جست‌وجوی استاد و پژوهشگر

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.

• Artificial Intelligence in Healthcare
• Precision Medicine
• Machine Learning Applications in Healthcare
• +۵ مورد دیگر

Magnus Boman is a Professor of AI and Health at the Department of Medicine, Solna, Karolinska Institutet (KI), where he leads the AI@KI initiative to support researchers in AI integration. He is affiliated with the Chronic Inflammatory Disease Epidemiology research group under Johan Askling. His research focuses on AI applications in precision medicine, multimodal prediction, ethical norms in AI systems, energy-efficient computing, and quantum sensor data interpretation. Research Interests: Artificial Intelligence in healthcare and precision medicine Multimodal data analysis for disease prediction and treatment Machine learning for clinical decision support systems Ethical and societal implications of AI Grants: Swedish Research Council: Improving breast cancer histology image classification (2024-2026) Scalable Federated Learning (2022-2025) Ai in sustainable cities (VINNOVA, 2019) Advising & Students: Supervised over 50 PhD and Master's students across KI, KTH, and Stockholm University, focusing on AI applications in healthcare, machine learning, and computational epidemiology. Notable projects include predictive modeling for mental health outcomes and variant filtering in genetic data. Labs & Teams: Leads AI@KI, fostering AI adoption in medical research. Collaborates with the Johan Askling group on epidemiology and chronic disease studies.

Panagiotis Papapetrouمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Time Series Analysis
• +۵ مورد دیگر

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

• Probability Theory
• Futurology
• Existential Risk
• +۴ مورد دیگر

Olle Häggström is a Professor of Mathematical Statistics at Chalmers University of Technology, specifically in the Department of Applied Mathematics and Statistics. His academic career spans several decades with a significant shift in research focus over time. Häggström's research interests have evolved from traditional probability theory to encompass broader future-oriented topics. Initially focused on mathematical statistics and probability theory, including percolation theory and stochastic processes, he has increasingly turned his attention to futurology, existential risk, and AI safety in recent years. His work demonstrates a unique interdisciplinary approach, bridging rigorous mathematical analysis with philosophical considerations about humanity's technological trajectory. The trends in Häggström's publications reveal a clear evolution from purely mathematical research toward interdisciplinary studies examining the societal implications of emerging technologies. His recent work focuses heavily on AI safety, existential risk assessment, and long-term futures thinking, while still maintaining connections to his mathematical foundations. This shift is evident in publications ranging from technical mathematical papers to broader philosophical discussions about technology's impact on civilization. Häggström has received research funding from notable sources including the FTX Foundation Future Fund for his project "Topics in the theory of xrisk and longtermism" (2022-2025), indicating recognition of the importance of his work in the existential risk community. His book "Here Be Dragons: Science, Technology and the Future of Humanity" (2016) represents a significant synthesis of his thinking on these topics. While specific details about his advising activities are not provided in the source material, his research projects suggest engagement with interdisciplinary teams working at the intersection of mathematics, computer science, and future studies. His work appears to influence both academic and policy discussions regarding technological risk and long-term planning.

Klas Markströmمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Hypergraphs
• +۳ مورد دیگر

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.

Robert Johanssonمشاهده پروفایل

دانشیار

• Discrete Mathematics
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Robert Johansson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University, and holds an affiliated position with the Faculty Office of Science and Technology. His research focuses on discrete mathematics and graph theory, with a strong emphasis on combinatorial structures and theoretical foundations. Research Focus His work explores critical areas in graph theory, including edge-decompositions, chromatic numbers, and path-factors. He has contributed to understanding extremal properties of graphs and combinatorial optimization problems in planar and r-partite graph systems. Publications The majority of his publications fall under discrete mathematics and theoretical computer science, with recurring themes in graph theory, combinatorial analysis, and network structures. His 2012 educational text on advanced mathematics further highlights his contributions to pedagogical frameworks. Labs & Teams Member of the Discrete Mathematics research group at Umeå University

Christoph Eggerمشاهده پروفایل

استادیار

• Cryptography
• Computational Complexity
• Statistical Privacy
• +۷ مورد دیگر

Christoph Egger is an Assistant Professor at Chalmers University of Technology in the Department of Computer Science and Engineering, where he works with the Security & Privacy Lab and the Crypto Team. Prior to this position, he was a Marie-Curie Fellow at Institut de Recherche en Informatique Fondamentale (IRIF) from fall 2022 to 2024, researching connections between cryptography and complexity theory. His educational background includes: PhD: "On Abstraction and Modularization in Protocol Analysis" Master's: "An implementation of global caching for the alternation-free coalgebraic μ-calculus" Bachelor's: "Analysing and attacking the I2P Network Database" Dr. Egger's research focuses on cryptography and its connections to computational complexity, statistical privacy, and formal methods. His work spans multiple areas including cryptographic foundations (random oracles, key agreement protocols), privacy-enhancing technologies (ring signatures, information flow techniques), and practical applications in genomic data security. He develops both theoretical frameworks and practical tools like CryptoZoo for cryptographic proof visualization, bridging the gap between theoretical cryptography and real-world security challenges. His recent publications demonstrate a strong focus on cryptographic foundations and privacy technologies, with significant contributions to ring signatures, key agreement protocols, and genomic data security. His work bridges theoretical cryptography with practical applications in blockchain, anonymous communications, and healthcare data management, showing consistent productivity across multiple high-impact venues in security and privacy. Dr. Egger has served on program committees for prestigious conferences including IEEE Computer Security Foundations Symposium (CSF), Proceedings on Privacy Enhancing Technologies (PETS), and Conference on Applied Cryptography and Network Security (ACNS). He currently advises PhD students Lucia Lavagnino at Chalmers and Kirthivaasan Puniamurthy at Aalto University. Previously, he advised Master's students including Julian Brost and Kirthivaasan Puniamurthy. His Marie-Curie Fellowship was cofunded by EU H2020 Marie Sklodowska-Curie Action and FSMP Comunity Service. Dr. Egger is an active member of the Security & Privacy Lab and Crypto Team at Chalmers. He is also a founding member of the FAUST CTF team and has been a Debian Developer for over a decade, contributing to various Free Software projects including the Linux kernel and Git version control system, demonstrating his commitment to both academic research and practical software security.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۲ مورد دیگر

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

• Control Systems
• Cyber-Physical Systems Security
• Power Systems Resilience
• +۳ مورد دیگر

Kamil Hassan is a Doctoral student and Researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. His work focuses on securing cyber-physical infrastructure through advanced control methodologies. His research spans Control Systems , Cyber-Physical Security , and Power Systems Resilience , with emphasis on mitigating attacks in time-critical networks. Key contributions include finite-time control barrier functions for power inverters and randomized detector tuning for attack impact reduction. His methodologies integrate hardware-in-the-loop validation with theoretical guarantees. Recent publications (2021–2025) reveal a trajectory toward resilient control architectures for energy systems, blending multiagent consensus theory with security-aware design. Work on power inverter networks dominates his output, addressing grid stability under cyber threats through novel barrier function frameworks and simulation-validated approaches. Hassan serves as course assistant for Cyber-Physical Security in Time-Critical Systems (EL2850) and operates within KTH's Decision and Control Systems division, contributing to hardware-in-the-loop testing environments for critical infrastructure protection.

Mohamed Faouzi Atigمشاهده پروفایل

استاد

• Model Checking
• Program Verification
• Concurrency
• +۷ مورد دیگر

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.

Tom Brittonمشاهده پروفایل

استاد

• Epidemic modeling
• Biostatistics
• Network analysis
• +۵ مورد دیگر

Tom Britton is a Professor in Mathematical Statistics (Holder of the Cramér chair) at the Department of Mathematics, Stockholm University. He leads the SU-Infection spread modeling research group and serves as chairman of the Stockholm Mathematics Center (SMC). His work primarily focuses on developing and analyzing mathematical/statistical models for infection spread and applying statistical methods to infection spread data. His research interests span epidemic modeling, biostatistics, network analysis, and mathematical statistics with applications toward genetics and molecular biology including phylogenetics. Britton's work encompasses applications of biostochastics and biostatistics, discrete random structures, and finance and insurance. He has made significant contributions to understanding disease transmission dynamics, particularly during the COVID-19 pandemic. Britton's recent publications demonstrate a strong emphasis on practical applications of mathematical models to public health challenges. His work examines contact tracing strategies, vaccine impact analysis, immunity waning dynamics, and optimal intervention strategies. There is a clear pattern of translating theoretical mathematical models into actionable public health insights, with particular focus on the interplay between population heterogeneity, immunity levels, and intervention effectiveness. As an academic leader, Britton serves as academic editor for PLoS Comp Biol and associate editor for Journal of Mathematical Biology and JRSS A (Statistics in Society). He is also a board member of the European Society for Mathematical and Theoretical Biology (ESMTB). Britton actively mentors the next generation of researchers, currently supervising PhD student Fanny Bergström and Post Doc Martina Favero. Throughout his career, he has successfully guided 13 PhD students and multiple postdoctoral researchers. His current research is supported by significant grants from the Swedish Research Council (3.6 MSek, PI 2021-2024) and the KA Wallenberg Foundation (0.7 MSek, PI 2023-2025). The SU-Infection spread modeling research group, which Britton leads, develops and analyzes mathematical/statistical models for the spread of infection and applies statistical methods to adapt these models to infection spread data. This work has been particularly relevant during recent pandemics and continues to inform public health decision-making.

• Randomized algorithms
• Machine learning
• Big Data
• +۳ مورد دیگر

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.

Johan Håstadمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity
• Circuit Complexity
• +۳ مورد دیگر

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