03/2023 – 09/2023
Mathematics of Data Science
Leon Bungert was the head of the MATH+ Junior Research Group “Mathematics for Data Science” at TU Berlin from March until September 2023. He is now a professor of machine learning at the Universität Würzburg. During his time at MATH+ he worked, inter alia, on adversarial robustness of machine learning and consensus-based optimization.
His research interests lie in applied analysis and numerics, focusing on applications in data science, machine learning, and imaging. In his research, he investigates PDEs and variational models (on graphs), variational regularization of inverse problems and neural networks, and nonlinear optimization.
Leon Bungert studied Mathematics and received his B.Sc. in 2016 and his M.Sc. in 2017, both from the Friedrich-Alexander-Universität Erlangen-Nürnberg. Afterwards, he did his doctoral studies there, and obtained his PhD (summa cum laude) in 2020 with a thesis on “Nonlinear Spectral Analysis with Variational Methods” under the supervision of Martin Burger. He continued his postdoctoral studies with Martin Burger from 2020 to 2021 before he moved to the Hausdorff Center of Mathematics in Bonn.
02/2017 – 08/2023
Optimization Under Uncertainty
From 2017 until 2023, Guillaume Sagnol was the Head of the Junior Research Group “Optimization under Uncertainty” at TU Berlin. It started as a Junior Research Group (JRG) in ECMath/MATHEON, and became a MATH+ JRG in January 2019. He is currently working as a Senior Consultant at FICO.
His research is at the interface of discrete optimization, convex conic programming, and statistics.
Guillaume Sagnol obtained his master degree in science and engineering at École des Mines de Paris in 2007, followed by his PhD in 2010 at INRIA Saclay & CMAP (École Polytechnique). From 2010 – 2014 he was postdoctoral researcher at ZIB, then from 2014 – 2016 at Charité, and 2016 – 2017 again at ZIB.
03/2020 – 06/2021
Mathematical Foundations of Data Science
Nicole Mücke led the MATH+ Junior Research Group “Mathematical Foundations of Data Science” at TU Berlin from 03/2020 until 06/2021. She is now a professor for statistical learning and information theory at TU Braunschweig.
Nicole Mücke works in the field of statistical learning theory, in particular, deep learning, the efficiency of kernel methods, stochastic approximation methods, and statistical inverse problems.
Nicole Mücke studied mathematics both in Potsdam and at HU Berlin. She received her Ph.D. from the University of Potsdam in October 2017. Afterward, she held postdoc positions at the Istituto Italiano di Tecnologia Center for Convergent Technologies for one year, and for another year at the University of Stuttgart before she returned to Berlin.