Ryan Schneider is an NSF Postdoctoral Fellow in the Department of Mathematics at the University of California, Berkeley. His research focuses on numerical analysis, randomized numerical linear algebra, and scientific computing, with applications to quantum mechanics and computational physics. Mentor: James Demmel (UC Berkeley) Former Advisor: Ioana Dumitriu (UC San Diego) Collaborator: Barry I. Schneider (NIST) Ryan's work centers on developing efficient numerical methods for solving differential equations, particularly the time-dependent Schrödinger equation. His research combines algorithm design with practical implementations in Fortran, emphasizing inverse-free and communication-efficient approaches. Recent publications highlight advancements in: Jacobi's method optimization for eigenvalue problems Structured divide-and-conquer algorithms Volterra integral equation solvers (ITVOLT) Deflating subspace computations Quantum system simulations Scientific awards include: NSF Postdoctoral Fellowship Measurement Science and Engineering Fellowship at NIST Contact: ryan.schneider@berkeley.edu | ryschnei@ucsd.edu
Akash Singha Roy is a Lecturer in the Department of Mathematics at the University of Georgia (UGA), where he completed his Ph.D. under Paul Pollack in July 2025. His research focuses on elementary, analytic, and combinatorial number theory, particularly the residue-class distribution of arithmetic functions and mean values of multiplicative functions. He will join Charles University, Prague, as a postdoctoral fellow in October 2025. Ph.D. in Mathematics, University of Georgia (2025) BSc. Honors in Mathematics and Computer Science, Chennai Mathematical Institute (2021) His work spans advanced analytic techniques, including the Siegel-Walfisz and Landau-Selberg-Delange methods, with applications to Benford's law, prime factor distributions, and algebraic structures like module theory and arithmetic geometry. He has coauthored publications with Vorrapan Chandee, Xiannan Li, Nathan McNew, and Paul Pollack. His recent publications extend classical analytic number theory methods to broader contexts, such as Dirichlet L-functions and hybrid families of additive/multiplicative functions. Key themes include residue-class equidistribution, statistical properties of arithmetic functions, and connections to Fourier coefficients of modular forms. Scientific Awards: William Armor Wills Memorial Scholarship Award (2024) UGA Graduate School Dean's Award (2023) Exemplary Counselor Award, Ross/Asia Mathematics Program (2019) At UGA, Singha Roy has taught Calculus I and Precalculus, contributed to curriculum design, and mentored students at the Ross Mathematics Program. He has refereed for journals like Monatshefte fur Mathematik and the Rose-Hulman Undergraduate Mathematics Journal.
Hon To Hardy Chan is a Research Fellow at the Department of Mathematics and Computer Science at the University of Basel, Switzerland, where he works in the Research Group Lenzmann. He currently holds an SNF Ambizione Fellowship, which is a prestigious independent research grant from the Swiss National Science Foundation. Dr. Chan earned his Ph.D. in 2018 from the University of British Columbia with a dissertation titled 'New solutions to local and non-local elliptic equations,' supervised by Juncheng Wei and Nassif Ghoussoub. Prior to this, he completed an M.Phil. in 2013 at the Chinese University of Hong Kong under Kai-Seng Chou, with a thesis on 'Convergence of bounded solutions for nonlinear parabolic equations.' His research focuses on nonlinear partial differential equations, particularly semilinear elliptic equations, nonlocal equations, phase transitions, the Yamabe problem, nonlocal minimal surfaces, construction of solutions, singular solutions, and free boundary problems. Dr. Chan has made significant contributions to the field including the flatness of stable free boundaries, classification of stable nonlocal minimal surfaces, theory of nonlocal ODEs, fractional elliptic gluing schemes, and new boundary singular phenomena. Analysis of his recent publications reveals a strong focus on fractional calculus and nonlocal operators, with particular attention to geometric problems, singular solutions, and boundary behavior. His work bridges pure mathematical analysis with applications in geometry and physics, demonstrating sophisticated techniques that combine classical ODE methods with modern nonlocal analysis. Among his notable achievements is the SNF Ambizione Fellowship, which supports his independent research program. His publications in top mathematics journals reflect the significance and impact of his work in the mathematical community. Dr. Chan teaches courses in advanced mathematics, including 'Elliptic PDEs: Theory and Applications' for the Spring semester of 2025. His research is supported by multiple prestigious fellowships including his current SNF Ambizione Fellowship, previous Severo Ochoa Postdoctoral Fellowship at ICMAT Madrid (2021-2022), and ERC Postdoctoral Fellowship at ETH Zurich (2018-2021). He is actively involved in the mathematical research community, collaborating with prominent mathematicians across Europe and contributing to the advancement of nonlocal partial differential equations and their applications.
Dr. Carolin Penke is a researcher at the Jülich Supercomputing Centre (JSC) of Forschungszentrum Jülich GmbH . She leads projects in training large language models (LLMs) on high-performance computing (HPC) systems as part of the OpenGPT-X initiative and the Accelerating Devices Lab . Current focus: HPC-AI integration , memory-efficient LLM training , and accelerator evaluation Prior work: numerical linear algebra and mathematical algorithm development for quantum chemistry Her research spans computational mathematics, artificial intelligence, and high-performance computing. Key contributions include: Developing CARAML for AI workload evaluation Creating JUPITER benchmark suite for exascale systems Optimizing low-rank representations in deep learning Advancing pseudosymmetric matrix algorithms for physics simulations Designing GPU-accelerated numerical methods for eigenvalue problems Her work bridges mathematical rigor with practical AI/HPC applications, emphasizing scalability , energy efficiency , and European sovereignty in AI .
Helena Margarida dos Santos Vasconcelos Gomes is an Assistant Professor at the School of Education of Viseu (Polytechnic Institute of Viseu), with a PhD in Mathematics (Spectral Graph Theory, 2018) and a Master's in Mathematics (Algebra and Systems Theory, 2006) from the University of Aveiro . She is a senior researcher at the Center for Research and Development in Mathematics and Applications (CIDMA) . Her work bridges Spectral Graph Theory and Mathematics Education , focusing on Early Childhood and Primary Education Algorithmic and Computational Thinking Humor in Mathematics Teaching Textbook Evaluation Teacher Training Programs Her research projects include EARLY (Robotics in Early Education), MindMaths (Flipped Learning for Math Anxiety), and Algolittle (Play-Based Algorithmic Skills). She has contributed to national and international studies on pedagogical content knowledge and interdisciplinary curriculum design. She supervises Master’s theses on topics like ICT in Geometry and teaching materials for Preschool Mathematics. Her recent publications analyze caterpillar graphs via H-join operations, Randic spectra, and humor integration in textbooks. Current affiliations : Assistant Professor, School of Education of Viseu (since 2018) Senior Researcher, CIDMA (since 2019) PhD Candidate, Artificial Intelligence and Intelligent Systems Engineering, Porto Higher Institute of Engineering (since 2022)
Christian Bär is a Professor of Mathematics at the University of Potsdam, where he maintains an active research program in differential geometry, spectral geometry, and mathematical physics. He serves as editor-in-chief of zbMATH Open and previously held the presidency of the German Mathematical Society (DMV) from 2011 to 2012. His research is supported through major collaborative projects including the IMPRS for Mathematical and Physical Aspects of Gravitation, Cosmology and Quantum Field Theory, the DFG Priority Programme 'Geometry at Infinity,' and the Berlin Mathematical School. Professor Bär's primary research interests focus on differential geometry with particular emphasis on spectral properties of Dirac operators, Lorentzian geometry, spin geometry, and scalar curvature problems. His work bridges pure mathematics and theoretical physics, exploring the deep connections between geometric structures and physical phenomena. His research has made significant contributions to understanding the relationship between Dirac operators and geometric invariants, particularly in the context of index theory and rigidity theorems. His recent publications demonstrate a consistent focus on boundary value problems for Dirac-type operators, scalar curvature rigidity, and Lorentzian geometry. The work shows increasing sophistication in handling non-compact boundaries and developing local versions of classical index theorems. His research group has made notable advances in connecting differential geometry with mathematical physics, particularly in the context of general relativity and quantum field theory on curved spacetimes. President of German Mathematical Society (2011-2012) Editor-in-chief of zbMATH Open Professor Bär is actively involved in multiple third-party funded research projects that form a significant part of his research program. The IMPRS for Mathematical and Physical Aspects of Gravitation, Cosmology and Quantum Field Theory provides a framework for interdisciplinary research, while the DFG Priority Programme 'Geometry at Infinity' supports fundamental research in geometric analysis. His collaboration with the Berlin Mathematical School indicates strong connections with graduate education and mentoring of young researchers.
Dr. Thomas Mach is a Postdoctoral Researcher at the Institute of Mathematics , University of Potsdam, Germany, focusing on Data Assimilation under Prof. Melina Freitag. His work bridges theoretical and applied numerical linear algebra. Research Interests Numerical linear algebra for large/structured matrices Krylov subspace methods and iterative eigenvalue solvers Adaptive cross approximation and regularization techniques Applications to inverse problems and parametric systems Publication Trends show expertise in matrix eigenvalue problems, regularization for ill-posed equations, and algorithm design for structured systems. Co-authored works with leading experts like David S. Watkins demonstrate methodological innovation. Awards : SIAM Outstanding Paper Prize (2015) for polynomial root-finding research Academic Service includes co-organizing workshops, serving as Associate Editor for numerical analysis journals, and active participation in international conferences like ILAS, SIAM, and GAMM.
Iveta Hnetynkova is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. She specializes in numerical linear algebra, inverse problems, and regularization methods, with applications in image processing and scientific computing. Education: Doctor of Natural Sciences (RNDr.) from Charles University (2003) Ph.D. in Scientific Computations from Charles University (2006) Habilitation thesis on error-contaminated linear approximation (2019) Research Interests: Krylov subspace methods Total Least Squares (TLS) formulations Noise revealing in discrete inverse problems Tensor generalizations and structured matrices Applications in image processing and jewelry defect analysis Scientific Awards: Visegrad Group Young Researcher Award (2014) J. Jirsa Prize for textbook excellence (2013) I. Babuska Prize (2nd place) (2007) SVOČ Prize in mathematics (2003)
David Hammond is a Professor of Mathematics at Oregon Institute of Technology's Portland-Metro campus, where he has held a faculty position since 2013. His academic trajectory includes Peace Corps service teaching mathematics in Malawi, doctoral studies at NYU's Courant Institute, and postdoctoral research at École Polytechnique Fédérale de Lausanne and the University of Oregon. His educational background features: B.S. in Mathematics/Chemistry from California Institute of Technology (1999) Ph.D. in Mathematics from New York University (2007) Dr. Hammond's research centers on signal and image processing with specialized expertise in wavelet analysis and graph signal processing . His theoretical work on spectral graph wavelets enables advanced data analysis on complex networks, while practical applications span biomedical imaging, microscopy enhancement, and computational neuroscience. This dual focus bridges abstract mathematical frameworks with tangible engineering solutions. Analysis of his recent publications reveals three dominant research streams: (1) foundational contributions to graph signal processing including the spectral graph wavelet transform, (2) biomedical applications in EEG source estimation and head tissue modeling, and (3) interdisciplinary policy work on sugary drink taxation and electricity market design. His 2018 paper on the spectral graph wavelet transform represents a cornerstone contribution with over 500 citations. No scientific awards were documented in the source material. As a full-time professor, Dr. Hammond maintains active research and teaching responsibilities. While specific advisees aren't listed, his publications indicate mentorship through collaborative research. His work has been supported by institutional affiliations rather than explicitly cited grants. Though no dedicated laboratory is described, his research methodology suggests utilization of computational facilities for signal processing tasks, with collaborations spanning biomedical engineering, astronomy, and public health domains.
Professor Nikolaos L. Tsitsas is a faculty member at the Department of Informatics, Aristotle University of Thessaloniki, Greece. Holding a PhD in Electrical and Computer Engineering from NTUA (2006), he specializes in analytical and numerical methods for wave scattering/propagation, integral equations, and optimization. His work bridges electromagnetics, photonics, and computational mathematics. Education: PhD (NTUA), MSc (NKUA), Diploma (NTUA) Research: Electromagnetic theory, metamaterials, numerical methods Memberships: AMS, IEEE, OSA, URSI Recent publications focus on photonic metagratings, electromagnetic cloaking, and hybrid computational methods. His team investigates dielectric waveguides, graphene shielding, and spherical wave interactions using advanced numerical techniques. Teaching responsibilities include undergraduate courses in Mathematical Analysis, Applied Mathematics, and Optimization. He has supervised graduate theses on numerical modeling and electromagnetic theory.
Marc Bonnet is a CNRS research director HDR at ENSTA Paris, working within the Applied Mathematics Unit (UMA) and associated with the Wave Propagation, Mathematical Study and Simulation (POEMS) research group. His academic career spans over two decades with extensive contributions to computational mechanics and mathematical modeling. Dr. Bonnet's research interests focus on wave propagation phenomena, mathematical modeling of physical systems, and advanced computational methods. His work particularly emphasizes boundary element methods, inverse problems, elasticity theory, acoustics, and topological sensitivity analysis. He has developed sophisticated mathematical frameworks for solving complex engineering problems related to structural mechanics and material characterization. His recent publications demonstrate a strong trend toward high-order numerical methods, particularly in boundary integral formulations and polynomial interpolation techniques. His research bridges theoretical mathematics with practical engineering applications, especially in nondestructive testing, viscoelastic material characterization, and microfluidic systems. The interdisciplinary nature of his work spans computational mathematics, solid mechanics, fluid dynamics, and electromagnetics. Throughout his career, Dr. Bonnet has maintained extensive collaborations with researchers across France and internationally, evident from his numerous co-authored publications across diverse application domains.
Daniel C. Jerison is an Assistant Professor in the Department of Mathematics and Statistics at the University of San Francisco, supporting undergraduate programs in mathematics and data science. His academic career demonstrates a strong trajectory through prestigious institutions including Stanford University, Harvard University, Cornell University, and Tel Aviv University. Education: PhD in Mathematics, Stanford University, 2016 BA in Mathematics, Harvard University, 2007 Dr. Jerison specializes in discrete probability theory with emphasis on the properties of random objects and convergence of Markov chain algorithms. His research explores random planar maps, abelian sandpiles, circle packing, and discrete complex analysis. His work bridges theoretical mathematics with applications in statistical physics, examining how complex systems evolve and reach equilibrium states through sophisticated probabilistic methods. His approach often combines combinatorial techniques with analytical methods to solve problems in discrete geometry and probability. Analysis of Dr. Jerison's publications reveals consistent focus on the intersection of probability theory and discrete structures. His research demonstrates sophisticated mathematical techniques for analyzing convergence rates in Markov chains, establishing geometric criteria for planar structures, and solving boundary value problems for discrete systems. The recurring themes across his work include probabilistic methods applied to geometric problems, the study of self-organized critical systems, and theoretical frameworks for understanding complex discrete phenomena. Dr. Jerison has extensive teaching experience across multiple institutions, teaching courses ranging from foundational calculus to advanced probability theory. He has taught Probability Theory I (Math 6710), Stochastic Processes (Math 4740), Linear Algebra for Engineers (Math 2940), Prove It! (Math 3040), and Applied Complex Analysis (Math 4220) at Cornell University, in addition to his current teaching responsibilities at USF. He has also been involved with prestigious summer programs for high school students including PROMYS and SUMaC, where he directed research labs and served on admission committees.
Russi Georgiev Yordanov is an Assistant Professor in the Department of Complex Analysis and Topology at the Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski". His office is located in room FzF-25, and he can be contacted via email at yordanov@fmi.uni-sofia.bg or by phone at +359 2 8161-546. His research primarily spans mathematical physics and applied materials science, with core interests including: Complex analysis and spectral properties of differential operators Integrable systems and soliton theory, particularly hierarchies of nonlinear equations Inverse scattering problems for Schrödinger-type operators Nonlinear wave phenomena and evolution equations Optical limiting properties of fullerenes and nanomaterials Yordanov's publication record demonstrates two distinct phases: foundational work (1984-1993) on mathematical physics (spectral theory, solitons, inverse scattering), and applied collaborations (1995-1998) characterizing optical limiting in fullerenes. His later interdisciplinary research involved experiments measuring nonlinear optical responses across visible and infrared spectra. No scientific awards, grants, student advisories, or lab affiliations are mentioned in the source material.
Vasilije Perovic is an Associate Professor in the Department of Mathematics and Applied Mathematical Sciences at the University of Rhode Island's College of Arts and Sciences. He joined the university as an Assistant Professor in 2015 and was promoted to Associate Professor in 2023. Dr. Perovic's primary research focuses on matrix theory and numerical linear algebra, with special emphasis on nonlinear eigenvalue problems. His work extends to applications of linear algebra in data science and the development of scientific algorithms for shared and distributed memory systems. His research bridges theoretical mathematics with practical computational applications, particularly in the areas of matrix polynomials, singular value decomposition, and parallel computing. His publication record demonstrates a consistent focus on numerical methods for matrix computations, with recent work centered on hybrid algorithms for singular value decomposition and properties of matrix polynomials. His research shows a progression from theoretical aspects of matrix polynomials to practical computational algorithms with applications in data science. Dr. Perovic has collaborated extensively with James Baglama and other researchers at the University of Rhode Island. His work on software development includes MATLAB implementations of advanced singular value decomposition algorithms that are publicly available. He earned his Ph.D. in Mathematics from Western Michigan University in 2015, with a dissertation titled 'Spectrally Equivalent Matrix Polynomials: Non-standard Representations and Preservation of Structure,' advised by Professor D. Steven Mackey. He also completed his B.Sc. in Applied and Computational Mathematics at Western Michigan University, graduating Summa cum laude and as a member of Phi Beta Kappa and Pi Mu Epsilon honor societies.
Wolfgang Steiner is a Senior Lecturer at the University of Applied Sciences Upper Austria (FH Wels), affiliated with the Center of Excellence Automotive/Mobility. His research focuses on multibody dynamics, finite element methods, optimization, and stability theory in mechanical systems. Primary Affiliation: University of Applied Sciences Upper Austria (FH Wels), Center of Excellence Automotive/Mobility External Position: Lecturer at TU Vienna (since 1997) His work spans theoretical mechanics and applied engineering, particularly in satellite dynamics and industrial robotics. Recent projects include KBMKSZ (2024-2026) and IOMMS (2023-2026), emphasizing advanced computational techniques for multibody systems. Research trends highlight the integration of adjoint methods for gradient optimization, application of multibody dynamics to biomedical problems (e.g., tumor drug dosage), and development of novel simulation frameworks for industrial gear transmissions. His publications demonstrate cross-disciplinary relevance in mechanics, aerospace, and computational modeling. Scientific Recognition : 2020: Best Paper Award He actively supervises research projects and participates in academic activities such as international conferences and committee memberships.
