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Susanna de Rezende is an Assistant Professor in the Department of Computer Science at Lund University (LTH). She is affiliated with the ELLIIT initiative on IT and mobile communication, the LTH Profile Area: AI and Digitalization, and the MIAO group collaborating with the University of Copenhagen. Her research focuses on computational complexity, proof complexity, circuit complexity, and communication complexity. She holds a PhD from KTH Royal Institute of Technology (2019) and a Master's from the University of São Paulo (2014). Her research explores connections between proof systems, circuit lower bounds, and communication complexity, with recent contributions to lifting theorems, automatability, and average-case hardness. She has received awards including the Stockholm Mathematics Centre Prize and Wallenberg Academy Fellow status. Current projects include funded PhD positions in theoretical computer science and editorial work for ZML: Zeitschrift für Mathematische Logik und Grundlagen der Mathematik. Key articles address proof complexity trade-offs, clique hardness in Sherali-Adams, and graph coloring challenges. Collaborations span institutions like the Czech Academy of Sciences and the Simons Institute. She advises multiple PhD students and collaborates on foundational research in complexity theory, supported by WASP, ELLIIT, and VR grants.

• Computational Complexity
• Proof Complexity
• Circuit Complexity
• +6 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

Devdatt Dubhashi is a Professor at the Data Science and AI 3 department at Chalmers University of Technology. His research spans multiple domains within computer science and data science, with a focus on theoretical foundations and practical applications of algorithms and machine learning. Professor Dubhashi's primary research interests include the design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. His work demonstrates a strong interdisciplinary approach, connecting theoretical computer science with practical applications in diverse fields such as quantum computing, transportation modeling, genomics, and social sciences. His research often bridges the gap between theoretical foundations and real-world implementations, as evidenced by his numerous collaborations across different domains. Dubhashi's recent publications reveal a consistent research trajectory focusing on algorithmic foundations of machine learning, with increasing emphasis on interdisciplinary applications. His work shows significant contributions to bandit algorithms, kernel methods, graph-based learning, and the theoretical understanding of deep learning models. There's also a growing focus on societal implications of AI, as seen in his publications addressing responsible AI development and policy considerations. Among his notable scientific contributions are publications in prestigious venues including ACM, IEEE, and Nature journals, covering topics from fundamental algorithm design to applications in computational biology and social sciences. His work has been cited extensively across multiple disciplines, reflecting its broad impact. Professor Dubhashi actively supervises PhD students and collaborates with researchers across Chalmers and internationally. His research group appears to focus on the intersection of theoretical computer science and practical machine learning applications, with projects spanning quantum computing, transportation modeling, and biological applications.

• Randomized algorithms
• Machine learning
• Big Data
• +3 more

Istvan Tomon is an Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University in Sweden. His research focuses on discrete mathematics, extremal and probabilistic combinatorics, and geometry. He leads research in combinatorial structures and graph theory, with recent work exploring hereditary families, symmetric chain decompositions, and Zarankiewicz problems. His publications demonstrate broad expertise in combinatorial optimization, hypergraph theory, and geometric combinatorics. Recent articles show consistent focus on extremal problems in set systems, matrix combinatorics, and incidence geometry.

• Discrete Mathematics
• Extremal and Probabilistic Combinatorics
• Geometry

Johan Håstad is a Professor at the Department of Mathematics, School of Engineering Sciences, KTH Royal Institute of Technology. His research focuses on theoretical computer science, particularly algorithms, computational complexity, approximation algorithms, and cryptography. He is funded by the Knut and Alice Wallenberg Foundation through project grants and the Wallenberg AI, Autonomous Systems and Software Program (WASP). External Funding: Knut and Alice Wallenberg Foundation, WASP Editorial Roles: Theory of Computing, TheoretiCS Professional Activities: Cyber Defense Center, Royal Swedish Academy of Sciences (former chairman) Research Interests : • Algorithms and Complexity • Circuit Complexity • Approximation Algorithms • Quantum Computing and Cryptography Scientific Awards : Knut and Alice Wallenberg Foundation Support Labs and Teams : • Involved in KTH's Cyber Defense Center • Editor for key journals in theoretical computer science

• Theoretical Computer Science
• Computational Complexity
• Circuit Complexity
• +3 more

Anders Johansson is a Lecturer at Gävle University, specializing in advanced mathematical research. His work focuses on dynamical systems, ergodic theory, and probability theory with applications in statistical mechanics and fractal geometry. His research explores topics such as transfer operators, g-measures, and phase transitions in complex systems. Key research interests include the mathematical analysis of Doeblin measures, Ising models, and Kusuoka measures. He has contributed to understanding the ergodic properties of non-uniformly hyperbolic systems and the construction of transport networks through stochastic processes. Recent work (2025) addresses continuous eigenfunctions of transfer operators in Dyson models and uniqueness criteria for Doeblin measures. Johansson’s publications span prestigious journals like Mathematische Zeitschrift and Ergodic Theory and Dynamical Systems. His earlier work (2003–2019) includes foundational studies on g-measures, graph theory, and combinatorial designs. His research often bridges pure mathematics with applications in theoretical biology and statistical physics. He has collaborated with notable researchers such as Anders Öberg and Mark Pollicott, producing influential papers on topics ranging from multifractal analysis to animal group size distributions. His work demonstrates a deep engagement with both theoretical rigor and interdisciplinary applications.

• Mathematics
• Dynamical Systems
• Ergodic Theory
• +4 more

Carl Johan Casselgren is a Professor and Head of the Division of Algebra, Geometry and Discrete Mathematics (ALGD) at the Department of Mathematics, Linköping University. He is actively engaged in research and teaching in discrete mathematics and is a core member of the graph theory research group at the university. Institution: Linköping University Department: Department of Mathematics (MAI) Division: Algebra, Geometry and Discrete Mathematics (ALGD) Role: Professor and Head of Division His research focuses on discrete mathematics, particularly graph theory, with emphasis on graph coloring, structural properties of graphs, and combinatorial optimization. He investigates problems such as edge colorings, list colorings, and the coloring of graph products, contributing to both theoretical developments and algorithmic aspects. Recent publications (2024–2025) reveal a consistent focus on edge coloring problems, including near-interval colorings, extensions of partial colorings, and the coloring of Cartesian products and hypergraphs. These works demonstrate a deep engagement with combinatorial structures and theoretical graph problems, often involving probabilistic and structural methods. While no specific scientific awards are mentioned in the provided texts, his sustained publication record in high-quality journals such as Discrete Applied Mathematics , The Electronic Journal of Combinatorics , and Discussiones Mathematicae. Graph Theory underscores his active and respected role in the mathematical community. He collaborates with researchers from Sweden and abroad, including Mesfin Masre, Fikre B. Petros, and Petros A. Petrosyan. Although no formal advisees are listed, his leadership role and publication collaborations suggest involvement in mentoring students and junior researchers. He is part of a vibrant research division with around twenty members, contributing to a dynamic academic environment. The ALGD division conducts research in algebra, geometry, topology, dynamical systems, combinatorics, and graph theory. The graph theory group specifically focuses on classical problems with an emphasis on graph coloring and Hamiltonian graph theory, aligning closely with Casselgren’s research interests.

• Discrete Mathematics
• Graph Theory
• Graph Coloring
• +4 more

Malin Palö Forsström is a Visiting Lecturer at Gothenburg University and lectures at Chalmers University of Technology. She previously held a postdoctoral position at KTH Royal Institute of Technology, focusing on probability theory under supervision of Fredrik Viklund and Jonatan Lenells. Her PhD, completed in 2019, was at Chalmers University of Technology under Jeffrey E. Steif. She has also taught courses at multiple institutions, including Chalmers, KTH, and Gothenburg University. Malin Palö Forsström earned her PhD in 2019 from Chalmers University of Technology, supervised by Jeffrey E. Steif. Prior to that, she pursued academic training leading to her doctoral studies. Her primary research interests lie in discrete probability theory, particularly applying probabilistic methods to study lattice gauge theories and related models. She also investigates properties of Boolean functions, including noise sensitivity, volatility, and total influence. Her work bridges theoretical probability with applications in mathematical physics and statistical mechanics. Her recent publications focus on lattice gauge theories, exploring Wilson loops and Higgs models, as well as the analysis of Boolean functions' sensitivity properties. Her work often combines probabilistic techniques with insights from mathematical physics, contributing to both theoretical frameworks and applied models in statistical mechanics. Scientific Awards and Grants: 2021: Ruth och Nils-Erik Stenbäcks stiftelse 2019: Kungliga vetenskapsakademin, Stiftelsen G S Magnusons fond 2015: Stiftelsen Olle Engkvist byggmästare 2014: SveFuM Malin has received several grants supporting her research, including notable awards from the Ruth och Nils-Erik Stenbäcks Foundation and the Swedish Research Council. She has not listed specific advising roles or grants beyond those mentioned. Her work has been presented at numerous events, including the Nordic Congress of Mathematicians, the Mittag-Leffler Institute, and international conferences like ICMP 2021.

• Discrete Probability Theory
• Lattice Gauge Theories
• Mathematical Physics
• +3 more

Daniel Ahlberg is an Associate Professor in the Department of Mathematics at Stockholm University. His research utilizes probability theory to study spatial random processes including percolation models, random growth systems, branching processes, and combinatorial optimization problems. Current investigations focus on phase transitions in percolation, noise sensitivity of random systems, and geometric properties of random metrics. Research has established fundamental results on sharpness of phase transitions in continuum percolation, scaling limits of Boolean functions, and asymptotic behavior of first-passage percolation models. Recent work examines chaos phenomena in random growth models and statistical properties of geodesics in disordered media. Funded by multiple Swedish Research Council grants including a current research project grant (2022-2025). Recipient of the Ruth and Nils-Erik Stenbäck Foundation stipend (2022). Supervises doctoral students in probability theory and stochastic processes. Developed educational materials on probability fundamentals and Markov chain mixing. Teaches courses in probability theory, stochastic processes, and discrete mathematics.

• Probability Theory
• Spatial Stochastic Processes
• Percolation Theory
• +3 more

Per Austrin is a Lecturer at the Royal Institute of Technology (KTH) in the Department of Theoretical Computer Science. He has been actively involved in teaching courses such as Algorithms and Complexity (DD2352) , Advanced Algorithms (DD2440) , and Problem Solving and Programming Under Pressure (DD2458) , serving as examiner and course coordinator for several programs. His research focuses on the foundations of computational complexity, approximation algorithms, and cryptographic barriers. Key areas include hardness of approximation for constraint satisfaction problems (CSPs), combinatorial optimization, and theoretical limits in cryptography and coding theory. His work often intersects with probabilistic methods and quantum computing challenges. Recent publications highlight his contributions to understanding lower bounds in optimization problems, inapproximability thresholds, and complexity in structured combinatorial problems. Detailed information about his work can be found on his personal website: Per Austrin's Website .

• Theoretical Computer Science
• Computational Complexity
• Approximation Algorithms
• +6 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

Liam Solus is an Assistant Professor of Mathematics at the Department of Mathematics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the ALGEBRA COMBINATORICS & TOPOLOGY division and the Digital Futures Faculty, focusing on research in combinatorics, discrete mathematics, algebraic statistics, and their applications in artificial intelligence and causal inference. His work is supported by grants from WASP (Wallenberg AI, Autonomous Systems, and Software Program), the Swedish Research Council, and the Göran Gustafsson Foundation. Education: PhD in Mathematics (University of Kentucky, 2015), postdoctoral fellowships at MIT, IST Austria, and the Max Planck Institute. Current research projects include causal AI for mobility behavior analysis (cAIMBER), combinatorial and geometric approaches to statistical models, and algebraic methods in machine learning. Research Interests: Algebraic and geometric combinatorics, lattice polytopes, causal inference, graphical models, algebraic statistics. Key contributions include studies on Ehrhart theory, Bayesian networks, and combinatorial sequences like Eulerian polynomials and Fibonacci numbers in causal structures. Awards: Large Prize for Young Researchers (Göran Gustafsson Foundation, 2023). Supervision includes PhD students Danai Deligeorgaki and former student Petter Restadh, along with postdocs in algebraic statistics and combinatorics. Labs/Teams: Co-PI of cAIMBER, member of Digital Futures, organizer of international workshops (e.g., AlCoVE 2024 in VR). Teaching includes courses on statistical learning, algebraic statistics, and probabilistic graphical models.

• Combinatorics
• Algebraic and Geometric Combinatorics
• Discrete Geometry
• +5 more

Jens Lundström is a Senior Lecturer at the School of Information Technology, Halmstad University. His research and teaching focus on Machine Learning and Artificial Intelligence, particularly their applications in healthcare, patient experience, and quality of life improvement. Research Area Leader for AI in CAISR Health Coordinator for Halmstad Professionals network (AI, data analytics, cybersecurity, service design) His research integrates applied and theoretical Machine Learning with healthcare domains, covering synthetic data generation, smart home systems, and data privacy. Recent publications highlight his contributions to Explainable AI, Generative Adversarial Networks, and robustness in Deep Learning for health and traffic data. No specific scientific awards or students are mentioned in the provided texts.

• Machine Learning
• Artificial Intelligence
• Health Informatics
• +3 more

Victor Lagerkvist is an Associate Professor at the Department of Computer Science (IDA) , Linköping University, Sweden. He is affiliated with the Theoretical Computer Science Laboratory (TCSLAB) and the Artificial Intelligence and Integrated Computing Systems (AIICS) division. His research focuses on the algebraic method for analyzing computational complexity , particularly in constraint satisfaction problems (CSPs), SAT, and graph homomorphism problems. PhD in Computer Science (2016, Linköping University) Habilitation (2020, Linköping University) His recent work investigates fine-grained complexity , twin-width , and universal algebra to improve algorithms for NP-hard problems. Publications span topics like propositional abduction, Allen's interval algebra, and semiring-based dynamic programming. His scientific awards include the Swedish Research Council Starting Grant (2020) and the 2017 Young Researcher Prize from the Ruth and Nils-Erik Stenbäck Foundation. He supervises PhD students such as Leif Eriksson and serves as a secondary supervisor for others at Linköping University and Université de Lorraine.

• Computational Complexity
• Constraint Satisfaction Problems (CSP)
• Theoretical Computer Science
• +3 more