Professor Dr. Martin Grepl is a faculty member at RWTH Aachen University, where he holds the Lehr- und Forschungsgebiet Optimierung mit partiellen Differentialgleichungen (Teaching and Research Area in Optimization with Partial Differential Equations). He has been affiliated with RWTH Aachen since 2009, first as a Professor (W1) and since 2014 as a Professor (W2). Education: Diplom-Ingenieur (Aerospace Engineering), University of Stuttgart (2000) Master of Science (Mechanical Engineering), MIT (2001) Doctor of Philosophy (Mechanical Engineering), MIT (2005) His research focuses on numerical methods for partial differential equations (PDEs) , particularly model order reduction , reduced basis methods , finite element methods , and optimal control for parametrized PDEs. He also investigates parameter estimation , inverse problems , and control constraints in elliptic and parabolic PDE systems. The scientific awards he has received include the Studienstiftung des deutschen Volkes (1997-2000), a Fellowship from the Dr. Jürgen Ulderup-Stiftung (1998-1999), and the Lehrpreis der Fachschaft Mathematik/Physik/Informatik (2011). His work spans applications in manufacturing , medical physics , and fluid dynamics , as evidenced by his patents and collaborative research. His publications demonstrate expertise in reduced basis methods for nonaffine/nonlinear PDEs , trust region optimization , and error bounds for real-time and many-query scenarios. His collaborations often involve interdisciplinary applications, including thermal conduction , welding processes , and glomerular filtration modeling .
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.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Prof. Dr. Wolfgang Schneider is an Honorary Professor of Mathematics Didactics at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences and Technology and the Institute of Mathematics. His work focuses on the didactics of mathematics, particularly the transition from school to university mathematics education, and algebra with emphasis on ring and module theory. Dr. Schneider's primary research interests lie in two main areas: university mathematics didactics (with special focus on the transition from school to university) and algebra (particularly the theory of rings and modules). His work bridges educational theory with practical classroom applications, developing approaches to help students navigate the challenging transition between secondary and tertiary mathematics education. In algebra, he has made contributions to ring theory, module theory, and related algebraic structures. Analysis of Dr. Schneider's publications reveals a consistent focus on mathematics education and algebra. His work spans from theoretical explorations in ring and module theory to practical applications in mathematics teaching. A significant portion of his research addresses the challenges students face when transitioning from school to university mathematics, with numerous publications focusing on geometry education, problem-solving techniques, and the use of technology in mathematics instruction. His research demonstrates a commitment to improving mathematics education through both theoretical insights and practical teaching methods. Dr. Schneider holds several notable professional memberships: Member of the Society for Mathematics Education since 1996 Founding member of the Caratheodory Society since 1999 Member of the Mathematical Association of America since 2008 Offered membership in the American Mathematical Society in 2011 Dr. Schneider actively participates in teaching and academic guidance at the University of Augsburg. He teaches courses such as "Analysis III and Number Theory/Algebra for secondary school teachers," "Linear Algebra 1," and leads a research group. His teaching approach emphasizes the connection between theoretical mathematics and practical pedagogy, particularly focusing on the transition challenges students face when moving from school to university mathematics. While specific grant information is not provided in the available materials, his extensive publication record and professional activities suggest sustained research engagement. Dr. Schneider is part of the Didactics of Mathematics team at the University of Augsburg, working alongside colleagues including Prof. Dr. Reinhard Oldenburg, Prof. Dr. Hans-Georg Weigand, and Dr. Renate Motzer. This collaborative environment supports research in mathematics education, teacher training, and the development of innovative teaching approaches for both school and university settings.
Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.
Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.
Dr. Constantin Christof is a Lecturer (Akademischer Rat auf Zeit) at the Department of Mathematics , Technische Universität München , with prior roles as a W2 Stand-in Professor at Universität Augsburg and Research Associate at TUM and TU Dortmund. His research focuses on Optimal Control of PDEs , Variational Inequalities , and Nonsmooth Optimization , with applications in Non-Newtonian Fluids and Neural Networks . May 2015 - July 2018: Dr. rer. nat. in Mathematics, TU Dortmund Oct. 2013 - July 2014: MAST (Part III of Mathematical Tripos), University of Cambridge Oct. 2009 - Sept. 2012: B.Sc. in Technomathematics and Mathematics, TU Dortmund Christof's work bridges Finite Element Error Analysis , Sensitivity Analysis , and Physics-Guided Machine Learning , particularly in problems involving Contact Mechanics and Parabolic PDE Constraints . His recent publications address challenges in Semilinear Elliptic PDEs , Obstacle Problems , and Nonsmooth Superposition Operators , with a focus on theoretical and numerical advancements. Scientific awards include the Dissertation Award and Best Graduate Award from TU Dortmund, and the Award for Academic Excellence by the Minister President of North Rhine-Westphalia. He has supervised 11 theses at the Master's and Bachelor's levels, covering topics from Neural Network Surrogate Models to Bingham Fluid Simulations .
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.
Prof. Dr. Irwin Yousept is a Full Professor of Mathematics at Universität Duisburg-Essen, leading the research group AG Optimal Control of Partial Differential Equations. His work focuses on the mathematical analysis and numerical solutions of electromagnetic problems, particularly in superconductivity and inverse problems. He holds a PhD from TU Berlin (2008) and has held academic positions at TU Darmstadt and TU Berlin. His research includes PDE-constrained optimization, numerical analysis, and applications in high-temperature superconductivity and electromagnetic shielding. Affiliations: Universität Duisburg-Essen, Fakultät für Mathematik Education: Diplom (2005), PhD (2008) in Mathematics from TU Berlin Research interests span Maxwell's equations, numerical methods for PDEs, and optimal control, with applications in superconductivity, electromagnetic shielding, and induction heating. He has authored over 40 publications and received awards including the Richard-von-Mises-Preis GAMM (2014). Current grants include DFG-funded projects on inverse problems and superconductivity.
Prof. Boris Vexler, born in 1977 in Moscow, Russia, is a Professor of Optimal Control at the TUM Department of Mathematics under the TUM School of Computation, Information and Technology . He has served as Dean of Studies since 2015 and as speaker of the International Research Training Group IGDK 1754 since 2012. Education: Diploma (2000) and Ph.D. (2004) in Mathematics from the University of Heidelberg; Habilitation (2008) from the University of Graz. His research focuses on numerical analysis of partial differential equations (PDEs) , particularly finite element methods for optimal control problems governed by parabolic, elliptic, and hyperbolic PDEs. Key contributions include error estimates , adaptive discretization , and handling state constraints and measure-valued controls . Recent publications emphasize transient Stokes equations , Navier-Stokes control , and sparsity-constrained optimization . His work often integrates scientific computing and uncertainty quantification . Scientific Awards: Award for best supervisor of the elite degree program TopMath (2018) Finalist, ECCOMAS Prize for best dissertation (2005) Leslie Fox Prize in Numerical Analysis, 2nd place (2004)
Jakob Wagner is a Research Fellow at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. He works within the Chair of Optimal Control under Prof. Boris Vexler and serves as an Invited Lecturer at Kutaisi International University in Georgia since 2022. His educational background includes a Master's degree in Mathematics from TUM (2019-2020, grade 1.0) and a Bachelor's degree in Mathematics from TUM (2014-2018, grade 1.2). Wagner's research focuses on Optimal Control of fluid dynamics equations, particularly the Navier-Stokes and Stokes systems. He specializes in Finite Element Methods and rigorous Error Estimates for discretizations of partial differential equations. His work addresses time-dependent problems, state constraints, and boundary control mechanisms in computational fluid dynamics, with significant contributions to stream-function formulations and pressure boundary conditions. Analysis of his publication record reveals a concentrated research trajectory in numerical analysis of incompressible flow control. His work consistently advances theoretical foundations for finite element discretizations, emphasizing fully discrete error analysis and pointwise constraints across Stokes and Navier-Stokes frameworks. Key innovations include novel approaches to blood flow modeling and ocean current reconstruction through coupled ODE systems. Wagner actively mentors students, supervising Master's theses by Alexandro Jedaidi (2025, ocean current reconstruction), Chenhong Lin (2025, Stokes equations), and Hendrik Bruhse (2023, parabolic problems), plus Noah An der Lan's Bachelor thesis (2025, Bayesian experiment design). His teaching portfolio spans Analysis, Optimization, and Numerical Methods courses at TUM and Kutaisi International University. He operates within TUM's Chair of Optimal Control research ecosystem, contributing to international collaborations through conference presentations at GAMM Annual Meetings and specialized symposia, while maintaining active involvement in computational mathematics projects including robotic swarm applications for radio imaging.
Kristina Bätz (née Meth) serves as an Associate Professor at the Chair of Mathematics IX (Inverse Problems) at the University of Würzburg. She is part of the Institute of Mathematics located at Campus Hubland Nord in Building 30 (Mathematics West), Room 00.018. Her contact information includes phone number +49 931 31-88700 and fax +49 931 31-838430. Dr. Bätz specializes in Inverse Problems, with particular focus on the mathematics of computed tomography as evidenced by her involvement with the Wuemax research project. Her research bridges theoretical mathematics with practical applications in imaging sciences. Within the teaching domain, Dr. Bätz offers advanced seminars in scientific computing, supervises final theses, and conducts oral examinations. Her academic work centers on mathematical approaches to solving inverse problems, which have significant applications in medical imaging and other scientific fields requiring reconstruction from indirect measurements. She maintains regular office hours by appointment, with initial contact typically established via email to facilitate scheduling and academic inquiries.
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.
Constanze Neutsch is a Doctoral Researcher at the University of Hamburg's Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences. Her research focuses on gradient robust discretizations for inverse problems in nonlinear flow problems, contributing to applied mathematics and optimization. She is affiliated with the Applied Mathematics (AM) group and is based at the Geomatikum building, Room 119. Contact details include a telephone number (+49 40 42838-5125) and email. Her work integrates advanced numerical methods to address complex flow dynamics, emphasizing robust computational techniques for solving inverse problems. While specific publications are not listed here, her research aligns with interdisciplinary efforts in applied mathematics and engineering applications.
Michael Wibmer is a Lecturer in Pure Mathematics at the University of Leeds since 2023. Previously, he held positions at Graz University of Technology, the University of Notre Dame, the University of Pennsylvania, and RWTH Aachen University. He earned his PhD from the University of Heidelberg under the supervision of B.H. Matzat. His research focuses on algebraic, algorithmic, and arithmetic aspects of differential and difference equations, including Galois theory and algebraic groups. Key areas include algebraic methods in dynamical systems, symbolic computation, and connections to model theory and number theory. He organizes events like the Online Kolchin Seminar in Differential Algebra and co-organized workshops such as DART XI (2023) and the MSRI Summer School on Differential Equations (2022). Education: PhD in Mathematics, University of Heidelberg (2010), Habilitation in Mathematics, RWTH Aachen University (2015) Research Interests: Galois theory of differential equations, proalgebraic groups, difference algebraic groups, number theory, and symbolic computation Recent Activities: Organized the Algebraic Theory of Differential and Difference Equations workshop at Leeds (2024) His publications span topics like torsors under affine group schemes, regular singular differential equations, and étale difference algebraic groups, reflecting his expertise in algebraic structures and their applications to differential systems.
