Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net
Prof. Dr.-Ing. Martin Hoffmann is a Professor of Microsystems Technology at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum. His academic career began at the University of Dortmund, where he earned his doctorate in high-frequency technology and later habilitated in microsystems technology (2003). He held roles as a private lecturer and industry researcher before becoming a university professor at TU Ilmenau (2006). He joined Ruhr University in 2017, specializing in cutting-edge microsystems research. His research focuses on MEMS, THz technology, microactuators, and nanoimprint lithography. Key projects include cooperative microactuator systems, THz biosensors, and energy-autonomous sensors. He collaborates with institutions like TU Ilmenau, Purdue University, and Nagoya University through international programs like Double Degree and Erasmus. His work spans academic advising, grants, and industry partnerships (e.g., HL Planartechnik GmbH, Silicon Manufacturing Itzehoe GmbH). Notable contributions include silicon grass nanostructuring, palladium-based gas sensors, and wafer-scale MoS₂ deposition. His lab develops micromechanical systems for biomedical, environmental, and defense applications.
Professor Björn Kiefer, Ph.D. serves as Professor of Engineering Mechanics – Solid Mechanics at the Institute of Mechanics and Fluid Dynamics, Freiberg University of Mining and Technology, Germany. His academic career spans prestigious institutions including TU Dortmund University and the University of Stuttgart in Germany, and Texas A&M University in the USA. His educational background includes: PhD in Aerospace Engineering from Texas A&M University (2006), with doctoral thesis "A Phenomenological Constitutive Model for Magnetic Shape Memory Alloys" under Dr. Dimitris C. Lagoudas Diploma in Mechanical Engineering/Applied Mechanics from Ruhr University Bochum (2001), with diploma thesis "Revisit and Characterization of a Thermomechanically Coupled Constitutive for the Description of Polycrystalline Shape Memory Alloys" under Prof. Dr.-Ing. Otto T. Bruhns Professor Kiefer's research expertise centers on continuum mechanics with geometric and physical nonlinearities, constitutive modeling of active materials, and electromagnetic-mechanical coupling phenomena. His work integrates computational mechanics, micromechanics, and multiscale modeling approaches to address complex problems in phase transformations, plasticity, damage mechanics, and fracture analysis. His research has significant applications in developing advanced multifunctional materials for engineering systems. His scientific recognition includes: Elected Fellow of ASME (2021) Multiple invited professorships in France Prestigious early-career recognition programs Graduate student awards including best paper presentation Professor Kiefer actively contributes to the academic community as Treasurer (and former Secretary 2020-2021) of the ASME Aerospace Division's Adaptive Structures and Material Systems Branch since 2011. He serves on technical committees for active materials and adaptive systems, and reviews for numerous top-tier mechanics journals. He has organized multiple international conferences including symposia at ASME events and GAMM meetings, demonstrating leadership in his field. His teaching portfolio includes Continuum Mechanics, Higher Strength Theory, Plasticity, and core Technical Mechanics courses covering statics and strength of materials.
Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.
Nina Otter serves as a Computational and Applied Mathematics Assistant Professor in the Department of Mathematics at the University of California, Los Angeles. She previously held a postdoctoral position in the Nonlinear Algebra group at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) through December 2018. Dr. Otter also acts as an executive editor for the open-access journal Compositionality , which commenced operations in August 2018. Her research program centers on applying algebraic topology and geometric methods to analyze complex-structured data. Current initiatives include developing topological signatures for galactic morphology classification and implementing persistent homology techniques for climate data interpretation. She maintains active theoretical interests in category-theoretic approaches to group finiteness properties, bridging abstract mathematics with real-world applications across astrophysics and environmental science. Dr. Otter's primary research affiliation was with the Nonlinear Algebra group at MPI MiS during her postdoctoral tenure (2017-2018), where she contributed to interdisciplinary projects connecting pure mathematics with data science. Her editorial work at Compositionality establishes her role in advancing research on compositional structures in mathematical sciences, reflecting her commitment to open-access scholarly communication.
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.
Prof. Dr. Hartmut Ruhl is a Professor (chair) at the Faculty of Physics of Ludwig-Maximilians-Universität München (LMU Munich), with his office located at Theresienstrasse 37, Room A237 in Munich, Germany. He leads an active research group focused on high field physics and quantum electrodynamics, particularly investigating radiation reaction, vacuum effects, and strong field phenomena. Prof. Ruhl's research spans several cutting-edge areas of theoretical and computational physics. His primary interests include high field physics, quantum electrodynamics in strong fields, radiation reaction effects, vacuum polarization phenomena, and computational methods for solving complex physical systems. He has made significant contributions to understanding the Heisenberg-Euler effective Lagrangian, vacuum high harmonic generation, and the Trident process for electron-positron pair production. His work combines theoretical developments with advanced numerical simulations to explore physics in extreme electromagnetic field conditions. Prof. Ruhl's publication record demonstrates consistent focus on nonlinear quantum electrodynamics in strong fields. His work spans theoretical developments in radiation reaction, numerical methods for solving Heisenberg-Euler equations, and investigations of vacuum effects like high harmonic generation and pair production. A recurring theme is the exploration of quantum vacuum nonlinearities and their observable consequences in high-intensity laser-matter interactions. Prof. Ruhl actively supervises PhD students in high field physics, requiring profound knowledge in quantum transport theory and advanced programming. His group seeks candidates who have completed his courses in Relativistic Quantum Theory and Advanced Programming. He co-organizes the seminar 'Selected Topics in Computational Physics' with Prof. A. Scrinzi, serving as a platform for master's and PhD students interested in computational plasma physics. Prof. Ruhl is associated with the Advanced Simulation Center (ASC) at LMU Munich, as indicated by room locations in his teaching schedule. He collaborates closely with Prof. A. Scrinzi on computational physics topics and is involved with the PSC (Plasma Simulation Code) project. His research group develops specialized numerical solvers for nonlinear wave equations based on the Heisenberg-Euler effective Lagrangian, contributing to the understanding of quantum vacuum effects in extreme field conditions.
Patricia Martinkova is an Associate Professor at the Faculty of Education, Charles University , a Senior Researcher leading the Department of Statistical Modelling at the Institute of Computer Science, Czech Academy of Sciences , and an Affiliate Associate Professor at the University of Washington (Statistics and Social Sciences). She is also the founder of the Computational Psychometrics Group and the Center for Educational Measurement and Psychometrics at Charles University. Her research focuses on advanced psychometric models and estimators for granular insights in education, psychology, and health, with emphasis on inter-rater reliability , differential item functioning (DIF) , and reproducible research via tools like ShinyItemAnalysis . She has developed software packages ( difNLR , SIAmodules , SIAtools ) and authored the book Computational Aspects of Psychometric Methods. With R (2023). Recent projects include the 2025–2027 EduCoDe (Czech Science Foundation) and 2024–2028 Digital Technologies and Wellbeing (EU-funded). She has received recognition as a Fulbright Alumna (2013–2015) and organized the IMPS 2024 conference (570+ participants). Teaching includes courses on Statistical Methods in Psychometrics and Item Response Theory , incorporating active learning and R-based tools.
Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771
Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.
Peter Massopust is a Privatdozent at the Technical University of Munich (TUM), where he is affiliated with the School of Computation, Information and Technology and the Department of Mathematics. His research spans multiple areas of mathematical analysis with a focus on fractal geometry, wavelet theory, and approximation methods. His educational background includes: Habilitation in 2011 from Technical University of Munich Ph.D. in Applied Mathematics from Georgia Institute of Technology (1986) MS in Mathematics from Georgia Institute of Technology (1985) MS in Physics from Georgia Institute of Technology (1981) Dr. Massopust's research interests primarily focus on Wavelets and Frames, Harmonic and Functional Analysis, Fractal Geometry and Fractal Interpolation Theory, and Splines and Approximation Theory. His work bridges theoretical mathematics with practical applications in signal processing, image analysis, and computational methods. His approach often combines classical mathematical techniques with innovative fractal-based methods to solve complex problems in approximation theory and functional analysis. His research has significantly contributed to the development of fractal interpolation functions, complex splines, and wavelet theory, with applications spanning from pure mathematics to engineering problems. His publication record demonstrates a consistent focus on fractal-based mathematical methods, with recent work expanding into quaternionic analysis, complex B-splines, and applications in signal processing. His research shows a clear trajectory from foundational work in fractal geometry to increasingly sophisticated applications in multidimensional signal analysis and computational mathematics. His scientific achievements have been recognized through several prestigious awards: Fulbright Scholarship (1980-1981) GIAN (Global Initiative for Academic Network) Award from the Republic of India (2016, 2017) Dr. Massopust has secured substantial research funding from various national and international sources, including the German Research Foundation (DFG), Bayerische Forschungsallianz, VolkswagenStiftung, and collaborations with Sandia National Laboratories and the National Science Foundation. His research program has consistently focused on advancing mathematical methods for signal and image processing, with particular emphasis on fractal-based approaches and wavelet theory. He has also been instrumental in fostering international collaborations, particularly through the EuroTech network and with institutions in Australia and India. Among his notable contributions is the GHM (Geronimo-Hardin-Massopust) Scaling Vector and DGHM (Donovan-Geronimo-Hardin-Massopust) Multiwavelet, developed at the Georgia Tech Research Institute in 1995. This work has had significant impact in the field of wavelet analysis and its applications.
Prof. Dr. Lisa Stinken-Rösner is a prominent physicist and educator at the Faculty of Physics, Bielefeld University . With extensive experience across Germany and international institutions like California Science Center and Leuphana University, she specializes in physics education and inclusive science teaching . Her work bridges digital media with experimental physics to enhance both student and teacher competencies. Current Professor for Physics and its Didactics at Bielefeld University (since 2023) Former Research Associate at Leuphana University (2018–2022) Key projects: LFB-Labs-digital (teacher training in digital labs), VidEX (video-based experiments) Lisa's research focuses on: Inclusive science education strategies for diverse learners Digital learning tools including interactive videos and virtual labs Experimental skill development through innovative pedagogies Physics identity formation in educational contexts Her 15 most recent publications (2023–2025) span topics from: Digital gamification in science classrooms Inclusive pedagogy frameworks Experimental video methodologies Physics identity development Teacher training in digital environments Contextual physics instruction She coordinates multiple modules including: 28-FD Subject Didactics 80-SU-MA Master's thesis 80-SU-BA Bachelor's thesis
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.
Ulrich Parlitz is an Adjunct Professor of Physics at Georg-August-University Göttingen and a Scientist leading the Biomedical Physics Group at the Max Planck Institute for Dynamics and Self-Organization. His research focuses on nonlinear dynamics, chaos theory, and biomedical applications, particularly in cardiac dynamics and excitable media. He has held visiting positions at institutions like UC San Diego and the Santa Fe Institute. Education: 1987 PhD in Physics, Georg-August-University Göttingen 1984 Diploma in Physics, Georg-August-University Göttingen Research Interests: Analysis of nonlinear systems (neurons, lasers, oscillators) Bifurcation and chaos phenomena Data-based modeling and synchronization control Wave dynamics in excitable media (e.g., cardiac arrhythmias) Fractal dimension estimation and reservoir computing Labs/Teams: Leads the Biomedical Physics Group at MPI-DS and contributes to the IMPRS Program in Physics of Biological and Complex Systems.
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.
