Dr. Sven Burger is a leading Researcher at the Zuse Institute Berlin (ZIB) within the Modeling and Simulation of Complex Processes department. His work focuses on Nanophotonics , Quantum Technologies , and Optical Resonance Computation , particularly in photonic crystals, plasmonic systems, and quantum light sources. Key projects: NanoLab GRIPS 2024 , MATH+ TES QT , MATH+ PaA-1 (perovskite solar cells), Colour Impression of Solar Cells Collaborations: MATH+ , BIFOLD , Research Campus MODAL His research spans Bayesian optimization for quantum systems, quasinormal mode expansions , chiral plasmonics , and terawatt-scale photovoltaics . Recent work emphasizes RPExpand software for resonance analysis and AAA algorithm applications in photonic design. He contributes to quantum key distribution via plug&play single-photon sources, hot carrier dynamics in plasmonic nanocrystals, and high-efficiency light extraction for deep-UV LEDs. His computational methods address non-Hermitian systems , exceptional points , and self-interference nanoparticle tracking .
Jochen Merker serves as Professor for Analysis and Optimization at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). His academic profile demonstrates deep expertise in mathematical analysis, numerical methods, and computational mathematics with applications across various scientific domains. Institution: Leipzig University of Applied Sciences (HTWK Leipzig) Faculty: Computer Science and Media Position: Professor for Analysis and Optimization Contact: Available by appointment via email Professor Merker's research spans multiple mathematical disciplines with particular emphasis on partial differential equations, numerical analysis, and mathematical modeling. His work bridges theoretical mathematics with practical applications in fluid mechanics, epidemiology, and machine learning. He has made significant contributions to the understanding of doubly nonlinear evolution equations, positivity preservation in numerical methods, and rate-induced tipping phenomena. His research demonstrates how advanced mathematical techniques can solve complex problems in physical systems and data science. Analysis of his publication trends reveals a consistent focus on mathematical rigor combined with practical applicability. His recent work shows increasing integration of mathematical theory with computational approaches, particularly in digital learning environments and e-assessment systems for STEM education. The interdisciplinary nature of his publications demonstrates how mathematical analysis serves as a foundation for solving problems across physics, engineering, epidemiology, and computer science. Primary research areas: Mathematical Analysis, Numerical Methods, Partial Differential Equations Application domains: Fluid Mechanics, Epidemiology, Machine Learning Methodological focus: Positivity preservation, Maximum principles, Numerical stability Educational contributions: Digital teaching in STEM fields, E-assessment systems Professor Merker actively contributes to the academic community through his research publications and educational initiatives. His work on digital teaching methods for STEM disciplines reflects his commitment to modernizing mathematical education. While specific grant information isn't available in the provided materials, his extensive publication record suggests sustained research activity across multiple projects. His laboratory or research team likely focuses on computational mathematics and numerical analysis, though specific details aren't provided in the source material.
Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.
Dr. Andreas Englert is a Senior Scientist at the Department of Applied Geology , Martin Luther University of Halle-Wittenberg, and a Lecturer at the University of Cologne. His work bridges hydrogeology and hydrogeophysics.
Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.
Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.
Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.
Hans-Peter Seidel is a leading researcher in computer graphics at the Max Planck Institute for Informatics, part of the Max Planck Society. His work focuses on advancing the frontiers of image synthesis, neural rendering, and computational photography, with a strong emphasis on high dynamic range imaging, inverse rendering, and perception-aware graphics techniques. His research interests span a broad spectrum of computer graphics and vision, including neural radiance fields, Monte Carlo denoising, image deblurring, and visual perception modeling. He has made significant contributions to real-time rendering, HDR image generation, and uncertainty-aware AI for scientific applications. His lab collaborates closely with experts in rendering, perception, and machine learning, pushing the boundaries of what is possible in digital image creation and manipulation. The most recent publications reveal a strong trend toward integrating deep learning with traditional graphics pipelines, particularly through differentiable rendering, neural fields, and adversarial training. His work frequently appears in top venues such as SIGGRAPH, ACM Transactions on Graphics, and Computer Graphics Forum, reflecting sustained impact and innovation in the field. Hans-Peter Seidel has not been publicly associated with any formal scientific awards in the provided text. However, his extensive publication record and leadership at a premier research institute underscore his influential role in the academic community. While there is no explicit mention of student advising or grant funding in the provided material, his collaborative publications with junior researchers suggest active mentorship. His work is likely supported by institutional funding from the Max Planck Society, enabling long-term, high-risk research in computer graphics and AI. He is part of a vibrant research group at the Max Planck Institute for Informatics, specializing in computer graphics. The team works on cutting-edge problems in rendering, perception, and machine learning, often bridging the gap between theoretical innovation and practical applications in virtual reality, computational photography, and scientific visualization.
Dr. Jan Bartsch is a Lecturer at the Institute of Mathematics, University of Würzburg, Germany. He has been working as a Scientific Employee at the Institute of Mathematics since 2024. Prior to this, he was a Postdoc at the University of Konstanz from 2021 to 2024 in the Collaborative Research Center 1432 "Fluctuations and Nonlinearities in Classical and Quantum Matter Beyond Equilibrium." Dr. Bartsch's educational background includes: PhD in Scientific Computing from the University of Würzburg (2018-2021), dissertation title: "Theoretical and numerical investigation of optimal control problems governed by kinetic models" Master's degree in Mathematics from the University of Würzburg (2016-2018), thesis title: "Optimal control problems governed by Liouville models - Mathematical analysis and implementation" Bachelor's degree in Computational Mathematics from the University of Würzburg (2013-2016), thesis title: "Optimal Control of Androgen Suppression of Prostate Cancer" His research focuses on optimal control theory, numerical methods for partial differential equations, and kinetic models. Dr. Bartsch specializes in Monte Carlo methods for solving kinetic models, optimal control of stochastic differential equations, and numerical methods for hyperbolic differential equations. His work bridges theoretical mathematics with practical computational approaches, particularly in the context of control problems governed by complex physical models. Dr. Bartsch's recent publications show a strong focus on applying adjoint-based methods to optimal control problems, particularly involving stochastic processes and kinetic models. His research demonstrates expertise in developing numerical frameworks that combine Monte Carlo approaches with control theory to solve complex mathematical problems arising in physics and engineering applications. Contact Information: Email: jan.bartsch@uni-wuerzburg.de Phone: +49 931 31-80733 Office: Building 40 (Mathematics East), Room 00.012, Emil-Fischer-Straße 40, 97074 Würzburg
Dietmar Weinmann is a Senior Researcher at the CNRS (Centre National de la Recherche Scientifique) affiliated with the IPCMS (Institut de Physique et Chimie des Matériaux de Strasbourg) and the University of Strasbourg. His work focuses on theoretical solid-state physics, particularly quantum effects in electronic properties, mesoscopic physics, and quantum transport phenomena. His research explores non-local heating in quantum thermoelectrics, scanning gate microscopy applications in graphene and semiconductor heterostructures, power dissipation asymmetry in quantum point contacts, and orbital magnetization mechanisms in mesoscopic systems. Key themes include electron correlations, spin-orbit interactions, and disorder effects in nanoscale devices. Scientific awards include the Marie Curie Fellowship during his postdoctoral work at SPEC Saclay. He teaches an elective course on Electronics for Quantum Science and Technology at the University of Strasbourg. As a member of the Mesoscopic Quantum Physics team, his research combines theoretical modeling with experimental collaborations on quantum transport imaging and inverse problem solving via machine learning.
Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations
Prof. Dr. Barbara Verfürth is a Professor at the Institute for Numerical Simulation (INS) at the University of Bonn since October 2022. Previously, she served as a Junior Research Group Leader and Tenure-Track Professor at Karlsruhe Institute of Technology (KIT) from 2020-2022, was a PostDoc at the University of Augsburg (2018-2020), and completed her PhD at the University of Münster (2015-2018). Her academic journey demonstrates a strong focus on numerical analysis and computational mathematics. Prof. Verfürth's research interests span several interconnected areas in computational mathematics: Numerical methods for partial differential equations Multiscale (finite element) methods (Numerical) homogenization (Time-harmonic) wave propagation: Helmholtz and Maxwell equations Nonlinear PDEs (nonlinear diffusion, nonlinear Helmholtz) Her recent publications demonstrate a consistent focus on multiscale methods for wave propagation problems, particularly in high-contrast and time-varying media. The research shows strong connections between theoretical numerical analysis and practical applications in metamaterials and wave physics. Her work bridges pure mathematical analysis with computational implementation, often developing novel algorithms for challenging multiscale problems. Prof. Verfürth leads several significant research projects: Homogenization of time-varying metamaterials (Project B4, DFG CRC 1173) Numerical methods for nonlinear, random and dynamical multiscale problems (Project 496556642, DFG Emmy Noether) Previously completed TEEMLEAP - a testbed for exploring machine learning in atmospheric prediction (KIT Future Fields) She is actively involved in academic mentoring, currently advertising for a PhD position focusing on numerical multiscale methods for linear elasticity with high-contrast coefficients. Her teaching includes courses such as "Scientific Computing I" and "Introduction to Numerical Mathematics" at the University of Bonn.
Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.
Hannah Laus is a Researcher at the Department of Mathematics within the School of Computation, Information and Technology at the Technical University of Munich. Her work focuses on advanced data science methodologies, particularly in uncertainty quantification, high-dimensional learning, and optimization algorithms. She contributes to research groups including Data Science, Probability Theory, and Numerical Analysis. Her research interests revolve around addressing challenges in inverse problems, machine learning, and statistical learning theory. Notable contributions include studies on uncertainty quantification in undersampled medical imaging and convergence guarantees for deep learning algorithms. Recent publications emphasize interdisciplinary applications of mathematical techniques in high-dimensional settings and iterative thresholding algorithms. No scientific awards are explicitly listed, but her involvement in collaborative projects like the TUM-ICL Mathematical Sciences Hub and the Cluster of Excellence MCQST highlights her active role in academic partnerships. As part of the academic staff, Laus is affiliated with the TUM’s outreach initiatives such as TUM-Entdeckerinnen and the Data Innovation Lab, promoting mathematical education and innovation. No doctoral advisees are listed in the provided information.
Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.
