Ilia Gaiur serves as a Research Fellow at the University of Geneva within Anton Alekseev's research group, focusing on advanced mathematical connections between geometry and differential equations. His work specifically targets the derivation of geometrical structures from the De-Rham framework of the Riemann-Hilbert correspondence, a fundamental relationship linking differential equations to monodromy representations. His research portfolio prominently features Geometry and Differential Equations as core disciplines, with specialized investigations into Riemann-Hilbert Correspondence structures, Integrability systems, and Poisson Geometry frameworks. This work contributes to theoretical mathematics through the exploration of geometric interpretations in differential equation solutions and symplectic structures. As an active member of Anton Alekseev's research team at the University of Geneva, Gaiur participates in collaborative projects examining deep mathematical structures at the intersection of geometry, topology, and mathematical physics, though specific laboratory facilities or team compositions are not detailed in available sources.
Filip Lindskog is a Professor of Insurance Mathematics at Stockholm University (SU) , where he heads the Mathematical Statistics division within the Department of Mathematics . With a background in financial mathematics and actuarial science, his research focuses on quantitative risk management, non-life insurance pricing, and applications of biostochastics and biostatistics. He has co-authored the textbook Risk and Portfolio Analysis: Principles and Methods (Springer, 2012) and supervises PhD students in actuarial mathematics. Education \n \n MSc in Engineering Physics, KTH Royal Institute of Technology (2000) \n PhD in Mathematical Statistics, ETH Zürich (2004) \n Research Interests Filip's work spans actuarial mathematics , financial risk modeling , and insurance analytics . He investigates stochastic processes in regime-switching environments, capital requirements for insurers, and mathematical frameworks for claims reserving. His recent publications emphasize machine learning applications in risk adjustment, asymptotic analysis of Poisson models, and regulatory compliance under IFRS 17.\n Scientific Contributions \n \n Editor, Scandinavian Actuarial Journal (2018–present) \n Director of SU's Master's Program in Actuarial Mathematics (2016–present) \n Head of SU's Mathematical Statistics Division (2018–present) \n \n Students and Collaborations Current and former PhD students include Nils Engler , Lina Palmborg , Jonas Alm , and Johan Nykvist . Former postdocs include Julie Thøgersen , Abhishek Pal Majumder , and Kristoffer Lindensjö . His research group explores discrete random structures, financial applications of biostatistics, and insurance modeling under capacity constraints.\n
Panagiotis Papastamoulis serves as Assistant Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). He joined AUEB in April 2020 after working as an Adjunct Lecturer from 2018-2019 and completing extensive postdoctoral research at prestigious institutions including the University of Manchester (2012-2018) and INRA in France (2011-2012). His educational background includes a BSc in Mathematics from the University of Patras (2003), an MSc in Applied Statistics (2006), and a PhD in Statistics (2010) from the University of Piraeus. His doctoral thesis addressed the label switching problem in Bayesian analysis of mixtures of distributions under the supervision of Professor G. Iliopoulos. Dr. Papastamoulis's research program centers on Bayesian and computational statistics, with particular expertise in finite mixture models, model-based clustering, and bioinformatics applications. His methodological contributions span theoretical developments in label switching solutions, reversible jump MCMC algorithms, and practical implementations for RNA-seq data analysis. His work demonstrates a consistent trajectory from foundational statistical theory to real-world biological applications. Analysis of his publication record reveals a strong focus on developing statistical methodology for complex data structures, with significant contributions to mixture modeling, Bayesian factor analysis, and bioinformatics. His most recent work (2023-2025) extends into cure rate modeling, directional data analysis, and multinomial mixture models for spatial data, showing continued innovation while maintaining connections to his core research themes. As an educator, he teaches undergraduate courses including Linear Models and Bayesian Inference Methods, and graduate courses such as Statistical Genetics-Bioinformatics and High Dimensional Statistics. He has also developed multiple open-source R packages that have become standard tools in the statistical community, including label.switching, BayesBinMix, and fabMix, which address fundamental challenges in mixture model analysis. Dr. Papastamoulis actively contributes to the academic community through organizing research seminars at AUEB and participating in conference committees, including the 22nd European Young Statisticians Meeting in 2021. His research integrates theoretical statistical development with practical computational implementations, creating tools that advance both methodology and application in multiple scientific domains.
Prof. Dr. Cora Uhlemann is a faculty member at the University of Bielefeld within the Faculty of Physics . She is also affiliated with the Bielefeld Graduate School in Theoretical Sciences as a Deputy Director and part of the Astroparticles and Cosmology Group . Her research focuses on advanced cosmological modeling, including weak lensing statistics, dark matter dynamics, and modified gravity theories. She contributes significantly to the CosmoVerse White Paper and Euclid mission initiatives, developing innovative mathematical frameworks for analyzing cosmic structures and gravitational probes. Key trends in her publications highlight applications of probability distribution functions (PDFs), large deviation theory, and quantum-inspired methods to cosmology. She leads efforts to improve cosmological parameter estimation through higher-order statistics and systematics mitigation in surveys. Uhlemann is actively involved in interdisciplinary research through Bielefeld’s CRIStal portal and collaborates with institutions like the Center for Cognitive Interaction Technology (CITEC) and Bielefeld Center for Data Science (BiCDaS) .
Dr. Kassian Kobert is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering, the Institute for Bioinformatics Infrastructure (BIBI), and the Center for Biotechnology (CeBiTec). His primary research base is within the Genome Informatics Group, where he focuses on computational approaches to genomic analysis and biological data processing. He maintains an active research presence with recent publications extending to 2022. Dr. Kobert's research interests center on bioinformatics and computational biology, with particular expertise in algorithm development for genomic data analysis. His work bridges computer science and biological applications, focusing on creating efficient computational methods for handling large-scale genomic datasets. His research spans phylogenetic inference, sequence alignment, viral evolution, and the development of specialized software tools for next-generation sequencing data processing. The interdisciplinary nature of his work connects computer science theory with practical biological applications, particularly in evolutionary biology and genomics. Analysis of Dr. Kobert's publication record reveals a consistent focus on developing computational methods for biological data analysis. His work demonstrates a trajectory from fundamental algorithm development to practical software implementation, with notable contributions including the PEAR read merger and ExaBayes phylogenetic analysis tool. His research shows strong emphasis on computational efficiency, particularly for handling the massive datasets generated by modern genomic technologies. The publications indicate expertise in both theoretical computer science aspects (algorithm design, computational complexity) and practical biological applications (viral evolution, phylogenetic analysis). Dr. Kobert appears to have significant involvement in software development for bioinformatics applications, with several publications describing tools that have become standard in genomic analysis workflows. His work on parallel computing approaches suggests engagement with high-performance computing environments necessary for modern genomic research.
Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.
Ivan Cherednik is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research spans multiple disciplines in mathematics and its applications. Representation theory Mathematical physics Combinatorics Number theory Algebraic geometry Low-dimensional topology Mathematical biology Quantitative finance Cherednik's work has been revolutionary in several areas of mathematics. He's known for defining Double Affine Hecke Algebras (DAHA) and proving Macdonald's conjectures in q-combinatorics. His research has produced new theories of hypergeometric functions, DAHA invariants of algebraic knots and links, and new developments in the theory of Rogers-Ramanujan identities. Recent publications show his continued research activity, with work in 2024 on R-matrix quantization of formal loop groups, superpolynomials of algebraic links, and connections between zeta-polynomials, superpolynomials, DAHA, and plane curve singularities. He has also applied his mathematical expertise to real-world problems, particularly in modeling the spread of the COVID-19 virus using Bessel functions and other mathematical approaches.
Koen Hillewaert is a Lecturer at the University of Liège (ULiège), working within the Faculty of Applied Sciences, specifically in the Department of Aerospace and Mechanics. He is affiliated with the Design of Turbomachines and Propulsors (DoTP) research group, located in Building B52/3 at Quartier Polytech 1. His work focuses on advanced computational methods for fluid dynamics and turbomachinery applications. His educational background includes: Doctor of Engineering Sciences from Catholic University of Louvain (2013) Electromechanical Engineer from Ghent University (1995) Hillewaert's research spans multiple areas of fluid dynamics and turbomachinery, with particular expertise in computational fluid dynamics using high-order methods. His work focuses on turbomachinery design and analysis, including gas turbines, hydraulic turbines, pumps, and compressors. He has developed expertise in discontinuous Galerkin methods for fluid flow simulations, with applications ranging from aerospace propulsion to wind energy systems. His research integrates numerical analysis, machine learning techniques for flow modeling, and advanced computational techniques for plasma physics applications. His recent publications demonstrate a strong focus on high-fidelity simulation methods for turbomachinery flows, with particular attention to wall-resolved simulations, turbulence modeling, and geometric variability effects. There's a clear trend toward integrating machine learning techniques with traditional computational fluid dynamics approaches, particularly for wall modeling in separated flows. His work spans both fundamental numerical method development and practical engineering applications in aerospace and energy systems. Hillewaert holds significant institutional roles including Vice-president of KNC in the European Research Community on Flow Turbulence and Combustion (ERCOFTAC) and membership on the steering committee of the CFD General Notation System (CGNS), indicating his standing in the international CFD community. He teaches several advanced courses including Aerospace Propulsion, Aerothermodynamics of High-Speed Flows, Wind Energy, Flow in Turbomachines, Practical Fluid Mechanics for the Process Industry, and Turbomachines, demonstrating his broad expertise across fluid dynamics and turbomachinery applications.
Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.
Khanh Duy Trinh is a Professor (non-tenure-track) at Waseda University's Global Center for Science and Engineering, specializing in probability theory and its applications to random matrix theory and stochastic topology. He holds a PhD from Osaka University (2012) and has held academic positions at Tohoku University and Kyushu University. Current affiliation: Waseda University (2025-present) Past roles: Associate Professor at Waseda (2019-2025), Tohoku University, Kyushu University Research areas: Beta ensembles, Random topology, Spectral measures, Stochastic geometry His work demonstrates universal behavior in random matrix models through spectral analysis and topological persistence. Key contributions include central limit theorems for eigenvalue statistics, Poisson approximations in high-temperature regimes, and geometric interpretations of persistence diagrams. His recent papers focus on generalized beta processes and higher-dimensional complex structures. Current projects include: JSPS Grant 2024-2029: Universal approaches in random matrix theory Past JSPS Grant 2019-2023: Multi-aspects of beta ensembles Teaching activities at Waseda include: Introduction to Probability and Statistics Advanced Probability and Statistics Master's Thesis advising in Pure and Applied Mathematics
Philip J. Morrison is a distinguished Professor of Physics at The University of Texas at Austin, holding the Texas Atomic Energy Research Foundation Professorship. He maintains dual research appointments as a Research Scientist at the Institute for Fusion Studies and Affiliated Faculty at the Oden Institute for Computational Engineering and Sciences. His career spans over four decades with continuous service at UT Austin since 1981, progressing from Assistant to full Professor. Dr. Morrison earned his B.A. (1972), M.S. (1974), and Ph.D. (1979) in Physics from the University of California San Diego. His academic journey includes postdoctoral work at Princeton University's Plasma Physics Lab and teaching positions at UCSD prior to joining UT Austin. A mathematical and theoretical physicist by training, Morrison's research centers on the intersection of plasma physics, nonlinear dynamics, and computational mathematics. His primary interests include: Computational statistical mechanics and Hamiltonian dynamics Nonlinear chaos theory in finite and infinite degree-of-freedom systems Metriplectic systems for thermodynamically consistent modeling Structure-preserving algorithms for plasma simulations Geophysical fluid dynamics applications His recent publications (2024-2025) demonstrate continued leadership in developing mathematically rigorous frameworks for plasma physics and computational methods. Dr. Morrison's exceptional contributions have been recognized with numerous prestigious awards: 2024 John Dawson Award for Excellence in Plasma Physics (APS) 2016 Alexander von Humboldt Research Award 2013 Agostinelli International Prize (Accademia Nazionale dei Lincei) 1992 Fellow of the American Physical Society Multiple teaching awards including the 2013 College of Natural Sciences Teaching Excellence Award As an educator and researcher, Morrison maintains active collaborations across disciplines through the Institute for Fusion Studies and Oden Institute. His work bridges theoretical physics with practical computational applications, particularly in fusion energy research. The Geophysical Fluid Dynamics Program has benefited from his expertise for over twenty-five years, demonstrating his commitment to interdisciplinary science. His laboratory resources are enhanced through UT Austin's advanced computational infrastructure and partnerships with national laboratories. The metriplectic frameworks he develops provide foundational tools for next-generation plasma simulation codes used in fusion research worldwide.
Rami Vainio is a Professor of Space Physics at the University of Turku's Department of Physics and Astronomy, where he serves as head of the Space Research Laboratory (SRL). SRL conducts experimental, theoretical, and computational research on high-energy space phenomena, with focus areas including solar energetic particles (SEPs) and collisionless shocks. The laboratory maintains active collaborations with international research groups. Professor Vainio's research examines: SEP physics : Acceleration mechanisms during solar eruptions and transport through interplanetary space Collisionless shocks : Energy dissipation and particle acceleration in space plasmas Space weather impacts : Radiation risks to technology and humans in space He leads curriculum development for Space Physics and teaches courses including: Mathematical Methods in Physics II (BSc) Space Physics (BSc) Hydrodynamics and Hydromagnetics (MSc) Plasma Astrophysics (MSc) Under his direction, SRL develops particle detection instrumentation and simulation codes. Recent publications focus on SEP forecasting, shock wave analysis using Solar Orbiter data, and radio burst observations with LOFAR.
Renaud Raquépas is a Phillip Griffiths Assistant Research Professor in the Department of Mathematics at Duke University, where he has been working since 2025 under the mentorship of Professor Jonathan C. Mattingly. Prior to his position at Duke, he was a Courant Instructor in the Mathematics Department of the Courant Institute at New York University (2022-2025), hosted by Professor Lai-Sang Young, and a postdoctoral researcher at CY Cergy Paris Université (2021-2022), working with Professor Armen Shirikyan. His educational background includes a PhD in Mathematics from McGill University and Université Grenoble Alpes (2017-2020), where he was jointly supervised by Professors Vojkan Jakšić and Alain Joye. His doctoral thesis focused on "Tools and results in the study of entropy production." He also earned an MSc in Mathematics and Statistics from McGill University (2016-2017) under the supervision of Professor Vojkan Jakšić, with a thesis on "Heat full statistics and regularity of perturbations in quantum statistical mechanics." His undergraduate studies were completed at McGill University, where he also earned his Master's degree over a period of approximately five years. Raquépas's research primarily focuses on mathematical physics, with particular emphasis on time-dependent aspects of statistical mechanics and entropy production in both quantum and classical systems. His work bridges several mathematical disciplines including probability theory (particularly large deviations and stochastic differential equations), dynamical systems and ergodic theory (covering recurrence, mixing, theory of C*-algebras, and random dynamical systems), and operator theory (focusing on spectra, resolvents, perturbation theory, and one-parameter semigroups). His research addresses fundamental questions about nonequilibrium statistical mechanics, quantum information, and the mathematical foundations of thermodynamics. The most recent publications by Raquépas demonstrate a consistent focus on entropy production, large deviation principles, and the mathematical structure of statistical mechanical systems. His work spans both classical and quantum domains, with particular attention to the connections between information theory, probability, and physics. A significant portion of his research examines return times, waiting times, and their relationship to entropy estimators, while other papers explore quantum measurement processes, fermionic systems, and diffusions with various types of noise. His publications appear in prestigious journals including Communications in Mathematical Physics, Annales Henri Poincaré, and Journal of Mathematical Physics. Raquépas has presented his research at numerous international conferences and seminars, including the IEEE International Symposium on Information Theory, the International Congress of Mathematical Physics, and various departmental seminars at institutions worldwide. His work has been featured at specialized workshops on entropy, dynamical systems, and mathematical physics. As an educator, Raquépas has taught a variety of undergraduate mathematics courses at multiple institutions. At Duke University, he is scheduled to teach Probability in the Fall 2025 semester. Previously at NYU, he taught courses including Ordinary Differential Equations, Introduction to Mathematical Modeling, Linear Algebra, and Applied Complex Variables. He has also taught mathematics courses in French at CY Cergy Paris Université and Université Grenoble Alpes, demonstrating his bilingual capabilities (French is his first language, with fluency in English). Raquépas was born in the 1990s in the Province of Québec and has been involved in mathematical outreach activities, including service on the committee of the Seminars in Undergraduate Mathematics in Montréal and work on the website of the French-language mathematics magazine Accromath.
Zhiyong Liang serves as Professor, Director of the High-Performance Materials Institute (HPMI), and Associate Dean for Research at Florida State University's College of Engineering. He holds dual appointments in the Industrial and Manufacturing Engineering and Materials Science & Engineering departments. His research drives innovation in advanced composite materials and nanotechnology through leadership in multiple centers including the Florida Center of Excellence in Advanced Materials (CEAM). His educational background includes a Ph.D. (2000), M.Sc. (1990), and B.Sc. (1987) in Materials Science and Engineering from Beijing University of Aeronautics & Astronautics. His research focuses on carbon nanotube buckypaper materials, multifunctional composites, and nanoscale manufacturing processes. Key achievements include developing roll-to-roll manufacturing techniques for aligned carbon nanotubes and creating novel nanocomposite structures with enhanced thermal/electrical properties. Liang's publication portfolio demonstrates consistent innovation in nanomaterials science, with emphasis on carbon nanotube alignment mechanisms, buckypaper composite fabrication, and energy storage applications. His work bridges fundamental nanoscale characterization with practical manufacturing solutions, particularly in aerospace and energy sectors. Recent publications highlight advancements in atomic-resolution interface analysis, flexible sensor design, and sustainable composite recycling methods. As principal investigator, Liang has secured over 50 research grants from NSF, AFOSR, ONR, and industry partners like Cytec Engineering Materials. His leadership has generated significant intellectual property including 25 US patents related to carbon nanotube processing and composite manufacturing. The Florida Center of Excellence under his direction represents major state investment in advanced materials research. Liang has mentored 36 graduate students (10 Ph.D. and 26 Master's graduates) and 22 postdoctoral researchers. His former students hold prominent positions in academia (including two U.S. professors), national laboratories, and industry leaders like Honeywell and M.C. Gill Corp. His research group maintains strong industry partnerships through sponsored projects and technology transfer initiatives.
Dipanwita Barai serves as Visiting Assistant Professor in the Department of Economics at the University of Tampa, with concurrent affiliation to Washington State University where she is completing her Ph.D. in Economics (expected 2025). Her academic credentials include a B.S.S. from Shahjalal University of Science and Technology (2014), an M.S. from the University of Texas at El Paso (2017), and an M.S. from the University of New Hampshire (2020). Dr. Barai specializes in applied macroeconomics, with research interests spanning econometrics, energy and environmental economics, international trade, and agricultural economics. Her scholarly work integrates theoretical frameworks with rigorous empirical methods to address complex issues in energy economics, agricultural trade, and public health. She employs advanced econometric techniques including error correction models, structural gravity models, and Poisson pseudo-maximum likelihood estimation across her research portfolio. Her research demonstrates strong policy relevance, particularly examining how economic incentives, institutional structures, and external shocks affect resource allocation and trade outcomes. Her work on drilled but uncompleted oil wells across U.S. regions highlights regional heterogeneity in energy markets, while her studies on pandemic impacts on U.S. meat and grain exports reveal critical disruptions to international trade flows. She also investigates optimal strategies for managing transboundary animal diseases, addressing spatial spillovers and global health risks. SES Excellence in Teaching Award, Washington State University (2024) AAEA Travel Grant (2022, 2023) SES Endowment for Sustainable Development Fellowship, WSU (2023) Scott & Betty Lukins Graduate Fellowship, WSU (2023) Vice Chancellor Award, SUST (2020) Prime Minister's Gold Medal, Bangladesh (2018) As an educator, Dr. Barai teaches Introduction to Microeconomics, Intermediate Microeconomics, Intermediate Macroeconomics, and Introduction to Econometrics. She has received high evaluation scores for her teaching, with a median of 4.5 and mode of 5 out of 5 for courses like ECONS 302 and ECONS 305. Her technical expertise includes MATLAB, Python, R, EViews, STATA, SAS, and LaTeX, supporting her interdisciplinary research approach across economics, environmental science, and public health domains.
