جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Science
• +۶ مورد دیگر

Fredrik Heintz is a professor at Linköping University's Department of Computer and Information Science within the Faculty of Science & Engineering. His research bridges artificial intelligence, education, and healthcare, focusing on AI literacy, synthetic data generation, and autonomous systems. Key affiliations: Linköping University (Faculty of Science & Engineering, Department of Computer and Information Science) Research Interests: Heintz's work spans multiple domains: Developing frameworks for AI literacy in K-12 education Creating fair synthetic healthcare data using GANs and bias-transforming techniques Advancing autonomous 3D exploration algorithms for dynamic environments Benchmarking tools for fairness, utility, and explainability in AI models Stream reasoning for real-time data analytics and knowledge extraction Evaluating ethical implications of AI in teacher education Scientific Contributions: His publications highlight collaborations with international researchers and significant grants from the Swedish Research Council, Knut and Alice Wallenberg Foundation, and VINNOVA. Notable projects include TransFusion for time-series generation, Bt-GAN for fair healthcare data, and DAEP for dynamic exploration planning. Funded by Wallenberg AI, Autonomous Systems and Software Program (WASP) ELLIIT Excellence Center at Linköping-Lund Mistra Geopolitics research program

Louis Ohlمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Unsupervised Learning
• Clustering Algorithms
• +۳ مورد دیگر

Louis Ohl is a Research Fellow and Postdoc at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on unsupervised learning methods, particularly clustering algorithms and PU learning, with applications to medical data (e.g., aortic stenosis phenogroups) and material science (e.g., MAX-phase to MXen structure changes). He holds a PhD from Côte d'Azur University (France) and Laval University (Canada), where he developed discriminative clustering techniques for cardiac disease analysis. Education: PhD in Computer Science from Côte d'Azur University and Laval University (France/Canada). Research interests include: Unsupervised and semi-supervised machine learning Clustering algorithm development Medical informatics applications in cardiology Material science computational modeling PU learning frameworks His recent work emphasizes bridging theoretical advancements in clustering with practical applications in healthcare and materials research. Collaborations include projects on aortic stenosis progression prediction and computational studies of structural phase transitions in materials. No scientific awards listed. Active in advising and research grants related to his current projects at STIMA. Associated with the STIMA division's international master's program in Statistics and Machine Learning.

• Program Verification
• Proof Engineering
• Formal Methods
• +۴ مورد دیگر

Karl Palmskog is a Lecturer at KTH Royal Institute of Technology in the Division of Theoretical Computer Science and the STEP research group. His work focuses on program verification and proof engineering, with particular emphasis on developing techniques and tools based on proof assistants for constructing functionally correct and secure software systems. Palmskog received his Ph.D. in Computer Science in 2014 from KTH, advised by Mads Dam, and his M.Sc. in Computer Science and Engineering from KTH in 2007. Prior to his current position, he was a postdoc at The University of Texas at Austin and University of Illinois at Urbana-Champaign. His research interests span programming languages, software engineering, and formal verification, with a particular focus on developing techniques and tools based on proof assistants. He is an avid user of the Coq proof assistant for both proving and programming, often complemented by OCaml, and also utilizes HOL4 and other ML family dialects. His work bridges theoretical foundations with practical applications, particularly in the domains of blockchain systems, distributed systems, and automotive software verification. Analysis of his recent publications reveals a strong focus on Coq-based verification, with significant contributions to proof engineering tools and methodologies. His work includes developing tools for regression proving, change impact analysis, mutation testing for Coq projects, and lemma name suggestion using deep learning. There's also a growing trend toward applying formal methods to real-world systems like blockchain protocols and automotive software. Palmskog has been involved in several research projects, including Coq-community Proof Engineering and Distributed Components. His past projects include Trustfull (SSF), Model-based Event Driven Scalable Programming for the Mobile Cloud (NSF), Highly Adaptable and Trustworthy Software (EU FP7), and 4WARD Future Internet (EU FP7). As an educator, Palmskog has served as examiner, course responsible, teacher, and assistant for various courses including Algorithms, Data Structures and Complexity; Degree Projects; Game Theory; Parallel and Distributed Computing; and Programming Paradigms. His work on Chip, a Coq formalization of change impact analysis, demonstrates his commitment to creating practical, certified tools that bridge formal methods with software engineering practice.

• Software Development
• Enterprise Application Integration
• Open Source Infrastructure
• +۵ مورد دیگر

Benneth Christiansson is an Assistant Professor at Karlstad University, focusing on software development paradigms, enterprise application integration, and open-source infrastructure. His research bridges the gap between emerging cloud-based tools (e.g., Software-as-a-Service) and traditional enterprise systems, emphasizing lean integration approaches. Research Interests: His work centers on component-based development, integration challenges in heterogeneous systems, and the adoption of open-source solutions to streamline enterprise processes. He explores frameworks for aligning software components with business processes, addressing interoperability and data standardization issues in public and private sectors. Publications: Over 15+ publications since 1995, with recent focus on lean integration strategies (2011-2012), open-source adoption in municipal systems (2007), and foundational component specification models (2003-2005). Key themes include COTS component acquisition, business-process-driven software specifications, and agile modeling techniques. Awards: No scientific awards explicitly listed in the text. Advising & Grants: No advisees or grants mentioned in the provided text. His contributions appear to focus on theoretical and applied research in software systems. Labs/Teams: No specific labs or collaborative teams described, though his work implies engagement with enterprise IT and software development communities.

Anne-Kathrin Petersمشاهده پروفایل

دانشیار

• Education Technology
• Sustainability Education
• Computing Education
• +۸ مورد دیگر

Professor Anne-Kathrin Peters is an Associate Professor in Technology Education at KTH Royal Institute of Technology's School of Industrial Engineering and Management (ITM), affiliated with the Department of Learning. She serves as Co-PI for the Digital Futures project addressing cognitive disabilities in education and co-leads the TREES research cluster. Her research focuses on sustainability in education, equity, and the intersection of social/environmental sustainability. Key roles include coordinating KTH's education for sustainable development initiatives and advising Norway's Excited Centre on IT education. She supervises two PhD students and teaches courses like 'Teaching and Learning in Higher Education.' Research Interests: Education for sustainable development Social and environmental sustainability intersections Norms/identities in computing education Emotions and care ethics in teaching Inclusive digitalization for cognitive disabilities Projects: Digitalisation and Educational Transformation project (Digital Futures) Anthropocene natural science education initiative Collegial networks on equality/sustainability in education at KTH Teaching: Leads core courses for educators, including pedagogical frameworks, gender theory, and challenge-driven education.

• Scientific Computing
• Computational Fluid Dynamics
• Finite Element Methods
• +۳ مورد دیگر

Johan Jansson is an Associate Professor in Scientific Computing at KTH Royal Institute of Technology and BCAM (Basque Center for Applied Mathematics). He leads research in predictive Direct FEM Simulation (DFS) for aerodynamics and multiphase flows, and co-founded Icarus Digital Math as CEO. His work includes the FEniCS open-source finite element software project and MOOC-HPFEM educational initiatives. He holds roles as Director of the Center for Digital Math and collaborates internationally in computational science. Research focuses on high-performance computing (HPC), fluid-structure interaction (FSI), biomedical modeling, and renewable energy systems. Notable contributions include adaptive FEM frameworks for turbulent flow, vocal fold simulations, and wave energy converter modeling. His work bridges academic research with industrial applications, leveraging FEniCS-HPC and Unicorn solvers. Key achievements include election to the IVA Royal Swedish Academy of Sciences 100-list and securing the Severo Ochoa Center of Excellence Award. He has pioneered open-source tools like SimTek and contributed to major projects like the Salter Sink and vocal production modeling. Teaching responsibilities include courses on database technology, computational fluid mechanics, and research methodology. He actively engages in large-scale simulation projects involving marine energy, cardiac ablation protocols, and aerodynamic optimization.

• Fusion Plasmas
• Tokamak Experiments
• Electromagnetic Field Theory
• +۴ مورد دیگر

Pär Strand is a Professor at Chalmers University of Technology, affiliated with the Department of Astronomy and Plasma Physics. His research focuses on transport in fusion plasmas , particularly through analysis of experiments at JET and development of simulation tools for ITER and other tokamak facilities. A key contributor to EU projects, he directs the Chalmers e-Science Centre, emphasizing data-driven methodologies and large-scale simulation technologies. Expertise: Fusion plasma dynamics, electromagnetic field theory, integrated modeling frameworks Projects: Code development for ITER/JET, FAIR data principles in fusion research, turbulence transport simulations Research Trends: Recent publications highlight advancements in: Tokamak power exhaust solutions (divertor shaping, neutral baffling) Machine learning applications for pedestal dynamics and disruption prediction High-order solvers for plasma transport equations Validation of D-T fusion power predictions against JET experiments

• Business Process Management
• Process Mining
• Object-Centric Process Mining
• +۶ مورد دیگر

Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.

• Cyber Situational Awareness
• Simulator Training
• Live Virtual Constructive
• +۳ مورد دیگر

Henrik Artman is a Professor at KTH Royal Institute of Technology, affiliated with the Media Technology and Interaction Design (MTM) school. His research focuses on Cyber Situational Awareness , Simulator Training , Live Virtual Constructive (LVC) systems, and intersections with Sustainability and Energy Supply . He serves as Deputy Head of School and teaches courses such as Advanced Individual Course in Human-Computer Interaction and Computer Game Design . Research Interests include: Human-Computer Interaction Cybersecurity and situational awareness LVC military training systems Sustainability in digital design Energy infrastructure resilience Key Contributions involve analyzing LVC training value for air combat, cyber threat response frameworks, and simulator mission design. His work bridges military training, cybersecurity, and interaction design, with a focus on real-world applications.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +۳ مورد دیگر

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

Luis Velez Quinteroمشاهده پروفایل

استادیار

• Data Science
• Extended Reality (XR)
• Machine Learning
• +۴ مورد دیگر

Luis Velez Quintero is an Assistant Professor at Stockholm University within the Department of Computer and Systems Sciences (DSV) . He is affiliated with the Data Science Research Group and the Stockholm Technology & Interaction Research (STIR) group, focusing on Human–Computer Interaction. His research spans adaptive immersive systems, affective computing, and physiological signal analysis in extended reality (XR) environments. Education & Background : Holds a PhD in Computer and Systems Sciences from Stockholm University (2023), MSc in Health Informatics from Karolinska Institutet (2019), and BSc in Electronics Engineering from the National University of Colombia (2015). He has led the startup PortalSense since 2018, developing VR solutions for real estate visualization. Research Interests : Combines data science and ML with XR technologies to create context-aware systems for healthcare, education, and professional training. Key themes include: Adaptive VR/AR systems using real-time physiological and behavioral signals Biosignal integration for personalized user experiences Immersive technologies for cognitive assessment and skill development Publications : Over 20 peer-reviewed articles, including work on affective databases (AVDOS-VR), biosignal frameworks (Excite-O-Meter), and XR applications in cybersecurity education. Recent efforts explore third-person locomotion in VR and early-stage Alzheimer’s detection via spatial navigation tasks. Awards & Grants : Wallenberg Foundation Grant (2023-2025): Analyzing non-verbal communication in psychotherapy Swedish Institute Scholarship (2017-2019): Fully funded Master’s and PhD studies Seed funding for PortalSense from Fondo Emprender SENA Colombia (2022-2023) Advising & Projects : Lead researcher in projects like AVDOS-VR and CS:NO . Co-designed the Excite-O-Meter open-source plugin for real-time physiological analysis in VR. Active in industry collaborations for scalable health interventions and immersive training systems. Labs & Teams : Collaborates with multidisciplinary teams at STIR and DSV, advancing human-centered AI and adaptive XR technologies.

Saad Mubeenمشاهده پروفایل

استاد

• Embedded Systems
• Real-Time Communication
• Predictable Software Development
• +۵ مورد دیگر

Saad Mubeen is a Full Professor of Computer Science at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. He holds a Master's in Electrical Engineering (Embedded Systems) and a PhD in Computer Science and Engineering from Mälardalen University (2014), with a Docent title (2018) focused on vehicular embedded systems. His research emphasizes predictable embedded systems, timing analysis for real-time communication, and component-based software design. Key areas include model-driven development for automotive systems, integration of TSN/5G networks, and fault-tolerant industrial architectures. He has led projects on end-to-end timing analysis in distributed systems, ROS 2 verification, and cognitive edge-cloud scheduling. Publications span 2021–2025, focusing on real-time systems, network protocols (TSN, AVB, 5G), and industrial automation. Notable work includes frameworks for TSN configuration, fault diagnosis tools using NETCONF, and scheduling algorithms for heterogeneous edge-cloud environments. His contributions address critical challenges in timing predictability, security, and resource optimization for cyber-physical systems. Education contributions include problem-based learning modules for vehicular software engineering. He is actively involved in bridging academia and industry through collaborative research on next-generation automotive and industrial systems.

Michael Minockمشاهده پروفایل

دانشیار

• Natural Language Interfaces
• Knowledge Representation
• Databases
• +۴ مورد دیگر

Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.

Bestoun S. Ahmed Al-Beywaneeمشاهده پروفایل

استاد

• Quality Assurance of Machine Learning Systems
• Software Testing
• Trustworthy AI Systems
• +۵ مورد دیگر

Bestoun S. Ahmed Al-Beywanee is a Professor in AI and Software Engineering at the Department of Mathematics and Computer Science, Karlstad University, Sweden. He joined the university as a Senior Lecturer in 2019, was promoted to Associate Professor in 2020, and to Professor in 2023. His roles include teaching advanced courses such as AI Engineering, Automated Software Engineering, and Software Testing Fundamentals. His research focuses on software quality assurance, trustworthy AI systems, and MLOps, with a strong emphasis on combinatorial testing, IoT systems, and applied optimization techniques. Education: BSc (Electrical and Electronic Engineering, University of Salahaddin-Erbil, 2004); MSc (University Putra Malaysia, 2009); PhD (Software Engineering, University Sains Malaysia, 2012). Postdoctoral research at the Swiss AI Lab IDSIA (2015) and positions at Salahaddin University and Czech Technical University further enriched his expertise. Research interests span Quality Assurance of machine learning systems, software testing methodologies, trustworthy AI, IoT system reliability, and optimization algorithms. He has pioneered frameworks like PatrIoT for IoT testing and contributed to MLOps robustness. His work integrates machine learning with anomaly detection, adaptive systems, and industrial applications. Recent articles highlight advancements in data-driven heat pump management, MLOps robustness, and edge-cloud AR/VR optimization. Collaborations include projects on digital twins, smart manufacturing, and industrial IoT. His contributions bridge theoretical research with practical industrial solutions, emphasizing system reliability and AI ethics.

Lars-Henrik Erikssonمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Logic in Computer Science
• Programming Language Theory
• +۷ مورد دیگر

Lars-Henrik Eriksson is a Senior Lecturer in the Department of Computer Science at Uppsala University, part of the Department of Information Technology. He holds a PhD and is recognized as an Excellent Teacher and educational mentor. He currently serves as the program director for the Master's program in Computer Science and has previously held leadership roles, including Head of the Department of Computer Science from 2004 to 2018. PhD in Computer Science Excellent Teacher Award Pedagogical Mentor at Uppsala University His research focuses on the applications of logic in computer science, particularly formal methods for software development. His work spans formal specification, verification, and synthesis, with strong emphasis on logic programming, interactive theorem proving, and logical frameworks. He has made significant contributions to the use of formal methods in safety-critical domains such as railway signaling. His current research includes modeling application domains within formal methods and formalizing concurrency theory using the Isabelle proof assistant. The recent publications reflect a sustained focus on modal logics for nominal transition systems, formal verification tools (e.g., GTO), and domain-specific applications of formal methods. These works demonstrate deep integration of theoretical logic and practical software engineering, especially in distributed and safety-critical systems. The recurring themes include concurrency, verification, and the use of domain models to enhance formal reasoning. Scientific Awards and Recognitions: Excellent Teacher Pedagogical Mentor Lars-Henrik Eriksson has advised numerous students through project supervision, thesis reviews, and course mentorship, though no formal list of advisees is provided. He has been involved in significant research management, including leading the department and directing a master’s program. He has also collaborated with industry, particularly in railway signaling, contributing to safety analysis and formal verification projects with Trafikverket and through companies he co-founded. He is a board member of Formal Methods Europe and served as program committee chair for the FME Symposium 2002 (part of FLoC’02). His work bridges academia and industry, especially in the application of formal methods to real-world engineering challenges.