Professor Stefan Gumhold is a leading academic in computer graphics and visualization at TU Dresden. He has held roles including Head of the Chair for Computer Graphics and Visualization since 2005 and served as Dean of the Faculty of Computer Science from 2010 to 2012. His expertise spans visualization techniques, medical imaging, and immersive technologies. Gumhold earned his Ph.D. in 2000, focusing on mesh compression, and later received a Dissertation Award in the same year. He has contributed to projects like the Heisenberg Scholar Research Group and led the Kommission Umwelt at TU Dresden. His teaching includes courses such as Introduction to Computer Graphics, Data Visualization, and Scientific Visualization. Gumhold’s research emphasizes visualization platforms (e.g., ISAAC), image fusion, and neural network applications in medical diagnostics. His work often integrates virtual reality for data analysis, as seen in tools like VRCellLabeler and ExtremeWeatherVis. Education: Diploma in Computer Science (University of Tübingen, 1998), M.Sc. in Applied Physics (University of Massachusetts Boston, 1996). Research Highlights: Development of visualization frameworks, 3D reconstruction, and invertible neural networks for outlier detection. Awards: Dissertation Award 2000. Grants & Labs: Leadership in TU Dresden’s Computer Graphics lab and contributions to collaborative research projects like Fast-Haptic.
Reinhard Heckel is a Professor of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM). His career includes positions as a Tenure-Track Assistant Professor at Rice University (2017–2019) and postdoctoral fellow at UC Berkeley's Berkeley Artificial Intelligence Research Lab. He holds a PhD from ETH Zurich (2014) and conducted doctoral research at Stanford University’s Statistics Department. Recognitions include being named one of Germany's 'Top 40 under 40' (2022) and the Werner von Siemens Ring Foundation Award (2022). Education & Professional Background: PhD in Computer Science, ETH Zurich (2014) Visiting Doctoral Fellow, Stanford University (Statistics Department) Postdoctoral Fellowship, UC Berkeley (EECS Department) Research Focus: His work bridges theoretical foundations and practical applications in machine learning, including: Algorithm development for deep learning and medical image processing Mathematical foundations of machine learning DNA data storage technology (error correction, synthesis methods) Computational imaging and inverse problem solutions Awards & Highlights: 2022: Capital 40 under 40, Werner von Siemens Ring Foundation Award 2015: ETH Zurich Medal for Doctoral Thesis, IBM Invention Achievement Award Grants & Collaboration: His research has been supported by grants focusing on DNA storage scalability and MRI reconstruction. He collaborates with institutions like IBM Research and the Berkeley AI Lab. Key projects include developing DNA synthesis methods and AI-driven medical imaging tools. Labs & Teams: Leads TUM's machine learning initiatives in computational imaging and biological data storage systems. Active in interdisciplinary teams bridging computer science, bioengineering, and statistics.
Dr. Marcel Padilla is a Postdoctoral Researcher at the Interactive Geometry Lab within the Department of Computer Science at ETH Zürich, supported by the Feodor-Lynen Fellowship from the Alexander von Humboldt Foundation. Previously, he completed his PhD at TU Berlin under Professors Ulrich Pinkall and Peter Schröder, focusing on solar corona modeling and geometry processing. Education: PhD in Computer Science, TU Berlin (2023) MSc in Mathematics, TU Berlin (2018) BSc in Mathematics, TU Berlin (2016) Research Interests: Marcel’s work spans physical simulations, geometry processing, fluid dynamics, and plasma modeling. He explores applications in computer graphics, such as modeling solar corona dynamics and vortex filament behavior. His methods often leverage discrete exterior calculus and variational principles to achieve computational efficiency and accuracy. Key Contributions: Recent work includes Exact 3D Green’s Function Integrations on Triangles (2025), Going with the Flow (2024), and Filament Based Plasma (2022). These studies address challenges in fluid-structure interaction, plasma visualization, and efficient numerical integration. Awards: Feodor-Lynen Fellowship (Alexander von Humboldt Foundation) Teaching & Outreach: Marcel has taught courses on Discrete Differential Geometry at ETH Zürich and Geometry Processing at TU Berlin. He also develops practical resources for Houdini programming and scientific presentation design. Labs & Collaborations: His research is supported by institutions like the DFG Collaborative Research Center TRR 109 and the Einstein Foundation Berlin. He actively contributes to open-source projects, including implementations of his publications on GitHub.
apl. Prof. Dr.-Ing. Claus Brenner is an Adjunct Professor at the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on LiDAR mapping, point cloud processing, and robust estimation, with applications in autonomous systems, urban mapping, and disaster risk assessment. He leads the Graduiertenkolleg 2159 research group on integrity and collaboration in dynamic sensor networks. Key research areas include 3D reconstruction, SLAM (Simultaneous Localization and Mapping), semantic segmentation of mobile mapping data, and cooperative perception systems. His work integrates advanced machine learning techniques with geospatial data analysis, addressing challenges in sensor fusion, uncertainty modeling, and real-time localization. Recent publications span topics like voxel-based point cloud localization for smart spaces, flood risk mapping using LiDAR, and adversarial shape completion. Brenner has contributed to benchmark datasets such as LuCoop and LUMPI, advancing research in cooperative perception and urban navigation. His methods emphasize robustness and scalability, often leveraging generative models and statistical frameworks for urban environment analysis. Notable projects include the development of high-definition mapping using LiDAR, trajectory-based road network reconstruction, and semantic annotation from user trajectories. His work bridges theoretical advancements in computer vision with practical applications in autonomous systems and smart infrastructure.
Professor Daniel Wachsmuth holds the Chair of Mathematics VII (Optimal Control) at the University of Würzburg, where he has been a professor since 2012. He is affiliated with the Faculty of Mathematics and Computer Science and maintains his office in Building 30 (Mathematics West), Room 02.011. Prior to his current position, he completed his academic training with positions as a postdoc at RICAM in Linz, Austria (2008-2012) and as a research assistant at TU Berlin (2002-2008). Wachsmuth's research focuses on optimal control theory , particularly concerning partial differential equations, nonsmooth optimization problems, and regularization of problems with bang-bang control. His work bridges theoretical mathematical analysis with practical numerical methods for solving complex control problems. He has made significant contributions to the understanding of sparse optimization, topological derivatives, and non-convex optimization problems in function spaces. His recent publications demonstrate a strong trend toward addressing L 0 constraints in optimal control, developing advanced numerical methods for non-smooth problems, and exploring connections between optimal control theory and deep neural networks. His work spans both theoretical developments (such as second-order conditions and stability analysis) and practical algorithmic implementations for solving challenging optimization problems. Among his notable recognitions is the Dimitrie-Pompeiu Preis awarded in 2016. His research has been published in top-tier journals including SIAM Journal on Control and Optimization, Computational Optimization and Applications, and Inverse Problems. Current Position: Professor of Mathematics at University of Würzburg (since 2012) Previous Positions: Postdoc at RICAM, Linz (2008-2012); Research Assistant at TU Berlin (2002-2008) Contact: daniel.wachsmuth@uni-wuerzburg.de; +49 931 31-89071
Martin Wahl is a Professor (W2) in the Faculty of Mathematics at Bielefeld University, appointed in 2023. He obtained his doctorate from Heidelberg University in 2015, followed by postdoctoral research at Humboldt University of Berlin (2015-2022) and a Feodor Lynen Research Fellowship at Georgia Tech. His research centers on mathematical statistics with emphasis on: High-dimensional statistics and probability theory Statistical learning theory and dimension reduction techniques Nonparametric estimation and minimax optimality Spectral methods for covariance operators and random matrices His recent articles (2020-2025) demonstrate strong focus on spectral methods, perturbation analysis, and error bounds in high-dimensional settings, with applications spanning manifold learning, PCA optimization, and stochastic PDEs. Theoretical developments in minimax estimation and concentration inequalities form consistent themes. Awards: Feodor Lynen Research Fellowship (Georgia Tech) He leads the project "Convexity and Grassmann Manifolds in Statistical Inference" (2027) under the Priority Program "Combinatorial Synergies". He is affiliated with the Bielefeld Graduate School in Theoretical Sciences and the Center for Statistics.
David Russell Luke is a Professor of Continuous Optimization at the Institute for Numerical and Applied Mathematics, University of Göttingen, where he also serves as Managing Director of the Institute. He holds editorial positions as Area Editor for the Open Journal of Mathematical Optimization and Associate Editor for multiple prestigious journals including Journal of Optimization Theory and Applications, ESAIM: Control, Optimization and Calculus of Variations, SIAM Journal on Optimization, and Advances in Computational Mathematics. Dr. Luke earned his BSc with honors in Applied Mathematics from the University of California, Berkeley in 1991, followed by an MSc (1997) and PhD (2001) in Applied Mathematics from the University of Washington under James Burke. His academic journey included positions at the University of Göttingen (2001-2003), Simon Fraser University (2002-2004), and University of Delaware (2004-2009) before returning to Göttingen. His research focuses on Continuous Optimization, Variational Analysis, and Inverse Problems , with particular expertise in nonsmooth and nonconvex optimization, phase retrieval, and computational imaging. His work bridges theoretical mathematics with practical applications in photonic imaging, tomography, and adaptive optics. Current research projects include atomic orbital tomography, stochastic computed tomography for X-FEL imaging, probabilistic analysis in fixed point theory, and topological optimization for tree structure analysis. Analysis of his recent publications reveals a strong trend toward computational methods for imaging science, particularly phase retrieval problems, with increasing focus on three-dimensional reconstruction techniques and applications in photoemission orbital tomography. His work consistently integrates theoretical convergence analysis with practical algorithm development, often implemented in the ProxToolbox software framework. NASA/GSFC Graduate Student Research Fellow (1998-2001) Editorial roles with multiple leading optimization journals Principal investigator on numerous DFG-funded research projects Dr. Luke has advised several PhD students including Patrick Neumann and Thao Nguyen. His research has been supported by significant grants from the National Science Foundation, German Research Foundation (including Collaborative Research Center 755, Graduiertenkolleg 2088), Bundesministerium fuer Bildung und Forschung, German Israeli Foundation, and Australian Research Council. He leads the Working Group on Continuous Optimization, Variational Analysis and Inverse Problems at the University of Göttingen, which maintains the ProxToolbox software laboratory for proximal algorithms and optimization methods. The group actively develops computational tools for inverse problems and optimization, with applications ranging from space telescope wavefront reconstruction to atomic-scale imaging. Current projects are organized within the Collaborative Research Center 1456 and Graduiertenkolleg 2088 frameworks, focusing on mathematical modeling of complex imaging scenarios and developing efficient numerical algorithms for large-scale optimization problems.
Jack B. Muir is a Marie Skłodowska-Curie Fellow at the University of Oxford's Department of Earth Sciences and Junior Research Fellow at Wolfson College. His research integrates advanced mathematics with seismology to address inverse problems in Earth imaging and hazard assessment. Education: PhD in Geophysics, Caltech Seismolab (2021) Research focuses on physics-informed neural networks for seismic wavefield simulation (TerraPINN project), nonparametric seismicity rate modeling using deep Gaussian processes, geologically-constrained tomography, and Bayesian methods for wavefield reconstruction. His work targets applications from near-surface structures to Earth's core, emphasizing machine learning acceleration and uncertainty quantification in inverse problems. Recent projects include Distributed Acoustic Sensing (DAS) optimization and seismic swarm analysis. Publication trends (2022-2025) reveal strong emphasis on machine learning integration (PINNs, Gaussian processes) with geophysical inverse problems, particularly for DAS data processing, deep Earth imaging, and probabilistic hazard assessment. Key themes include multi-scale analysis, instrument response calibration, and computational efficiency. Scientific Awards: Marie Skłodowska-Curie Fellowship John Monash Scholarship Junior Research Fellowship at Wolfson College, Oxford Grant-funded projects include TerraPINN for physics-based seismic hazard assessment and collaborations leveraging Caltech's Community Seismic Network. He actively develops open-source tools for core-mantle boundary modeling and DAS data processing. Labs and teams involve Oxford's Seismology group (Tarje Nissen-Meyer), Caltech (Zach Ross), Australian National University (Hrvoje Tkalčić), and JAMSTEC (Satoru Tanaka), with fieldwork utilizing ocean-bottom seismometers and urban sensor networks.
Dr. Deniz Bezgin is a Researcher at the Department of Aerodynamics and Fluid Mechanics of the Technische Universität München (TUM) . Her work focuses on computational fluid dynamics (CFD), machine learning integration in numerical methods, and high-order differentiable solvers for compressible flows. Research specialties include shock-capturing methods, multi-phase flow modeling, and data-driven shape optimization. Developed JAX-Fluids, a fully-differentiable framework for compressible two-phase flows. Key contributions to ENO/WENO schemes and thermodynamically consistent interface models. Current projects explore machine-learned discretizations and GPU-based high-performance computing. Her recent publications address differentiable simulations, data assimilation, and turbulence modeling. She has not received any explicitly listed scientific awards.
Václav Snásel is a Professor at the Department of Informatics, VSB - Technical University of Ostrava, Czech Republic. He holds a PhD from Masaryk University (Brno, Czech Republic). His research focuses on optimization algorithms, machine learning, metaheuristics, data mining, and their applications in engineering and computational intelligence. Key research interests include developing novel metaheuristic algorithms (e.g., Walrus Optimizer, Artificial Protozoa Optimizer), optimization frameworks for engineering problems, and applications in wireless sensor networks, power systems, and medical diagnostics. He also explores computational methods for data analysis, including graph-based techniques and surrogate-assisted evolutionary algorithms. His recent work emphasizes multi-objective optimization, algorithm design for high-dimensional problems, and interdisciplinary applications in agriculture, energy systems, and bioinformatics. Collaborations span institutions globally, with frequent co-authorship on topics like swarm intelligence and evolutionary computation.
Frank Werner is a Professor of Mathematics at the Chair of Scientific Computing, University of Würzburg, specializing in statistical inverse problems and regularization theory. His research bridges statistics and inverse problems, with applications in biophysics, particularly fluorescence microscopy and non-Gaussian noise modeling. Education: Diplom in Mathematics (2009) and PhD (2012) from Georg-August-Universität Göttingen. Werner's research focuses on uncertainty quantification via minimax tests, nonlinear inverse problems, and photonic imaging. His recent work includes adaptive regularization methods and multiscale scanning techniques. He leads the Inverse Problems team and collaborates internationally, including at Fudan University, Technical University of Chemnitz, and the Max-Planck-Institut für biophysikalische Chemie (2014–2020). His publications span journals like Inverse Problems , Annals of Statistics , and SIAM Journal on Numerical Analysis , with trends emphasizing Poisson data, impulsive noise, and computational algorithms for microscopy. Scientific Awards: Diplomprüfung with Distinction (2009) Erskine Fellowship (2023) Werner is actively involved in academic service, organizing conferences (e.g., AIP2025 in Rio), and mentoring collaborations. He is married with two sons and maintains affiliations with societies like the EMS-TAG Inverse Problems and the Society for Inverse Problems.
Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.
Prof. Dr. Melanie Birke is a faculty member at the University of Bayreuth , holding the Professorship for Mathematical Statistics within the Faculty of Mathematics, Physics and Computer Science . Her research spans multiple areas of statistics, including nonparametric methods, functional data analysis, inverse problems, goodness-of-fit tests, and random matrices. Women's Representative of the Mathematical Institute DAV Correspondent for actuarial training exemptions Research Interests are focused on nonparametric statistics, functional data, and inverse problems. She develops asymptotic theory for estimation and testing procedures, particularly in high-dimensional or infinite-dimensional spaces, with applications to real-world issues like image distortion and data collection errors. Her work also includes constructing goodness-of-fit tests for regression models and functional data, ensuring robust statistical analysis. Publications highlight her contributions to quantile regression, symmetry testing in inverse problems, and shape-constrained density estimation. A recurring theme involves improving statistical consistency by addressing model misspecification through advanced nonparametric techniques. Statistical Consulting is offered to both internal and external stakeholders, emphasizing methodological guidance during experimental design to prevent data inconsistencies. She also supervises Bachelor's and Master's theses in nonparametric statistics and financial mathematics.
Michael Kartmann is a doctoral student and Research Assistant at the Department of Mathematics and Statistics, University of Konstanz, Germany, since October 2022. His work is funded by the BMBF ElAN project, focusing on efficient local waste heat utilization in low-temperature networks, and he collaborates with the YMMOR group (Young Mathematicians in Model Order Reduction). Research Interests: His work centers on adaptive reduced-order modeling, PDE-constrained optimization, optimal control, and domain decomposition methods for nonlinear preconditioning, with applications in switched low-temperature heat networks and large-scale dynamical systems. He also explores reinforcement learning for optimization problems. Publications: Recent submissions include methods for model predictive control of switched systems and L1-regularized optimal control. His 2024 published work details adaptive trust region reduced basis approaches for parameter identification, with a 2022 master thesis on hierarchical multiobjective optimization. Scientific Contributions: He presented talks at conferences including MORE24, IFIP24, and EUCCO23. His software contributions are available on GitHub. Teaching Roles: He supervises courses such as 'Numerical Mathematics,' 'Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control,' and 'PDE-constrained Optimization' at the University of Konstanz, collaborating with Professors Stefan Volkwein, Behzad Azmi, and others since 2022.
Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.
