Marko Huhtanen is a Visiting Professor at the Department of Mathematics and Systems Analysis, Aalto University, within the School of Science. His academic career includes extensive contributions to numerical analysis, matrix theory, and operator theory. He holds a Doctor of Technology in Mathematics from Helsinki University of Technology (1997) and a Master of Science in Engineering (Mathematics) from the same institution (1993). Research Interests: Huhtanen’s work focuses on numerical linear algebra, matrix factorizations, iterative methods, and operator theory. He explores topics such as eigenvalue problems, matrix decompositions, and applications in computational geometry and differential equations. His research often intersects with functional analysis and geometric aspects of linear algebra. Publications: His recent work includes studies on gradients of quotients in eigenvalue problems (2025), factoring matrices using circulant and diagonal structures (2015), and numerical solutions to R-linear Beltrami equations (2012). These contributions highlight his expertise in theoretical and computational mathematics. Collaborations: He has collaborated on international projects, such as the Workshop on Operator Theory and Applications in Poland (2010) and hosted academic visitors at Aalto University. His research also involves advancements in iterative methods and their applications in engineering and physics.
Lauri Nyman is a Research Fellow at the Department of Mathematics and Systems Analysis, Aalto University, affiliated with the School of Science. His primary role involves advanced research in mathematics and systems analysis. He is based at the Otakaari 1 campus in Espoo, Finland, in Room M329. His research interests span multiple areas including numerical analysis, differential geometry, mathematical physics, algebra, and operations research. He contributes to projects involving Riemannian optimization, matrix theory, perturbation analysis, and computational methods for solving complex mathematical systems. Recent publications focus on topics such as Riemannian optimization methods for matrix stability, eigenstructure perturbation theory, and algorithms for solving polynomial systems. His work bridges theoretical mathematics with practical applications in engineering and data science. No scientific awards or specific grants are explicitly listed in the provided information. He is actively involved in academic activities like organizing conferences (e.g., NNPSM2016, HAPDE2015) and participates in research groups such as Nonlinear PDEs and Time-Frequency Analysis. His contact details include an email address and phone number for professional inquiries.
Professor Atul Bhaskar is a faculty member in the Computational Engineering and Design Group within the Faculty of Engineering and the Environment at the University of Southampton. He holds a PhD in Mechanics from Cambridge University and has taught Aeronautics & Astronautics at the university. Education: Bachelor of Mechanical Engineering, IIT Kanpur (1985) Masters in Applied Mechanics, IIT New Delhi (1989) PhD in Mechanics, Cambridge University (1992) Research Focus: Professor Bhaskar's work spans Dynamics and Vibrations (nonlinear mechanics, damping, wave propagation), Numerical Analysis (finite elements, collocation methods), Design Optimisation (sensitivity analysis, evolutionary structural optimisation), and Additive Manufacturing (structure-property relationships for biomedical and aerospace applications). He also contributes to Applied Mathematics through eigenvalue problems and biophysical dynamics. Scientific Awards: Leverhulme Senior Research Fellowship George Stephenson prize and medal (IMechE) Churchill College Fellowship Senior Rouse Ball Studentship FP7 EC-funded grants (ReBioStent, PIPER, ACTIVE) H2020 grants (HyMedPoly, InDESTrust) Grants & Collaborations: He has secured major research grants from EPSRC, Rolls-Royce, EU FP7/H2020, and Knowledge Transfer Partnerships. His projects include structural dynamics of cellular solids, multifidelity optimisation in aerospace, and additive manufacturing for biomedical applications.
Ivan Damnjanovic is an Assistant Professor at the Department of Mathematics, Faculty of Electronics, University of Niš, where he also completed his academic education. He earned his doctorate in 2024 in the field of Applied Mathematics under the Electrical Engineering and Computer Science program, with prior master's and bachelor's degrees from the same institution. His research is centered on graph theory and combinatorics , particularly focusing on transmission irregular graphs, nut graphs, extremal graph indices (such as Sombor and Mostar), and spectral properties of trees and dendrimers. His work bridges theoretical mathematics with applications in mathematical chemistry and computational methods. The most recent articles show a strong trend in structural graph analysis , inverse problems in topological indices , and classification of special graph families . His publications frequently appear in high-impact journals in applied and computational mathematics, reflecting a consistent and impactful research trajectory. Scientific Awards: No scientific awards mentioned in the text. Advising and Grants: Number of students advised: Not specified. Current research projects: 1 national project; no international projects listed. Labs and Research Teams: While no specific lab or team name is mentioned, his collaborations with researchers such as Dragan Stevanović, Tomaž Pisanski, and Nino Bašić suggest active participation in a strong combinatorics and graph theory research group at the Faculty of Electronics.
Volkan Öger is a Researcher at Dokuz Eylül University, affiliated with the Faculty of Science and the Department of Mathematics . He earned his PhD in Mathematics (2016) and Master's degree (2003) from Dokuz Eylül University. His research centers on numerical analysis, differential equations, and computational mathematics. Education: PhD (2016), Master's (2003), and Bachelor's (2001) in Mathematics from Dokuz Eylül University. Research Interests: Numerical solutions for differential equations, eigenvalue problems, and reliability engineering in coherent systems. His publications include numerical methods for Lane-Emden equations , Sturm-Liouville problems , and reliability analysis of dependent components. These works span applied mathematics, computational modeling, and probability theory.
Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.
Christian B. Mendl is an Assistant Professor (Rudolf Mößbauer Tenure Track) at Technische Universität München's Department of Computer Science. His research focuses on quantum computing, tensor network methods, computational physics/chemistry, high-performance computing, and theoretical condensed matter physics. He holds a PhD in Physics from LMU München and dual diplomas in Physics and Mathematics from TU München, with postdoctoral experience at Stanford University and TU Dresden. His work bridges quantum algorithms, numerical methods, and interdisciplinary applications. Notable contributions include Riemannian quantum circuit optimization, tensor network simulations, and quantum-classical computing frameworks. He collaborates with institutions globally and actively develops open-source tools like PyTreeNet for tensor networks. Recent research emphasizes scalable quantum algorithms, optimization techniques, and hybrid quantum-classical systems. His work addresses challenges in quantum computing, many-body systems, and high-performance simulation frameworks.
Mahdi Moeini is an Associate Professor in Operations Research and Machine Learning at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise), affiliated with the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. His research focuses on optimization techniques, including DC programming, combinatorial optimization, and their applications in logistics, healthcare, and finance. Academic Roles: Associate Professor (2022–present), Adjunct Lecturer (2014–2022) at TU Kaiserslautern, Germany. Research Expertise: Portfolio optimization, vehicle routing with drones, emergency medical systems, and metaheuristics. Key Contributions: Published 14 journal papers, 24 conference chapters, and 8 technical reports, with a Habilitation in 2018. Research Interests: Combinatorial optimization, mathematical programming, sustainability in digitization, and machine learning applications. Teaching: Courses include Operations Research, Data Science, Computational Intelligence, and Financial Data Analysis.
Petr Tichý is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University. He specializes in numerical analysis, matrix computations, and Krylov subspace methods. His work focuses on error estimation in iterative methods and the convergence properties of algorithms like the conjugate gradient method. Education: Ph.D. in Scientific Computations, Charles University (2002) M.S. in Computational Mathematics, Charles University (1997) Research Interests: His research emphasizes numerical linear algebra, including matrix approximations, CG algorithm stability, and the development of efficient error estimation techniques. He has contributed to the analysis of block conjugate gradient methods, Gauss-Radau error bounds, and the theoretical foundations of Krylov subspace methods. Publications: Tichý has authored over 30 peer-reviewed articles and a book on error norm estimation in conjugate gradient algorithms. His recent work includes studies on matrix best approximation and the behavior of error estimates in iterative solvers. Awards: Dean's Award for Best Book (2024) Jaroslav Jirsa Prize (2013) Otto Wichterle Prize (2007) Ivo Babuška Prize (2002) Grants & Collaborations: He has led projects funded by GACR and participated in EU initiatives. Collaborators include Gerard Meurant, Vance Faber, and Jörg Liesen.
Professor Lev Kantorovich is a distinguished academic at King’s College London’s Department of Physics, affiliated with the Thomas Young Centre and the Centre for Non-Equilibrium Science (CNES). He holds a Professorship since 2009, progressing from a Lecturer role (2002) and Reader (2005). His academic journey includes a PhD from Latvian University (1975), followed by postdoctoral work at Keele University and University College London. His research focuses on theoretical simulations of solid surfaces, atomic-scale imaging via AFM/STM, and self-assembly of molecular systems. Key areas include non-equilibrium statistical mechanics, quantum transport in nanojunctions, graphene growth kinetics, and embedded-cluster methods for electronic structure calculations. He develops advanced computational techniques like non-equilibrium Green’s functions (NEGF) and kinetic Monte Carlo simulations. Recent work explores photon-assisted phenomena in nanojunctions, photoinduced surface reconstructions, and catalytic applications of molybdenum subnanoclusters. His mathematical contributions span transforms (Laplace, Fourier), linear algebra, and partial differential equations, underpinning interdisciplinary modeling. Affiliated with CNES, he collaborates globally on non-equilibrium science. His research addresses energy, materials, and nanotechnology challenges, with applications in molecular electronics and surface engineering.
Prof. Thomas Blesgen is a Professor of Mathematics and Digital Technology at Technische Hochschule Bingen, Department 2. He holds a habilitation and PhD in Mathematics from the University of Bonn and University of Leipzig respectively. His career includes research stays at the University of Cambridge, California Institute of Technology, and Max Planck Institute for Mathematics in the Sciences. Research focuses on nonlinear mechanics, electronic structure calculations, multiscale modeling, and materials science. He has pioneered work on Cosserat plasticity, two-phase flow models, and continuum limits of nanomaterials. His methods integrate partial differential equations, calculus of variations, and geometric measure theory. Notable projects include DFG-funded dynamic recrystallization studies and NSF/AFOSR-sponsored electronic structure computations. Recent work addresses microstructure formation in nonlinear elasticity. He teaches applied mathematics across bachelor's and master's programs.
Dirk Blömker is a Professor in the Institute of Mathematics at the University of Augsburg (since 2006). His research focuses on Stochastic Partial Differential Equations (SPDEs) with applications to surface growth models , phase separation (Cahn-Hilliard, Allen-Cahn), stochastic bifurcation , and multiscale analysis (amplitude/modulation equations, fast diffusion). He also works on filter theory (Ensemble Kalman) and numerics of SPDEs . Habilitation : RWTH Aachen (2006) Doctorate : University of Augsburg (2000) Diploma : University of Münster (1997) Intermediate Diploma : University of Münster (1993) Abitur : Gymnasium Lengerich (1989) His work spans rigorous mathematical analysis of SPDEs (existence/uniqueness, blow-up, regularity) and numerical methods (convergence analysis, a-posteriori bounds). Recent articles examine fractional noise, Lévy processes, and ensemble Kalman inversion. Collaborators include Luigi Bianchi, Claudia Schillings, and Philipp Wacker. He has held academic positions at RWTH Aachen (Assistant/Acting Lecturer), University of Bonn (Acting Lecturer), and University of Warwick (DFG Research Fellowship).
Professor Alexander Molev is a distinguished academic affiliated with the Faculty of Science at the University of Sydney . His research focuses on Classical Lie algebras and their representations Vertex algebras Quantum groups Algebraic combinatorics and aligns with the Faculty’s research strengths in Understanding the Universe, Fundamental Laws of Nature, and Complex Systems. Research Trends : His recent work explores super-Yangians, quantum Sugawara operators, and W-algebras for classical and superalgebras. He investigates algebraic structures related to Lie superalgebras, quantum integrability, and symmetry algebras in mathematical physics. Scientific Awards : He has received 2001 : Medal of the Australian Mathematical Society 2019 : Fellow of the Australian Academy of Science Grants : His research has been supported by multiple Australian Research Council (ARC) Discovery Projects, including studies on quantum vertex algebras (2017), classical W-algebras (2014), and affine Lie algebras at the critical level (2013). Education : He earned a PhD from Moscow State University in 1986.
Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.
Jingzhi Li is a Professor and Associate Chair of the Department of Mathematics at Southern University of Science and Technology (SUSTech), where he has been serving since January 2020. Previously, he was an Associate Professor at SUSTech from June 2012 to December 2019. His research focuses on scientific computing, finite element methods, inverse problems in mathematical physics, shape optimization in differential forms, and computational finance. Education: PhD in Applied and Computational Mathematics, Chinese University of Hong Kong, 2009 MS in Computer Science, Wuhan University, 2004 BS in Mathematics, Wuhan University, 2001 Professor Li's research spans multiple areas of computational mathematics with a particular emphasis on inverse problems and their applications. His work combines theoretical analysis with practical numerical methods to solve challenging problems in mathematical physics. He has made significant contributions to the development of globally convergent numerical methods for coefficient inverse problems, finite element methods for high-order PDEs, and optimization techniques using differential forms. His research has applications across various domains including electromagnetic scattering, wave propagation, and computational finance. Analysis of Professor Li's recent publications reveals a strong focus on inverse problems, particularly in the areas of coefficient identification, scattering theory, and phaseless data reconstruction. His work demonstrates consistent innovation in developing convexification methods for solving nonlinear inverse problems with guaranteed global convergence. The research spans multiple mathematical disciplines including partial differential equations, numerical analysis, and optimization theory, with applications in physics, engineering, and medical imaging. Scientific Awards: Career Award, Shenzhen, 2021 Excellent Mentor Award, Shude Residential College, SUSTech, 2020 Dual-excellence Award, Faculty of Science, SUSTech, 2020 Highlight Award, Faculty of Science, SUSTech, 2019 Excellent Research Award, SUSTech, 2016 Excellent Mentor Award, SUSTech, 2016 Peacock Award (Tier B), Shenzhen, 2013 Best Doctoral Dissertation Award, Mathematical Society of Hong Kong, 2011 Shenzhen Excellent Talent Project (Outstanding Young Scientists Project), 2021 National Key Talent Program Youth Project, Mathematics and Science, 2012 Professor Li has demonstrated strong commitment to student mentorship, evidenced by multiple Excellent Mentor Awards from SUSTech. His research has been supported by significant grants including the National Key Talent Program Youth Project and Shenzhen's Peacock Plan. His collaborative work spans multiple institutions, particularly with ETH Zurich and Chinese Academy of Sciences, reflecting a strong international research network. While specific grant details aren't provided in the text, his extensive publication record in top journals suggests substantial research funding. Professor Li's research activities are centered around computational mathematics with particular strength in inverse problems. His work connects theoretical mathematics with practical applications across physics and engineering domains. The consistent publication record in high-impact journals demonstrates an active and productive research program with significant contributions to the field of computational inverse problems.
