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Alejandro RussoView profile

Professor

• Computer Science
• Information Security
• Functional Programming
• +3 more

Alejandro Russo is a Professor at Chalmers University of Technology , specializing in Information Flow Control (IFC) , Secure Programming Languages , and Functional Programming . His research bridges theoretical foundations and practical implementations, focusing on mitigating timing channels , covert channels , and data leakage in concurrent systems. Developed novel frameworks for Differential Privacy with provable accuracy bounds Pioneered COWL integration for browser security and instruction-based scheduling to prevent cache timing attacks Led major projects like HIPSTER (hybrid static/dynamic IFC) and AppFlow (practical IFC deployment) His publications reveal expertise in security libraries for Haskell and Python , with a focus on faceted execution , label manipulation , and mechanized security proofs . Students under his supervision have explored topics ranging from secure eDSLs to privacy-preserving compilation techniques . Scientific awards : Google Research Award (2011) for Python taint analysis Advising and grants : Principal Investigator for VR , STINT , and Google Research Award Supervised 12+ PhD and Master’s students in security and functional programming research

Klas MarkströmView profile

Professor

• Combinatorics
• Graph Theory
• Hypergraphs
• +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.

Leif NilssonView profile

Associate Professor

• Statistical Learning
• Defect Detection
• Occupational Health
• +5 more

Leif Nilsson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on statistical learning methods for industrial quality control and occupational health risk assessment. Statistical learning for defect detection Exposure modeling for occupational hazards Biostatistical analysis of health outcomes Recent work applies probabilistic classifiers and spline smoothers to automate surface finish inspection. He also investigates exposure variability in hand-arm vibration and its health impacts through epidemiological studies. Nilsson collaborates with clinical teams on stress recovery interventions and contributes to methodological developments in biological monitoring. His statistical expertise spans Bayesian modeling, resampling techniques, and spatio-temporal data analysis.

Anna LukinaView profile

Assistant Professor

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +4 more

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

Timothy BehrensView profile

Professor

• Computational Neuroscience
• Diffusion MRI
• Brain Connectivity
• +1 more

Professor Timothy Behrens of the University of Oxford is a leading computational neuroscientist specializing in diffusion MRI and brain connectivity mapping. He was awarded the 2014 Lennart Nilsson Prize for developing probabilistic tractography methods that enable non-invasive, high-resolution imaging of human brain architecture. His research bridges computational modeling with neuroimaging to understand neural computations underlying behavior. Education: Information Engineering (undergraduate), DPhil in diffusion imaging methods Research Focus: Brain connection organization, frontal cortical mechanisms, computational descriptions of behavior Key Grants: NIH Human Connectome Project (anatomical connectivity section leader), Wellcome Trust, MRC UK, James S McDonnell Foundation Scientific Contributions: His work combines diffusion MRI with functional and neurochemical data to reveal how brain regions communicate. The probabilistic tractography methods he pioneered allow precise quantification of connectivity patterns in vivo. Awards: Lennart Nilsson Award (2014) - Recognizing innovative diffusion MRI techniques

• Legal Evidence
• Cognitive Bias in Legal Decision Making
• Bayesian Evidence Models
• +3 more

Christian Dahlman is a Professor in the Department of Law at Lund University, serving as Research Coordinator for the Law, Evidence and Cognition (LEVIC) research group. His work focuses on legal evidence, cognitive biases in legal decision-making, and Bayesian models of evidence evaluation. He contributes to UN Sustainable Development Goals related to justice and governance. Dahlman is actively involved in interdisciplinary research at the intersection of law, psychology, and artificial intelligence. Research interests include probabilistic reasoning in legal contexts, cognitive biases affecting judicial fact-finding, and the application of Bayesian networks to forensic evidence analysis. His work critiques common legal fallacies and proposes methods to debias legal decision-making processes. Recent activities include organizing the NORTH SEA GROUP legal evidence seminars and delivering invited lectures on topics like 'Against Plausibility' (2024). He supervises doctoral research in areas such as decisional privacy and legal cognition. Dahlman's projects address preventing miscarriages of justice and improving evidence evaluation frameworks. He leads the long-running Law, Evidence and Cognition research initiative (2010–present) and has coordinated international collaborations across Europe. Dahlman's contributions span legal education, historical legal case analyses, and policy recommendations for judicial reform.

Zhenxia LiuView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Large Deviations
• +3 more

Zhenxia Liu is an Associate Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). Her work contributes to theoretical and applied probability, with a focus on stochastic processes and statistical modeling. Her research interests lie at the intersection of mathematical statistics and probability theory. Key areas include large deviations , longest runs in Markov chains , and Monte Carlo methods . These topics are central to understanding rare events, sequential dependencies, and numerical estimation techniques in complex systems. The recent publications demonstrate a consistent focus on probabilistic analysis of dependent structures, particularly through Markov models. Her work combines theoretical rigor with applications in computational statistics, showing trends toward improving bounds and simulation efficiency in stochastic modeling. Mathematical Statistics Probability Theory Large Deviations Markov Chains Monte Carlo Methods Computational Mathematics Zhenxia Liu has actively contributed to high-quality journals such as Statistics and Probability Letters , Results in Applied Mathematics , and Probability and Mathematical Statistics . While no formal advising or grant information is available in the provided text, her collaborative publications suggest engagement in research networks within applied mathematics. She is part of the research environment in Applied Mathematics at Linköping University, which focuses on computational mathematics, optimization, and mathematical modeling across science and engineering disciplines.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

Martin MagnussonView profile

Professor

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +5 more

Martin Magnusson is a Professor at the Department of Natural Sciences and Technology, Örebro University, leading the Center for Applied Autonomous Sensor Systems (AASS) and the Robot Navigation and Perception Lab . His research focuses on robotics and artificial intelligence , particularly 3D mapping, localization, radar-based navigation, and human-robot interaction . Email: martin.magnusson@oru.se Phone: +46 19 303870 Location: Room T1222 His work addresses fundamental challenges in achieving robust autonomy through innovations like the 3D Normal Distributions Transform (3D-NDT) and methods for scan registration in dynamic environments. Recent research extends to radar-based navigation and heterogeneous map data integration , with ethical implications regarding military applications of autonomous systems. Key research themes include: Autonomous Perception: Radar and lidar sensor fusion for localization Dynamic Mapping: Flow-aware and quality-assessed environmental models Human-Aware Robotics: Predictive modeling for safe shared-space navigation Professor Magnusson teaches Computer Graphics , connecting academic principles (e.g., ray tracing, light scattering) to applied research in radar simulation models and neural rendering . His research projects span DARKO (agile production robots), NiCE (changing environment navigation), and Radarize (underground autonomous vehicles).

Mariela MorveliView profile

Researcher

• Artificial Intelligence
• Computer Science
• Argumentation
• +6 more

Mariela Morveli is a postdoctoral researcher in Artificial Intelligence at Umeå University's Department of Computing Science. She works within the Formal Methods for Trustworthy Hybrid Intelligence research group, focusing on argumentation frameworks, intelligent agents, and explainable AI. Education: Ph.D., Federal University of Technology of Parana (Brazil) M.Sc., Federal University of Rio de Janeiro (Brazil) B.Sc., National University of San Agustin of Arequipa (Peru) Her research spans argumentation theory, multi-agent systems, and explainable AI, with emphasis on goal reasoning, activity reasoning, and handling uncertainty through formal methods. She has developed frameworks like QuAD-V for transparent task delegation and explored probabilistic models for argumentation systems. Recent work trends show strong focus on contrastive explanations , gradual semantics , and hybrid reasoning mechanisms that integrate symbolic and probabilistic approaches. Her publications address challenges in human-agent interaction , trust modeling , and conflict resolution in dynamic environments. She contributes to the field through rule-based argumentation systems , rhetorical argument strength calculations , and neuro-symbolic architectures like SATyrus for constraint processing.

Thomas SchönView profile

Professor

• Machine Learning
• Probabilistic Modeling
• Signal Processing
• +7 more

Thomas Schön serves as the Beijer Professor of Artificial Intelligence at Uppsala University's Department of Information Technology within the Faculty of Science and Technology. His research focuses on developing probabilistic models and algorithms for extracting knowledge from data, with particular emphasis on dynamical systems. His research interests span multiple disciplines at the intersection of Machine Learning and statistics, signal processing, automatic control, and computer vision . He takes a systematic approach to representing and manipulating uncertainty through probability theory. His work encompasses both basic and applied research, with strong collaborations with industry partners including ABB Crane Systems, Autoliv, Saab, Sectra, and Xsens Technologies. Schön's research output demonstrates significant contributions to probabilistic modeling of dynamical systems , with particular expertise in sequential Monte Carlo methods (particle filters), Markov chain Monte Carlo, Gaussian processes, and deep learning. His recent work includes advancements in incorporating background knowledge into machine learning models and developing flexible probabilistic frameworks. Schön actively supervises numerous PhD students working on diverse topics including Bayesian nonparametric models, deep learning applications, uncertainty-aware systems, and probabilistic computer vision. His research is funded by The Swedish Research Council (VR), The Swedish Foundation for Strategic Research (SSF), and Vinnova.

Mohamed Faouzi AtigView profile

Professor

• Model Checking
• Program Verification
• Concurrency
• +7 more

Mohamed Faouzi Atig is a Professor in Computer Systems at the Department of Information Technology, Uppsala University. His career spans roles as Senior Lecturer (2018-2021), Associate Senior Lecturer (2014-2018), and Researcher (2012-2018) at the same institution. He obtained his Doctoral Degree in Computer Science from the University of Paris Diderot-Paris 7 (2010) and a Master in Engineering from Tunisia Polytechnic School (2005). Current Role: Professor in Computer Systems Institution: Uppsala University Research Focus: Model checking, verification of infinite-state systems, weak memory models, automata theory, string constraints, concurrent program analysis His research explores formal methods for concurrent programs, weak memory models (TSO/PSO/POWER), automata theory for verification, and SMT solvers for string constraints. Recent work integrates graph neural networks with word equation solving and advances stateless model checking techniques. Key article trends include Weak Memory Model Verification (TSO, PSO, POWER) Stateless Model Checking Algorithms String Constraint Solvers (TRAU, Norn) Timed Automata and Multi-Pushdown Systems Mohamed Faouzi Atig has led collaborations on fence insertion procedures, timed pushdown automata, and database-driven system verification. His contributions are recognized through publications in top-tier conferences and journals.

• Formal Verification
• Model Checking
• Concurrent Systems
• +6 more

Parosh Abdulla is a Professor in the Division of Computer Systems within the Department of Information Technology at Uppsala University, Sweden. His research focuses on formal methods and program verification with emphasis on concurrent systems, memory models, and verification techniques. With an extensive publication record spanning over three decades, he has established himself as a leading researcher in his field. His primary research interests include formal verification, model checking, concurrent systems analysis, memory models (particularly TSO and PSO), string constraints, timed automata, parameterized verification, and program analysis. His work addresses fundamental challenges in verifying complex software systems, especially those involving concurrency and weak memory models. In recent years, he has expanded his research to include quantum systems verification and the application of machine learning techniques to formal methods. Analysis of his recent publications (2023-2025) reveals a continued focus on advancing verification techniques for concurrent systems, with growing interest in quantum computing verification and the integration of graph neural networks with traditional formal methods. His work demonstrates consistent innovation in reducing the state space explosion problem while maintaining verification completeness. Professor Abdulla has established significant collaborations with researchers including Mohamed Faouzi Atig, Bengt Jonsson, and others, resulting in numerous high-impact publications in top-tier venues. His research has practical implications for developing reliable concurrent software systems and advancing formal verification methodologies. While specific details about his advising activities and grants aren't explicitly mentioned in the available information, his extensive publication record suggests active supervision of graduate students and involvement in multiple research projects. His work likely contributes to formal methods research groups at Uppsala University, focusing on verification tools and theoretical foundations of program analysis.

• Theorem proving
• Decision procedures
• SAT/SMT solving
• +8 more

Philipp Rümmer is a Professor of Theoretical Computer Science at the University of Regensburg and a Senior Lecturer/Associate Professor at the Department of Information Technology , Uppsala University. His career spans over two decades of academic and industrial collaboration in formal verification, SMT solvers, and program analysis. Professor (2022–present), Faculty of Informatics and Data Science, University of Regensburg Senior Lecturer/Associate Professor (2018–present), Department of Information Technology, Uppsala University His research focuses on formal verification of software and embedded systems, SMT solvers (e.g., Princess, Norn), model checking (Eldarica), and integrating machine learning with verification techniques. He has pioneered tools like JayHorn for Java verification and Sloth for string constraint solving. Recent publications emphasize probabilistic verification of parameterized systems, timing analysis in embedded updates, heap invariants via Horn clauses, and graph neural networks in solving word equations. These works reflect his commitment to advancing formal methods through interdisciplinary approaches. Awards include: Oscarspris (2013), Uppsala University SAP Award for top Computer Science graduate (2005) Bosch Telecom Award for natural science excellence (1998) He has secured significant grants from the Swedish Research Council , Knut and Alice Wallenberg Foundation , and Microsoft Research , supporting projects like UPDATE (2020–2024) and WebSec (2018–2023). His leadership in tools such as Princess and Eldarica underscores his influence in automated reasoning and verification.

Istvan TomonView profile

Associate Professor

• Discrete Mathematics
• Extremal and Probabilistic Combinatorics
• Geometry

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.