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Olof Runborg is a Professor in Numerical Analysis at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He works in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH. His research focuses on developing and analyzing numerical methods for partial differential equations, particularly for wave propagation problems. His educational background includes: MSc in Electrical Engineering from KTH (1992) BSc in Economics & Business Administration from SSE (1994) PhD in Numerical Analysis/Applied Mathematics from KTH (1998) Postdoc at Paris VI University (1999) Postdoc at Princeton University's Program in Applied and Computational Mathematics (2000-2001) Became Docent at NADA in 2003 Appointed Professor at KTH in 2010 Professor Runborg's research centers on the numerical treatment of partial differential equations, with special emphasis on wave propagation problems. His work spans multiple areas including high-frequency waves, multiscale phenomena, numerical homogenization, uncertainty quantification, multiresolution analysis, Gaussian beams, and mesh generation. A recurring theme in his research is developing methods to solve computationally expensive problems more efficiently while maintaining accuracy. His approach often involves coupling different numerical methods or reformulating equations to create more efficient computational approaches. His research has applications across physics and engineering domains where wave phenomena are important. An analysis of his recent publications (2015-2025) shows continued focus on high-frequency wave propagation with expanding applications to areas like the Landau-Lifshitz equation for magnetic materials and elastic wave propagation. His work bridges theoretical numerical analysis with practical computational techniques, often developing novel methods like the WaveHoltz iteration for solving the Helmholtz equation. The publications demonstrate increasing attention to uncertainty quantification and multiscale methods while maintaining strong theoretical foundations in error analysis. Professor Runborg teaches several courses at KTH including Numerical Methods (basic course), Applied Numerical Methods, Numerical Algorithms for Data-Intensive Science, and Selected Topics in Numerical Analysis II. He serves as examiner for degree projects in Scientific Computing. His teaching reflects his research expertise in numerical methods and computational mathematics, providing students with both theoretical foundations and practical implementation skills. Beyond his core research, Professor Runborg has engaged in interesting side projects, such as his analysis of 'Numbers on the Web' where he investigated the frequency of numbers 11-1000 in web content, revealing patterns related to dates, time, computer systems, and cultural phenomena. This demonstrates his broader interest in data analysis and computational approaches to understanding patterns in information.

• Numerical Analysis
• Partial Differential Equations
• Wave Propagation
• +7 more

Georgios Dimitroglou Rizell is a Senior Lecturer in the Department of Mathematics at Uppsala University, Sweden, where he also serves as Head of the Department since 2020. His academic work is centered at the Ångström Laboratory, where he conducts research in symplectic and contact topology. He maintains dual affiliations with both the Department of Mathematics and the Center for Geometry and Physics at Uppsala University. Dr. Dimitroglou Rizell earned his PhD from Uppsala University in 2012 under the supervision of Tobias Ekholm. Following his doctoral studies, he held postdoctoral positions at the Université Libre de Bruxelles (2012-2013), Université Paris-Sud (2013-2014), and the University of Cambridge (2014-2015), all supported by prestigious fellowships from the Knut & Alice Wallenberg Foundation. He returned to Uppsala University as a researcher (2015-2017) and Assistant Lecturer (2017-2021) before being promoted to Senior Lecturer in 2021. His research primarily focuses on symplectic and contact topology, with special emphasis on understanding and classifying Lagrangian and Legendrian submanifolds. His work employs advanced mathematical techniques including pseudoholomorphic curves, pseudoholomorphic foliations, Symplectic Field Theory, and Floer homology. His investigations span a broad range of topics within geometric topology, from the classification of Lagrangians near the Whitney immersion to the study of Legendrian submanifolds and their invariants. His research has significant implications for understanding the geometric structures underlying classical mechanics and quantum physics. His recent publications (2020-2025) demonstrate a consistent focus on Lagrangian and Legendrian submanifolds, with particular attention to their classification, invariants, and interactions with symplectic structures. A notable trend is the development of new techniques for studying C^0-limits of Legendrians, exact Lagrangians in various settings, and the geometric generation of Fukaya categories. His collaborative work with researchers like Michael Sullivan, Roman Golovko, and others has produced significant advances in Floer theory and symplectic field theory. Scientific Awards Wallenberg Scholar (2023-2028, KAW 2023.0294) Wallenberg Academy Fellow (extension 2022-2027, KAW 2021.0191) Wallenberg Scholar (2022-2023, KAW 2021.0300) Wallenberg Academy Fellow (2017-2021, KAW 2016.0198) As Head of the Department of Mathematics, Dr. Dimitroglou Rizell oversees academic programs and research initiatives. His leadership is supported by significant funding from the Knut & Alice Wallenberg Foundation, which has awarded him multiple prestigious fellowships throughout his career. These grants have enabled his research in symplectic geometry and supported collaborative projects with international mathematicians. Dr. Dimitroglou Rizell is actively involved in the Center for Geometry and Physics at Uppsala University, where he collaborates with researchers across mathematical disciplines. His work intersects with theoretical physics, particularly in areas related to geometric quantization and the mathematical foundations of quantum mechanics. He participates in seminar series and reading groups focused on symplectic topology and its applications.

• Symplectic Geometry
• Contact Topology
• Lagrangian Submanifolds
• +4 more

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

Oliver Schlotterer holds the position of Associate Professor at Uppsala University, affiliated with both the Department of Mathematics (Centre for Geometry and Physics) and the Department of Physics and Astronomy (Theoretical Physics). His research focuses on theoretical physics and mathematical structures in string theory, particularly exploring string amplitudes, modular forms, and algebraic geometry. He has contributed to understanding one-loop string amplitudes, modular graph forms, and supersymmetric field theories. Education: Not explicitly detailed in the provided text. Departments: Joint appointment in Mathematics and Theoretical Physics. His research interests include the interplay between string theory and mathematical frameworks like modular forms, algebraic geometry, and polylogarithmic functions. Recent work emphasizes chiral-splitting techniques, cyclic products of kernels, and the coaction principle in scattering amplitudes. Collaborations span topics from supersymmetric Yang-Mills theories to Einstein-Yang-Mills systems. Publications highlight advancements in string perturbation theory, including studies on non-holomorphic modular forms, genus-one integrals, and the single-valued map in string theory. His work often bridges high-energy physics with number-theoretic structures, such as zeta functions and Poincaré series. No awards or grants are listed in the provided data. He advises no students explicitly mentioned, though his collaborative research suggests involvement in training through projects. Active in the Centre for Geometry and Physics and theoretical physics groups, his research explores foundational aspects of string theory and their mathematical implications.

• Theoretical Physics
• String Theory
• Mathematical Physics
• +3 more

Andrei Khrennikov is Professor of Mathematics at the Department of Mathematics, Linnaeus University, where he also serves as director of the International Center for Mathematical Modeling (ICMM) . He leads a vibrant research group focused on interdisciplinary modeling in physics, biology, cognition, and social systems. Research Interests: His work spans a vast interdisciplinary landscape, including mathematical physics, p-adic and non-Archimedean analysis, quantum foundations, quantum-like modeling of cognition and decision-making, econophysics, and biological dynamics . He is a pioneer in applying quantum probability and formalism outside quantum physics, especially in psychology and social sciences. The Växjö series of quantum theory conferences , which he organizes, is the longest-running continuous conference series on quantum foundations, fostering dialogue between theorists, experimentalists, and philosophers. His recent publications (2021–2025) show a strong focus on quantum cognition, p-adic biology, entanglement models, and social laser theory , often leveraging generalized probability and open quantum systems frameworks. Scientific Contributions: Developed quantum-like models for cognition, decision-making, and biological processes. Pioneered use of p-adic and ultrametric analysis in genetics and brain dynamics. Advanced classical random field models as alternatives to quantum interpretations. Introduced the social laser model for collective emotional amplification in societies. He is actively involved in major research projects such as QUARTZ (Quantum Information Access and Retrieval Theory) and DYNALIFE (Information, Coding, and Biological Function) . His work bridges mathematics, physics, and cognitive science, promoting a unified framework for understanding complex systems through quantum-inspired tools.

• Mathematics
• Physics
• Biology
• +23 more

Edwin Langmann is a Professor of Physics at KTH Royal Institute of Technology in Stockholm, Sweden. He holds a PhD in Theoretical Physics from the University of Vienna (1990) and has held academic positions including Assistant Professor at KTH (1994–1998), Postdoc at the University of British Columbia (1991–1994), and various roles at Swedish institutions since 2000. His research focuses on mathematical physics, integrable systems, and superconductivity theory, with contributions to quantum many-body systems and exactly solvable models. Affiliations: Department of Condensed Matter Theory, KTH Royal Institute of Technology Educations: PhD (Theoretical Physics, University of Vienna, 1990), M.Sc. (Technical Physics, TU Graz, 1986) Langmann teaches courses in physics and mathematical methods, advising numerous master’s theses. His work bridges theoretical physics and mathematics, addressing topics like Calogero-Sutherland models, fractional quantum Hall effects, and Hubbard model phase diagrams. Recent research includes antiferromagnetic order in 3D systems and BCS superconductivity with finite-range potentials. He has supervised students including Frode Boman (2021), Max Oliveberg (2021), and Charles Gilljam (2020). His publications span journals such as Communications in Mathematical Physics and Physical Review B , emphasizing integrable systems and quantum field theory.

• Mathematical Physics
• Integrable Systems
• Superconductivity Theory
• +3 more

Robert Berman is a Professor in the Department of Mathematical Sciences at the University of Gothenburg. He is affiliated with the Algebra and Geometry division and can be reached at robertb@chalmers.se. His research focuses on advanced areas of mathematics including algebraic geometry, complex geometry, and geometric analysis, with a particular emphasis on Kähler metrics, Monge-Ampère equations, and probabilistic methods in geometry. His work bridges pure mathematics with applications in theoretical physics and numerical analysis. Key research themes include the study of K-stability in algebraic geometry, the interplay between statistical mechanics and geometric structures, and the analysis of discretized Monge-Ampère equations in optimal transport. Recent publications (2023–2025) address Hölder inequalities in Kähler geometry, Manin-Peyre conjectures, and probabilistic approaches to Kähler-Einstein metrics. Berman collaborates extensively with leading researchers such as Bo Berndtsson and Sébastien Boucksom. His contributions span foundational results in geometric analysis and their implications for birational geometry and arithmetic geometry. No awards or grants are explicitly listed in the provided materials.

• Algebraic Geometry
• Complex Geometry
• Mathematical Physics
• +3 more

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +7 more

Joseph Minahan is a Professor at Uppsala University, affiliated with the Department of Physics and Astronomy (Theoretical Physics) and the Department of Mathematics (Centre for Geometry and Physics). He holds dual Swedish and American citizenship. His research focuses on string theory, quantum field theory, and the AdS/CFT correspondence, with particular emphasis on integrability and localization techniques to derive exact results in supersymmetric theories. Education: BSc in Physics (MIT, 1982), MA and PhD in Physics (Princeton University, 1984–1987). He has held visiting positions at MIT, Harvard, and other institutions. Awards include the Lily och Sven Thuréus Prize (2013) and Bjoerkénska priset (2018), Uppsala University's highest science honor. His work spans topics like Hagedorn temperatures in supersymmetric gauge theories, integrable systems in the ABJM model, and dimensional analysis in supersymmetric Yang-Mills. He has led grants from the Swedish Research Council, Knut and Alice Wallenberg Foundation, and others. His research often bridges high-energy physics and mathematical structures, with contributions to the thermodynamic Bethe ansatz and AdS/CFT integrability.

• String Theory
• Quantum Field Theory
• AdS/CFT Duality
• +3 more

Dan Petersen is a Professor of Mathematics at Stockholm University, specializing in the intersection of algebraic geometry and algebraic topology with a focus on moduli spaces. His research explores topics such as cohomology theories, homological stability, and geometric structures. He has advised PhD students including Erik Lindell, Louis Hainaut, Josefien Kuijper, and Oliver Lindström. His work frequently intersects with geometric topology, number theory, and representation theory, as seen in his recent publications on handlebody groups, Mumford conjectures, and configuration spaces. Collaborations with postdocs such as Johan Alm, Marcel Rubió, and Sylvain Douteau highlight his involvement in advanced research networks. His contributions to algebraic structures and topological methods have advanced understanding in both pure and applied mathematics contexts.

• Algebraic Geometry
• Algebraic Topology
• Moduli Spaces
• +3 more

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +5 more

Dr. Sofia Tirabassi is a Full Professor in the Department of Mathematics at Stockholm University, where she has been affiliated since 2019. She leads the research group 'Algebra, Geometry, Topology, and Combinatorics' and directs projects funded by the Knut and Alice Wallenberg Foundation and the Swedish Research Council (VR). Her work bridges algebraic geometry, derived categories, and arithmetic geometry, with emphasis on positive characteristic and birational classification. She holds a Ph.D. from Università degli Studi Roma Tre (2012), an M.Sc. and B.Sc. from the University of Bologna. Previously, she held positions at the University of Bergen, University of Utah, and University of Warsaw as a Marie Curie Fellow. Her research explores two primary areas: (1) Derived categories in positive characteristic , investigating invariants of algebraic varieties, and (2) Birational characterization of semiabelian varieties , developing tools for effective classification. Her publications frequently address geometric structures like abelian surfaces, Enriques surfaces, and cohomological properties. Tirabassi's recent articles (2020-2024) demonstrate a focus on surfaces in positive characteristic, derived equivalences, and quasi-abelian varieties. Common themes include Fourier-Mukai transforms, Brauer groups, and log-canonical thresholds, reflecting interdisciplinary engagement with combinatorics and topology. Awards & Fellowships: Marie Curie Fellowship (European Commission) International Postdoctoral Scholarship (Knut and Alice Wallenberg Foundation) Young Research Talents Grant (Research Council of Norway) Grants & Projects: She currently leads 'Generic Vanishing and Characterization of Semiabelian Varieties' (VR-funded) and hosts a guest professorship for Rita Pardini. Past projects include 'Derived Categories in Positive Characteristic' (KAW Foundation) and 'The Arithmetic of Derived Categories' (Norway). Teaching: In 2024/2025, she instructs courses in Galois Theory, Combinatorics, Abstract Algebra, and Topology, and is developing a master's course in Algebraic Geometry.

• Algebraic Geometry
• Derived Categories
• Birational Geometry
• +4 more

Dr. Martin Herschend is a Senior Lecturer at the Department of Mathematics , Uppsala University. His research focuses on Algebra , Representation Theory , and Category Theory , with particular emphasis on quiver representations, cluster tilting theory, and homological algebra. Research Areas: Algebraic structures, quiver theory, cluster tilting, homological algebra, and categorification. Key Collaborations: Active collaborations with Sondre Kvamme, Yu Liu, Hiroyuki Minamoto, and other researchers in higher-dimensional representation theory. Recent Publications: Pioneering work on nZ-cluster tilting , Geigle-Lenzing complete intersections , and exangulated categories with applications to noncommutative geometry and singularity theory. Contact: martin.herschend@math.uu.se

• Algebra
• Representation Theory
• Category Theory
• +3 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

Torsten Lindström is a faculty member at Linnaeus University, affiliated with the Faculty of Technology and the Department of Mathematics . His research focuses on qualitative analysis of dynamical systems with applications to ecology and epidemiology , encompassing differential equations , discrete-time models , and stochastic processes . He is actively involved in the International Center for Mathematical Modeling (ICMM) and the Stochastic Analysis and Stochastic Processes research group. His research spans ecological modeling, predator-prey dynamics, and mathematical education. He has authored a textbook on Linear Algebra and contributes to Ordinary Differential Equations Dynamical Systems Teacher education in Geometry courses. Notably, he is the editor of a special volume on mathematical models in biology. His recent publications analyze stochastic disease spread , limit cycles , and stability theory , reflecting interdisciplinary work between mathematics, ecology, and education. Collaborations with institutions like the University of Oslo and SpringerLink highlight his international engagement. Teaching materials, including recorded lectures on Linear Algebra and Dynamical Systems, are publicly accessible.

• Dynamical Systems
• Ecological Modeling
• Epidemiology
• +3 more