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Jonas Bergström is a Professor in the Department of Mathematics at Stockholm University specializing in Algebra, Geometry, Topology, and Combinatorics. His research focuses on arithmetic geometry, moduli spaces, Siegel modular forms, and number theory, with extensive collaborations across international institutions including KTH Royal Institute of Technology. His research interests span algebraic geometry, topology, combinatorics, and number theory, with particular emphasis on moduli spaces of curves, abelian varieties, Siegel modular forms, and arithmetic geometry. Bergström's work bridges theoretical mathematics with computational approaches, often developing algorithms for complex mathematical structures. His research group actively explores commutative and homological algebra, complex and real algebraic geometry, arithmetic geometry, homotopy theory, and Ramsey theory. The most recent publications reveal a strong focus on cohomology of moduli spaces, Siegel modular forms, abelian varieties over finite fields, and L-functions. His work demonstrates a consistent pattern of combining algebraic geometry with number theory, particularly investigating arithmetic properties of algebraic varieties and developing computational methods for modular forms. The research shows increasing emphasis on algorithmic approaches and connections to theoretical physics through moduli space cohomology. Bergström has supervised several PhD students including Sjoerd de Vries (current), Stefano Marseglia, and Olof Bergvall (with Prof. Carel Faber). He currently mentors postdoctoral researchers Séverin Philip and Thomas Wennink, while former postdocs include Angelina Zheng, Valentijn Karemaker, Oliver Leigh, and Alex Samuel Bamunoba. His research is supported through collaborations with major mathematical networks including the Nordic number theory network and joint seminars with KTH. He is affiliated with the Algebra and Geometry Seminar (KTH and SU) and maintains active research connections through multiple collaborative projects, including joint work with Gerard van der Geer and Carel Faber on Hecke operators and Siegel modular forms. Bergström also contributes to open mathematical research through GitHub repositories containing computational results on cohomology of moduli spaces.

• Algebra
• Geometry
• Topology
• +5 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more

Evan Sorensen is a Joseph F. Ritt Assistant Professor in the Department of Mathematics at Columbia University, specializing in probabilistic models of complex stochastic systems. Education PhD in Mathematics, University of Wisconsin—Madison (2023), advised by Timo Seppäläinen MA in Mathematics, University of Wisconsin—Madison (2020) BS in Mathematics, Brigham Young University (2017) Research Focus Dr. Sorensen investigates universal phenomena in stochastic processes through the lens of the KPZ universality class. His work rigorously analyzes prelimiting models including first-and last-passage percolation, directed random polymers, and interacting particle systems to uncover deep connections to random matrix theory and nonlinear stochastic partial differential equations. Current research explores scaling limits, fluctuation exponents, and invariant measures in these high-dimensional random environments. Affiliations & Activities Joseph F. Ritt Assistant Professor position (prestigious postdoctoral fellowship at Columbia) Active researcher in probability theory with focus on integrable probability No scientific awards, student advising records, or laboratory affiliations are publicly documented in available sources.

• Probability Theory
• Stochastic Processes
• KPZ Universality Class
• +4 more

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +8 more

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +3 more

Roles and Affiliations: Alexander Herbertsson is a Senior Lecturer in Statistics and Quantitative Finance at the University of Gothenburg, affiliated with the CFF-Centre for Finance. He is based in the Department of Economics with Statistics, located at Vasagatan 1, Gothenburg. His work focuses on financial risk management, credit risk modeling, and quantitative finance methodologies. Education: Ph.D. in Economics: Quantitative Finance (2007), University of Gothenburg Licentiate of Engineering in Industrial Mathematics (2005), Chalmers University of Technology M.Sc. in Engineering Physics (Applied Mathematics specialty) (2001), Chalmers University of Technology Research Interests: Herbertsson's research emphasizes applied financial mathematics and statistical methods in finance. Key areas include credit risk modeling (default contagion, systemic risks), financial engineering, and the development of dynamic models for portfolio credit risk. His work often integrates Markov chain models, phase-type distributions, and stochastic processes to analyze dependency structures and pricing of credit derivatives. Recent studies explore saddlepoint approximations for portfolio risk analysis and risk management under exogenous shocks. Teaching: He teaches advanced courses in credit risk modeling, quantitative finance, and applied probability theory, emphasizing practical applications in risk management and financial markets. Grants and Labs: While specific grants are not detailed in the text, his affiliation with the CFF-Centre for Finance suggests involvement in collaborative financial research projects. His work often addresses real-world applications of theoretical models in systemic risk and portfolio hedging.

• Quantitative Finance
• Credit Risk
• Financial Engineering
• +3 more

Fredrik Viklund is a Professor at the Department of Mathematics, KTH Royal Institute of Technology. He holds the Wallenberg Scholar title and receives research funding from the Swedish Research Council (VR) and the Gustafson Foundation. His research focuses on the intersection of complex analysis, probability, and mathematical physics, with applications to computer science and applied mathematics. PhD from KTH Simons Fellow and J.F. Ritt Assistant Professor at Columbia University Associate Professor at Uppsala University Research interests include: Schramm-Loewner evolution (SLE) curves Critical lattice models Laplacian growth and aggregation Conformal field theory Teichmüller theory Gaussian fields Recent publications focus on Coulomb gases, Loewner energy, Hele-Shaw flows, and lattice Yang-Mills theories. Notably, his work explores the interplay between Loewner chains and Dirichlet energies. Scientific roles: Editorial Board: Arkiv för Matematik and Analysis and Mathematical Physics Scientific Council for Natural and Engineering Sciences, Swedish Research Council (VR) Member of the Random Matrix Theory and Random Geometry (RMRG) research group Advising: Supervises PhD students and collaborates with postdocs. Organizes the KTH Probability and Mathematical Physics Seminar.

• Complex Analysis
• Probability Theory
• Mathematical Physics
• +9 more

Dr. Alfonso Mateos Caballero is a full Professor in the Department of Artificial Intelligence at the School of Computer Science, Universidad Politécnica de Madrid (UPM), where he leads the Decision Analysis and Statistics Research Group. His career spans extensive contributions to Operations Research, Decision Support Systems, and Data Science, with a focus on complex networks and multicriteria decision-making. He has participated in 58 research projects (23 as Principal Investigator), including European, national, and regional initiatives, as well as collaborations with 27 companies. His scholarly output includes 41 JCR-indexed research papers, 38 book chapters with international publishers, 171 conference proceedings, and six co-authored books on Operations Research and Data Science. His recent work explores financial market dynamics using random matrix theory, pandemic risk mitigation through air transport management, and Parkinson's disease detection via voice waveform analysis. He has supervised five PhD theses and 37 Master's projects, while contributing to software development and evaluating over 25 research proposals.

• Operations Research
• Decision Support Systems
• Data Science
• +3 more

Xiangfeng Yang is an Associate Professor and Docent in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical probability and applied mathematical modeling, with a strong emphasis on large deviations theory and stochastic processes. His research interests include: Large Deviations Theory Random Matrices and Sample Covariance Matrices Stochastic Bridges (Bernstein and Markov) Longest Runs, Gaps, and Extremal Eigenvalues Hidden Markov and Reciprocal Processes Modern Multivariate Statistical Analysis Xiangfeng Yang's recent publications (2015–2024) reflect a consistent and deep engagement with probabilistic asymptotics and extreme behavior in stochastic systems. His work spans theoretical foundations in probability, applications in random matrix theory, and modeling in financial and environmental contexts. A notable trend is his focus on large deviation principles across diverse settings—ranging from eigenvalues of random matrices to gaps in Poisson processes and runs in Markov chains. He frequently collaborates with PhD students, indicating an active supervisory role. His scientific contributions are published in reputable journals such as Journal of Applied Probability , Stochastic Processes and their Applications , and Theory of Probability and Mathematical Statistics . While no explicit awards are listed, the quality and volume of his output suggest recognition within the mathematical community. Xiangfeng Yang has supervised several PhD students to completion, including Joseph Okello Omwonylee, Denise Uwamariya, and Stefane Saize, with research topics closely aligned with his expertise in large deviations and stochastic modeling. His research likely involves collaboration within the Applied Mathematics division, though no specific lab or team name is mentioned. He continues to contribute actively to modern theoretical and applied probability.

• Large Deviations Theory
• Probability Theory
• Stochastic Processes
• +11 more

Sajad Daei Omshi is a Postdoctoral researcher at KTH Royal Institute of Technology, working in the Division of Information Science and Engineering under Professors Mickael Skoglund and Gabor Fodor. His research focuses on Integrated Sensing and Communications (ISAC), wireless signal processing, and radar-communication system design. Previously, he held a postdoc position at EURECOM under Professor Marios Kountouris, focusing on semantic and goal-oriented communications. Education: PhD in Electrical and Electronics Engineering from Iran University of Science and Technology (2015–2019). Research areas include mathematical optimization, compressed sensing, super-resolution techniques, MIMO communications, and channel estimation. His work addresses challenges in 6G networks, millimeter-wave systems, and radar-communication integration. Key research contributions span ISAC frameworks, blind signal recovery, and distributed computing. Notable achievements include the 2020 IEEE Iran Section Outstanding PhD Thesis Award. He collaborates actively with institutions like Farzad Parvaresh's Lab at KTH and has published over 50 articles in top conferences/journals. Awards: Outstanding PhD thesis award of IEEE Iran Section (2020) Advising/Grants: No specific advising information available, though his postdoc roles imply mentorship in collaborative projects. His research is supported by grants focusing on ISAC and 6G technologies. Labs/Teams: Member of Farzad Parvaresh's Lab at KTH, contributing to signal processing and communication systems research.

• Integrated Sensing and Communications
• Wireless Communications
• Signal Processing
• +3 more

Maxim Zabzine is a Professor at Uppsala University, holding dual affiliations: one within the Department of Mathematics under the Centre for Geometry and Physics, and another within the Department of Physics and Astronomy in the Theoretical Physics division. His work bridges theoretical physics and mathematical physics, focusing on advanced geometric structures and their applications. His research interests encompass supersymmetry, quantum field theory, and topological field theories, with a particular emphasis on topics such as Yang-Mills theory, Virasoro constraints, and the interplay between geometry and physical models. He has contributed to studies involving localization techniques, equivariant cohomology, and sigma models on curved manifolds. Articles published since 2020 highlight his exploration of cutting-edge theoretical frameworks, including equivariant models, refined Chern-Simons theories, and modular properties of partition functions. His collaborative work often integrates geometric insights with high-energy physics. No scientific awards or grants are explicitly mentioned in the provided text. He is affiliated with the Ångström Laboratory at Uppsala University, contributing to interdisciplinary research at the intersection of mathematics and physics.

• Theoretical Physics
• Quantum Field Theory
• String Theory
• +4 more

Kevin Schnelli is an Associate Professor at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. His research focuses on Probability Theory , Mathematical Statistics , and Mathematical Physics , particularly Random Matrix Theory and Free Probability . He is affiliated with the focus group Random Matrices, Stochastic Models and Analysis and collaborates with researchers like Maurice Duits and Kurt Johansson.

• Probability Theory
• Mathematical Statistics
• Mathematical Physics
• +3 more

Gaultier Lambert is an Associate Professor at the Department of Probability, Mathematical Physics and Statistics, KTH Royal Institute of Technology. His research focuses on the intersection of probability theory, mathematical physics, and random matrix theory, with particular emphasis on determinantal processes, log-correlated fields, and quantum statistics. He holds a PhD and has an extensive publication record in high-impact journals. Key research areas include fluctuations in free fermionic systems, multiplicative chaos, and universality in random matrix ensembles. His work explores topics such as characteristic polynomials of random matrices, law of large numbers for Coulomb gas potentials, and quantum statistics transmutation via magnetic flux attachment. Recent publications (2021–2025) emphasize asymptotic analysis of eigenvalue fluctuations, universality in semiclassical systems, and stochastic processes in high-dimensional spaces. Lambert’s contributions bridge theoretical probability with applications in quantum physics and statistical mechanics. No scientific awards or grants are explicitly listed in the provided texts. He advises no listed students and is affiliated with the Lindstedtsvägen 25 research group at KTH.

• Mathematical Physics
• Probability Theory
• Random Matrix Theory
• +5 more

He Guo is a Researcher in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. He holds a Ph.D. from Georgia Institute of Technology and a B.S. with distinction from Zhejiang University. His research focuses on combinatorics and graph theory, particularly probabilistic and topological methods. He has been affiliated with Technion – Israel Institute of Technology as a postdoctoral fellow, hosted by Ron Aharoni, and currently works under the mentorship of István Tomon. Key achievements include receiving the Top Graduate Student Award during his Ph.D., ranking first in his undergraduate class, and presenting at events like the European Conference on Combinatorics (Eurocomb'25). His work spans topics such as Ramsey theory, matroid intersections, rainbow cycles, and random graph structures. He maintains an active presence in the Discrete Mathematics research group and contributes to academic service and teaching.

• Combinatorics
• Graph Theory
• Probabilistic Methods
• +2 more

Göran Bergqvist is a Professor at Linköping University, affiliated with the Department of Mathematics and the division of Algebra, Geometry and Discrete Mathematics (ALGD). His work contributes to theoretical and applied aspects of algebra and discrete mathematics. His research interests include: Linear Algebra Tensor Analysis Matrix Theory Computational Mathematics Discrete Mathematics The recent publications indicate a strong focus on tensor decompositions, matrix spectra, and probabilistic aspects of tensor ranks. His work bridges pure mathematical theory with applications in signal processing and numerical analysis. There are no scientific awards listed in the provided information. He has advised no students listed in the available data and no information is available about grants or funding. No specific research team or laboratory is mentioned.

• Algebra
• Discrete Mathematics
• Linear Algebra
• +4 more