Find Academic Supervisors

Martin BurgerView profile

Professor

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +15 more

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

Felix KrahmerView profile

Assistant Professor

• Compressed Sensing
• Signal Processing
• Optimization
• +4 more

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

Christian WaldView profile

Research Fellow

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +3 more

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

Barbara DrosselView profile

Professor

• Complex Systems Theory
• Theoretical Ecology
• Random Boolean Networks
• +7 more

Barbara Drossel is a Full Professor at the Institute of Solid State Physics within the Faculty of Physics at the Technical University of Darmstadt, where she has been conducting research since February 2002. Her work bridges theoretical physics, complex systems theory, and theoretical ecology, focusing on interdisciplinary approaches to understanding emergent phenomena in natural systems. She leads the AG Drossel research group that investigates the theoretical foundations of complex networks, ecological communities, and quantum systems. Professor Drossel's research spans multiple domains with emphasis on complex systems theory, where she has made significant contributions to understanding random Boolean networks, food web modeling, and the physics of ecological communities. Her work demonstrates how simple rules can lead to complex emergent behavior across different scales, from quantum systems to ecological networks. She investigates how top-down causation operates in complex systems and explores the relationship between microscopic dynamics and macroscopic patterns in diverse contexts. Analysis of her recent publications reveals a consistent focus on theoretical frameworks that connect physics with ecology. Her work shows increasing integration of quantum mechanics with ecological modeling, particularly in understanding emergence and time evolution in complex systems. She frequently employs network theory to analyze ecological communities and has developed innovative approaches to studying species interactions, mutualistic networks, and spatial dynamics in meta-communities. Minerva Fellowship Heisenberg Fellowship DFG Fellowship for research at MIT Professor Drossel has supervised numerous doctoral students whose work spans theoretical ecology, complex systems, and statistical physics. Her research group has secured funding for projects examining the stability of ecological networks, quantum decoherence, and the mathematical foundations of complex systems. She maintains active collaborations with researchers across Europe and has contributed to major theoretical advances in understanding how complexity emerges from simple interactions in diverse systems. The AG Drossel research group operates at the intersection of physics and theoretical biology, maintaining strong connections with both the physics and biology departments at TU Darmstadt. The group combines mathematical rigor with biological relevance, developing models that capture essential features of complex natural systems while remaining analytically tractable. Their work has influenced both theoretical physics and ecological theory, demonstrating the power of interdisciplinary approaches to complex systems.

Matthew K. TamView profile

Associate Professor

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +3 more

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.

Florian BuettnerView profile

Professor

• Bioinformatics
• Machine Learning
• Multi-omics Data Integration
• +2 more

Florian Buettner is Professor for Bioinformatics in Oncology at Goethe University Frankfurt, with affiliations at the German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ). His research integrates multi-omics data with machine learning for cancer research. Research focuses on: Multi-omics bioinformatics AI for precision oncology Probabilistic modeling Single-cell analysis Uncertainty quantification Buettner received an ERC Consolidator Grant to develop trustworthy AI models for cancer diagnosis. His methodological innovations include techniques for single-cell RNA sequencing analysis and model calibration.

Thomas KoehlerView profile

Researcher

• Tomographic imaging
• Inverse problems
• Computed tomography
• +3 more

Thomas Koehler is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) , affiliated with the Department of Radiology under the Faculty of Medicine. He leads the Focus Group on Phase Contrast Computed Tomography and collaborates with Philips Research Laboratories in Hamburg. His academic work bridges industry and academia, focusing on advanced imaging techniques. Education : Studied physics at the Christian-Albrechts-University in Kiel (1989–1994), earned a Ph.D. from Philips Research Hamburg (1998). His research spans tomographic imaging, inverse problems, and iterative reconstruction algorithms for CT and phase-contrast X-ray imaging. Key contributions include advancements in helical cone-beam CT and grating-based phase-contrast imaging. Research Interests : Koehler’s work centers on tomographic imaging techniques, phase-contrast X-ray imaging, and medical diagnostics. He has pioneered innovations in dark-field chest imaging for conditions like emphysema and COVID-19 pneumonia. Collaborations with TUM’s Radiology Department drive cutting-edge iterative reconstruction methods. Labs/Teams : Leads the Phase Contrast Computed Tomography Focus Group at TUM-IAS, collaborating with industry partners like Philips. His team develops novel algorithms for medical imaging, emphasizing clinical applicability and hardware optimization.

• Mathematical Data Science
• Machine Learning
• Compressed Sensing
• +5 more

Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.

Andrea BarthView profile

Professor

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +4 more

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Andrea WaltherView profile

Professor

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +3 more

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Jürgen HesserView profile

Professor

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +7 more

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

• Bayesian Statistics
• Uncertainty Quantification
• Experimental Design
• +3 more

Dr. Arved Bartuska is a Researcher at the Department of Mathematics, RWTH Aachen University, working in the Chair for Mathematics for Uncertainty Quantification under Prof. Raúl Tempone. His research focuses on developing efficient computational methods for Bayesian optimal experimental design, particularly in the presence of nuisance uncertainty. Education: PhD (Dr. rer. nat.) from RWTH Aachen University, supervised by Prof. Raúl Tempone, with co-advisors Prof. Luis Espath (University of Nottingham) and Prof. Robert Scheichl (University of Heidelberg). Dissertation: Hierarchical Methods for Bayesian Optimal Experimental Design . M.Sc. and M.A. (fields not specified in the text). Dr. Bartuska's research centers on Bayesian statistics and uncertainty quantification . He addresses computational challenges in Bayesian optimal experimental design by developing approximations like Laplace methods and leveraging Monte Carlo techniques to handle high-dimensional parameters and nuisance uncertainties in complex models. His 2022 publication introduced a small-noise approximation for Bayesian experimental design with nuisance uncertainty, significantly reducing computational burden by avoiding nested Monte Carlo sampling. This enables practical application to engineering problems through efficient expected information gain computation. Dr. Bartuska actively presents at international venues including the SIAM Conference on Uncertainty Quantification 2022 and the Stochastic Numerics Workshop at King Abdullah University. He maintains collaborations with institutions across Saudi Arabia, the UK, and Germany within the DFG-funded IRTG 2379 framework. He is a core member of the Mathematics for Uncertainty Quantification chair, contributing to the International Research Training Group 2379 Modern Inverse Problems focused on interdisciplinary approaches to inverse problems from geometry, data, and models.

Simon AdrianView profile

Professor

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +3 more

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

• Asymptotic Statistics
• Goodness-of-Fit Testing
• Stochastic Processes
• +5 more

Bruno Ebner is a researcher at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). He maintains an active research program in theoretical and applied statistics, with particular expertise in goodness-of-fit testing and distribution characterizations. His office is located in Kollegiengebäude Mathematik (20.30) room 2.018, and he holds regular office hours on Tuesdays from 2 p.m. to 3 p.m. Dr. Ebner's primary research interests focus on asymptotic statistics , goodness-of-fit problems , stochastic processes , and distribution characterizations . His work prominently features Stein's method as a theoretical foundation for developing new statistical tests. He has made significant contributions to directional data analysis, particularly for hyperspherical data, and has developed novel approaches for testing uniformity on spheres. Analysis of his recent publications reveals a strong trend toward developing unified theoretical frameworks for goodness-of-fit testing across various distribution families. His work increasingly integrates computational methods with theoretical statistics, particularly through collaborations that bridge Stein's method with modern computational techniques. The development of R packages like gofIG, mnt, and gofgamma demonstrates his commitment to making theoretical advances accessible to practitioners. Dr. Ebner has developed several R packages that implement his theoretical work, including gofIG for Inverse Gaussian distribution testing, mnt for multivariate normality tests, and gofgamma for Gamma distribution testing. These packages represent significant contributions to statistical methodology with practical applications across various scientific domains. His teaching portfolio demonstrates expertise across multiple domains, including introductory stochastics for teaching candidates, generalized regression models, statistics for biology students, and specialized courses on Stein's method. He has also contributed to educational initiatives for economics students at KIT, reflecting his commitment to statistical education across disciplines.