Transforming the World

through Mathematics

Independent Junior Research Groups

MATH+ funds six positions for heads of independent junior research groups (JRG) in application-driven basic mathematical research, in order to strengthen support for excellent young researchers who already have some years of postdoc experience, and to give them a head start in building their own research profile and group within the supportive environment of a large Cluster of Excellence. JRG leaders receive funds to hire a doctoral researcher (PhD candidate) to start their group.

 

Current Group Leaders

Franziska Eberle

TU Berlin

Optimization Under Uncertainty

 

Since October 2023, Franziska Eberle is the Head of the Junior Research Group “Optimization under Uncertainty” at TU Berlin. Her research interests lie in combinatorial optimization under uncertainty, mainly focusing on online and stochastic ways to model uncertainty in the input parameters. In her research, she investigates optimization problems with only partially known input parameters and designs and analyzes algorithms with provable performance guarantees for solving them.

 

Franziska Eberle studied mathematics (B.Sc.) and Mathematics of Operation Research (M.Sc.) at Technische Universität München (TUM).  She then moved for her doctoral studies to Bremen where she obtained her PhD from the “Department of Mathematics and Computer Science” at Universität Bremen (Germany) with a thesis on “Scheduling and Packing Under Uncertainty“in 2020. After her postdoctoral research in Bremen, she held a position as Research Officer at the Department of Mathematics of the London School of Economics and Political Science (UK) from 2021 to 2023.

2023-Eberle

Maximilian Engel
FU Berlin

Application-driven Random and Multiscale Dynamics

 

Maximilian Engel took up his position as head of the MATH+ Junior Research Group “Application-

driven random and multiscale dynamical systems” at FU Berlin in July 2020. His research concerns various areas within Dynamical Systems, including random dynamical systems and their parameter-dependent change (bifurcations) as well as fast-slow ODEs, SDEs and PDEs, in particular those with non-hyperbolic singularities. Additionally, he is working on stochastic models in population dynamics, with an increased focus on game theory and (chemical) reaction networks.

 

Maximilian Engel studied mathematics, economics, and philosophy at LMU Munich, Munich School of Philosophy, and Imperial College London. He received his Ph.D. in mathematics from Imperial College London in February 2018, under the supervision of Jeroen Lamb and Martin Rasmussen. Afterward, he held a Postdoc position at TU Munich within the DFG CRC 109 “Discretization in Geometry and Dynamics”, working with Christian Kuehn.

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Tim Jahn
TU Berlin

Mathematics of Data Science

 

Tim Jahn started his position as head of the MATH+ Junior Research Group “Mathematics for Data Science” at TU Berlin in September 2023. His research interests lie in the interface of numerics and statistics, focusing on applications in data science, machine learning, and imaging. He investigates adaptive techniques for high-dimensional inverse problems, dimension reduction for untrained neural networks, and stochastic optimization methods. Hereby, a particular focus is on the development of discretization-adaptive regularization, a new type of multiparametric regularization technique.

 

Tim Jahn studied physics (B.Sc.) and mathematics (M.Sc.) at Goethe Universität Frankfurt, where he received his PhD with his dissertation on “Regularizing linear inverse problems under unknown non-Gaussian noise” in 2021. Afterward, he held the Hausdorff Postdoc position at the Hausdorff Center for Mathematics in Bonn, also a mathematical Cluster of Excellence, until 2023.

 

2023-Jahn

Aswin Kannan
HU Berlin

Data-Driven Computational Optimization

 

Aswin Kannan took up his position as head of the MATH+ Junior Research Group “Data-Driven Computational Optimization” at HU Berlin in October 2021. His research targets efficient deployment of mathematical optimization to build machine learning models. Applications of interest span from Engineering and Computational Biology to Smart Energy Usage and Imaging. He also works on Derivative Free Optimization and Variational Inequalities.

 

Prior to joining MATH+, he had worked for IBM Labs as a Research Staff Member in India (2015-2021). Even earlier, he had worked for Argonne National Labs and Oracle Analytics (both in the United States). He holds a Doctorate from Penn State (United States), a Masters from University of Illinois at Urbana Champaign (United States), and a Bachelors from College of Engineering Guindy, Chennai, India.

Aswin Kannan

Fabian Telschow

HU Berlin

Statistical Inversion and Quantification of Uncertainties

 

Fabian Telschow began working as head of the MATH+ Junior Research Group “Statistical Inversion and Quantification of Uncertainties” at HU Berlin in March 2021.

 

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.

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Sarah Wolf

FU Berlin

Mathematics for sustainability transitions

 

Since December 2019, Sarah Wolf is Head of the MATH+ Junior Research Group “Mathematics for Sustainability Transitions“ at FU Berlin. Her main research interest is a mathematical foundation for empirical agent-based modeling and simulation to address societal challenges. Application topics include a macroeconomic sustainability transition as envisaged by the European Green Deal and a sustainable mobility transition, but also models of epidemic spreading. A complementary research interest is the Decision Theatre – a dialogue format for involving stakeholders and citizens in the research process.

 

Sarah Wolf studied mathematics at HU Berlin. Since her PhD studies at the Potsdam Institute for Climate Impact Research and FU Berlin, her research has been interdisciplinary. She has worked on economic and agent-based modeling in the context of Green Growth and sustainable mobility as well as the Decision Theatre at the Global Climate Forum, where she remains a Board Member.

Sarah Wolf

JRGL-Alumni