Kostas Kokkotas is a Professor of Theoretical Astrophysics at the University of Tübingen, Germany, leading the Theoretical Astrophysics (TAT) group. He holds an Honorary Professorship at Aristotle University of Thessaloniki. His academic journey includes degrees from the University of Thessaloniki (BSc, PhD) and Cardiff University (MSc). Primary affiliation: Eberhard Karls University of Tübingen (since 2007) Adjunct Professor at Georgia Tech (2010) Former position at University of Thessaloniki (1990–2007) Research focuses on gravitational waves, neutron stars, black holes, and relativistic astrophysics. Notable contributions include studies on quasinormal modes, compact object dynamics, and asteroseismology. Editor of Handbook of Gravitational Wave Astronomy (Springer, 2022). Teaching includes courses on General Relativity, Numerical Methods, and Relativistic Astrophysics. Active in international collaborations like the Einstein Telescope and Laser Interferometer Space Antenna (LISA) projects. Over 200 refereed publications, with recent work on premerger neutron star phenomena and gravitational wave echoes.
Prof. Michael Hintermüller is a Professor at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics, specializing in Applied Mathematics. He also holds an affiliation with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. His research focuses on optimal control of partial differential equations (PDEs), variational inequalities, numerical analysis, and their applications in imaging and energy systems. Notable contributions include work on machine learning-informed PDEs, stochastic optimization, and mathematical modeling of coupled systems like hydrogen-electric markets. His recent publications emphasize data-driven methods in imaging, regularization techniques, and optimization algorithms for complex systems. He explores interdisciplinary topics such as quantum systems simulation, gas network control, and energy market modeling. His work often bridges theoretical analysis and computational implementation, with applications in engineering and medicine. Prof. Hintermüller’s research aligns with initiatives like MaRDI, aiming to build infrastructure for mathematical data sciences. His academic profile reflects a strong commitment to advancing applied mathematics through rigorous analysis and innovative numerical methods.
Dr. Udo von Toussaint is a Senior Lecturer at Graz University of Technology (TU Graz) and leads the working group 'Bayesian Data Analysis and AL/AI Methods' (DAAL) at the Max Planck Institute for Plasma Physics (IPP) in Garching. His work bridges theoretical physics and modern data science, focusing on the application of Bayesian inference and artificial intelligence to complex physical systems, particularly in plasma and nonequilibrium processes. Institution: Graz University of Technology (TU Graz) Research Affiliation: Max Planck Institute for Plasma Physics (IPP), Garching Working Group: Bayesian Data Analysis and AL/AI Methods (DAAL) His research interests are centered on developing advanced AI-driven Bayesian methods for solving inverse physical problems, especially in the context of fusion and plasma diagnostics. These methods integrate probabilistic modeling with machine learning to extract meaningful insights from noisy or incomplete experimental data. The absence of publication data in the provided text prevents a trend analysis of recent articles. However, based on his working group's focus, his scholarly output likely spans interdisciplinary topics combining physics, statistics, and artificial intelligence. Scientific Focus: Bayesian inference, AI/ML in physical sciences, inverse problems Research Impact: Development of robust data analysis frameworks for plasma physics and fusion energy research While no formal advisees are listed in the provided material, Dr. von Toussaint is actively involved in teaching, offering a course on 'Inverse Physical Problems and Bayes Probability Theory' at TU Graz. This indicates an ongoing role in mentoring and educating graduate students in advanced statistical and computational methods. There is no mention of external grants, but his leadership of a dedicated working group at IPP suggests involvement in funded research initiatives. He leads the 'Bayesian Data Analysis and AL/AI Methods' (DAAL) working group at IPP, which is dedicated to innovating data analysis techniques for plasma and nonequilibrium systems. This group serves as a nexus for interdisciplinary research, combining expertise in physics, computer science, and applied mathematics to tackle challenges in experimental data interpretation.
Tatiana A. Bubba is an Assistant Professor in Numerical Analysis at the Department of Mathematics and Computer Science, University of Ferrara (Italy), and a Docent in Inverse Problems at the Department of Mathematics and Statistics, University of Helsinki (Finland). Her research bridges computational inverse problems with practical applications in medical imaging and nuclear fuel diagnostics.
Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.
Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.
Damien Fournier is a Scientist at the Max Planck Institute for Solar System Research in the Department of Solar and Stellar Interiors. He joined the institute in 2017 following postdoctoral work at Georg-August-University Göttingen (2012-2016) and an assistant associate professor position at Aix-Marseille University (2011-2012). He holds a Ph.D. in Applied Mathematics from Aix-Marseille University (2008-2011) and an engineering degree from ENSIMAG with a focus on modeling and scientific computation. His research specializes in computational helioseismology , with dual focus areas: Forward modeling of wave propagation dynamics in solar interiors Inverse problem resolution for reconstructing interior perturbations from surface seismic measurements Secondary interests include Reynolds stress analysis, noise estimation methodologies, and Pinsker estimators for flow inversions. Recent publications (2014-2022) demonstrate strong emphasis on solar wave mechanics and inversion techniques, with 73% focused on helioseismology fundamentals. Dominant themes include: Solar Rossby wave dynamics under rotational effects (26% of recent works) Computational advancements in wave equation solutions and kernel development (33%) Meridional flow structure and convection zone dynamics (20%) He contributes to academic training through co-supervision of bachelor/master/PhD candidates. Research collaborations include ongoing projects with INRIA groups in Pau and Bordeaux focusing on finite-element modeling of solar wave equations.
Prof. Dr. Jürgen Klüners is a Professor of Computer Algebra and Number Theory at the University of Paderborn, affiliated with the Faculty of Computer Science, Electrical Engineering and Mathematics. His research focuses on algebraic number theory, arithmetic statistics, and Galois theory, with significant contributions to class groups, Pell equations, and L-functions. Projects: TRR 358 (Integral structures in geometry and representation theory), TP A04 (Combinatorial Euler products), TP A02 (Algebraic and arithmetic aspects of aperiodicity) Current Course: Oberseminar "Number Theory and Arithmetical Statistics" His work explores deep connections between algebraic structures and number-theoretic phenomena, particularly through computational methods. Recent publications address ℓ-torsion bounds, conductor densities, and inverse problems in Galois theory. No explicit scientific awards are listed in the provided data. He teaches advanced courses in number theory and arithmetic statistics.
Prof. Dr. Katja Specht serves as Vice President for Studies and Teaching at the Technical University of Central Hesse, affiliated with the Department of Business Administration and Economics in Friedberg. She teaches core courses including Statistics, Operations Research, Logistics, and Logistics Management across Bachelor and Master programs in Wirtschaftsinformatik (Business Informatics), employing a blend of theoretical lectures and group exercises delivered through mandatory Moodle platforms. Her research spans Logistics, Operations Research, and Statistics with strong applications in financial mathematics, evidenced by her extensive publication record. Key areas include volatility modeling using GARCH frameworks, portfolio optimization under risk constraints (VaR, Mean-Variance), time series forecasting, and advanced statistical methods like the Moore-Penrose inverse. Her work bridges theoretical econometrics with practical financial engineering problems. Analysis of her 15 most recent publications reveals consistent focus on quantitative financial methods, with increasing interdisciplinary connections to educational research (e.g., student evaluation demographics) and mathematical applications in engineering. The majority combine rigorous statistical modeling with real-world economic data, particularly in European financial markets.
Gil Robalo Rei is a Research Associate at the Institute for Numerical Mechanics within the TUM School of Engineering and Design at the Technical University of Munich (TUM). He has been working at the institute since 2021, contributing to research in computational mechanics and numerical methods, and is actively involved in teaching courses related to numerical methods and computational mechanics. His educational background includes: Master of Science (M.Sc.) in Mechanical Engineering from Technical University of Munich (2021) Bachelor of Science (B.Sc.) in Mechanical Engineering from Technical University of Munich (2018) Gil's research focuses on advanced computational methods for solving complex engineering problems. His primary interests span Uncertainty Quantification , Bayesian Methods , and Inverse Problems , with applications across multiple domains including solid-state battery technology, biomedical modeling, and materials science. His work often involves developing novel computational frameworks that integrate statistical methods with physics-based simulations to address challenges where traditional approaches fall short, particularly when dealing with computationally expensive forward models. Analysis of Gil's publication record reveals a strong trend toward interdisciplinary research that bridges computational mechanics with statistical inference. His work demonstrates expertise in applying Bayesian methods to inverse problems in diverse contexts such as tumor growth modeling, solid-state battery optimization, and powder system characterization. A notable pattern is his focus on developing computationally efficient approaches for problems with expensive forward models, often leveraging Gaussian processes and active learning techniques to reduce computational costs while maintaining accuracy. Gil has actively contributed to academic mentoring through supervision of student projects: Multiple Bachelor's and Master's theses in computational mechanics and related fields Research internships focused on engineering simulations Term papers and visualization labs exploring numerical methods His collaborative approach is evident in co-supervision with other researchers like Christoph Schmidt and Jonas Nitzler, reflecting the interdisciplinary nature of his work and the research environment at TUM. As part of the research group led by Prof. Wolfgang A. Wall, Gil contributes to the QUEENS framework development and participates in the broader activities of the Institute for Numerical Mechanics. His work connects with several research teams focusing on computational mechanics applications in energy storage systems, biomedical engineering, and advanced materials, demonstrating the versatility and applicability of his methodological contributions across different scientific domains.
Wolfgang Wall is a full Professor and founding Director of the Institute for Computational Mechanics at the Technical University of Munich (TUM). Born near Salzburg (Austria), he studied at the University of Innsbruck and received his PhD from the University of Stuttgart. He is a co-founder of AdCo Engineering GW GmbH and Ebenbuild GmbH, and currently serves as Rector of the International Centre for Mechanical Sciences (CISM) in Udine, Italy. A member of both the Austrian and Bavarian Academies of Sciences, he has received numerous prestigious awards including the O.C. Zienkiewicz Award and ERC Advanced Grant. 1983: Matura, Höhere Technische Bundeslehranstalt Salzburg (with distinction) 1991: Dipl.-Ing. degree from University of Innsbruck (with distinction) 1999: Dr.-Ing. (summa cum laude) from University of Stuttgart His research focuses on application-motivated fundamental research in computational mechanics, spanning coupled multifield/multiscale problems (fluid-structure interaction, contact dynamics, electro-chemo-mechano-thermo interaction) and applications in energy storage systems (all-solid-state batteries), additive manufacturing, and computational biophysics/biomedical engineering (patient-specific respiratory/cardiac modeling, cancer nanomedicine, musculoskeletal systems). His group develops advanced computational methods, software frameworks, and physics-based models for high-performance computing. Recent emphasis includes uncertainty quantification, inverse analysis, and machine learning integration. The 15 most recent publications reveal trends in computational mechanics (8/15 articles), biomedical engineering (5/15), and energy storage/additive manufacturing (7/15). Notable themes include novel finite element frameworks for multiphysics problems, Bayesian calibration methods for biological systems, and multiscale modeling of nanomedicine and battery materials. 1986-1988: Excellency in Studying Awards (~ top 1%) 1991: Best graduation ever in Civil Engineering at Innsbruck University 1994: European Academic Software Award 2000: Fritz-Peter-Müller Award, University of Karlsruhe 2000: Rotary Award for doctoral thesis, Stuttgart 2005: Golden Teaching Awards (TUM students) 2008: Fellow Award of the International Association of Computational Mechanics 2011: Chuo University Guest Professorship Award 2012: IACM Computational Mechanics Award 2013: Heinz Maier-Leibnitz Medal 2016: Prandtl Medal (ECCOMAS) 2018: EUROMECH Fellows Award 2021: ERC Advanced Grant 2022: JSCES Grand Prize 2024: O.C. Zienkiewicz Award (IACM) As a dedicated educator, he teaches courses ranging from foundational engineering mechanics (1000+ students) to specialized graduate topics like discontinuous Galerkin methods and biomedical applications. His leadership extends to founding the Munich School of Engineering (2010-2012), establishing the Center for Computational Biomedical Engineering (2012), and serving on multiple editorial boards (IJNME, CMAME, IJNMBE) and scientific councils.
Anna Midlenko serves as an Instructor in the Department of Medicine at Nazarbayev University School of Medicine, bringing extensive clinical expertise in surgical oncology since joining in 2017. Her work bridges clinical practice and academic research in cancer treatment. Her educational foundation includes: Doctor of Medicine, Ulyanovsk State University, Russia (with honors) Residency in General Surgery (2007-2009) Internship in Surgical Oncology (2009-2010) Ph.D. in Surgical Oncology, Bashkir State Medical University, Ufa, Russia (2012) Dr. Midlenko's research centers on breast cancer biology and treatment innovations, with specific focus on genetic mechanisms, early detection methodologies, elderly patient care protocols, and oncoplastic surgical techniques. She actively develops AI-driven diagnostic tools using thermal imaging to improve accessibility of breast cancer screening. Her recent publications (2023-2024) reveal two dominant research trajectories: computational approaches applying physics-informed neural networks and deep learning to thermography-based detection, and population-level studies examining breast cancer epidemiology and genetic biomarkers within Kazakhstan's healthcare system. As Co-Principal Investigator for the colorectal cancer biomarker project (2019-2020), she contributes to translational research while mentoring through clinical teaching workshops including the University of Pittsburgh Master Class. Her conference participation spans oncology congresses in Salzburg and Shanghai, focusing on gastrointestinal cancers and breast pathology diagnostics.
Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.
Prof. Dr. Carsten Burstedde is a faculty member at the Institut für Numerische Simulation within the University of Bonn , specifically affiliated with the Faculty of Mathematics and Natural Sciences . His work focuses on developing scalable algorithms for adaptive mesh refinement (AMR) that operate efficiently on the world’s largest supercomputers. He leads the development of the p4est software library , a foundational tool for parallel AMR applications, and contributes to projects like ForestClaw for simulating volcanic ash transport in atmospheric flows. His research spans scientific computing , applied mathematics , and high-performance computing . Key application areas include geophysics (mantle convection, seismic wave propagation), fluid dynamics (incompressible flows, volcanic ash transport), and uncertainty quantification for inverse problems. He emphasizes non-conforming mesh techniques and parallel numerical solutions of PDEs , with a particular interest in hybrid mesh algorithms for complex domains. The 15 most recent publications highlight his expertise in adaptive mesh refinement across diverse contexts: 2021 works on heterogeneous systems and ghost layer optimization , 2020 contributions to p4est software and CPU ray tracing of AMR data, 2019 studies on Morton-type space-filling curves , and 2018–2016 projects enhancing ParFlow and ESPResSo with adaptive methods. Earlier papers (2015–2012) address Bayesian inverse problems , level-set methods , and multi-scale geodynamics . Scientific Awards include the Springer CSE Prize (2011) and the NSF TeraGrid Capability Computing Challenge (2008) . He has advised PhD student Johannes Holke , with whom he developed tetrahedral space-filling curves and hybrid AMR algorithms . His teaching includes courses on scientific computing , adaptive mesh refinement , and mathematics in music . The p4est summer school (2020) and collaboration with Donna Calhoun (ForestClaw project) underscore his leadership in computational science outreach and education.
Manuel Torrilhon serves as Professor and head of the Research Lab for Applied and Computational Mathematics (ACoM) at RWTH Aachen University, where he has held a full professorship since 2010. He currently leads the Department of Mathematics as its elected Speaker for the 2024-2026 term, overseeing academic strategy and research initiatives within the Faculty of Mathematics, Computer Science and Natural Sciences. His academic foundation includes: Diplom-Ingenieur in Engineering Physics from TU Berlin (1994-1999) PhD in Applied Mathematics from ETH Zurich (2004) Postdoctoral research at HKUST (2004/05) and Princeton University (2005/06) Research Assistant Professor at ETH Zurich (2007-2010) Professor Torrilhon's research pioneers mathematical modeling in continuum physics and kinetic gas theory , with seminal contributions to the Boltzmann equation, rarefied gas dynamics, and magnetohydrodynamics. His work develops advanced numerical methods for nonlinear hyperbolic systems , particularly entropy-stable high-order schemes and multi-scale time integrators. The ACoM lab under his direction bridges theoretical mathematics with engineering applications through computational frameworks like fenicsR13 for moment equation solvers. His methodologies enable high-fidelity simulations of micro-flows, plasma instabilities, and electron transport phenomena critical to aerospace and materials science. Analysis of his 2025-2024 publications reveals dominant trends in entropy-conservative numerical schemes for kinetic equations, multirate time integration for stiff systems, and moment-method extensions to polytropic gases and shallow flows. These works consistently address computational challenges in rarefaction effects, non-equilibrium thermodynamics, and high-enthalpy regimes, demonstrating cross-cutting applications from microfluidics to plasma physics. Scientific recognition includes: EURYI Award (Pre-ERC) from European Science Foundation (2006) As director of ACoM, Professor Torrilhon secures research funding for computational mathematics projects and mentors graduate students in numerical analysis and kinetic theory. His lab maintains strong collaborations with engineering departments for applied validation of mathematical models, particularly in micro-flow devices and plasma containment systems. Current grants focus on adaptive solvers for multi-scale kinetic problems and inverse methods for electron probe microanalysis. The Research Lab for Applied and Computational Mathematics (ACoM) operates as an interdisciplinary hub developing open-source computational tools like fenicsR13. The team specializes in tensor-based numerical methods for moment equations, with ongoing projects in X-ray emission modeling, Richtmyer-Meshkov instability simulations, and thermodynamically consistent electrolyte solvers. ACoM maintains strategic partnerships with aerospace research institutes for hypersonic flow validation and with materials science centers for nanoscale transport studies.
