جست‌وجوی استاد و پژوهشگر

Cyrille Arthoمشاهده پروفایل

دانشیار

• Software Engineering
• Formal Methods
• Cybersecurity
• +۵ مورد دیگر

Cyrille Artho is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, actively contributing to research in formal methods, software testing, and cybersecurity. His work spans model checking, smart contract security, and concurrent systems verification, with significant contributions to tools like Java Pathfinder and Modbat. PhD from ETH Zurich (2005) with dissertation on multi-threading fault detection Teaches Software Safety and Security, Software Engineering Fundamentals, and supervises degree projects His research focuses on developing formal techniques for safety-critical systems, particularly in blockchain security and distributed applications. Recent work emphasizes smart contract verification, anomaly detection in microservices, and trusted execution environments for secure cloud analytics. The 15 most recent publications reveal a strong trend toward blockchain security (6 articles), formal verification of distributed systems (5), and novel testing methodologies (4), with increasing integration of machine learning for vulnerability detection. As chair of the FTSCS workshop series and contributor to major conferences like ASE and ICST, Artho has significantly shaped the formal methods community. His leadership in organizing workshops demonstrates commitment to advancing safety-critical systems research. Principal investigator for C3.ai DTI Cyber Safety Cage for Networks project Develops Modbat framework for model-based API testing Active in Digital Futures research initiative at KTH

Matteo Magnaniمشاهده پروفایل

استاد

• Network Science
• Social Network Analysis
• Computational Social Science
• +۱۰ مورد دیگر

Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.

• Distributed Systems
• Machine Learning
• Big Data
• +۳ مورد دیگر

Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Neda Haj Hosseiniمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Medical Imaging
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Neda Haj Hosseini is a Senior Lecturer and Associate Professor in Biomedical Engineering at Linköping University's Department of Biomedical Engineering (IMT) . She contributes to teaching courses like TBMT56 - Medical Technology and TBME08 - Biomedical Modeling and Simulation , while leading research initiatives in AI-driven cancer diagnostics and biomedical optics. Research Focus: Development of AI methods for cancer diagnostics, optical coherence tomography (OCT) applications, and fluorescence spectroscopy in surgical guidance Affiliations: Center for Medical Image Science and Visualization (CMIV) , Analytic Imaging Diagnostic Arena (AIDA) , Swedish Medical Technology Association Recent Research Trends demonstrate expertise in applying deep learning to: Pediatric brain tumor classification using multimodal imaging Optical biopsy techniques for intraoperative decision support Automated biomarker quantification in histopathology Medical imaging data integrity and algorithm validation Scientific Awards include grants from: Joanna Cocozza Foundation (2022) Swedish Childhood Cancer Foundation (2024) Academic Leadership involves mentoring students in projects such as: "Multiple Instance Attention-based Learning for Brain Tumor Classification" "Vision Transformers for Multiclass Brain Tumor Tissue Classification" "Reaction-diffusion Models for Image-driven Tumor Simulation"

• Total Productive Maintenance
• Artificial Intelligence
• Multi-Criteria Decision Making
• +۳ مورد دیگر

Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)

• Visualization
• Computer Graphics
• Flow Visualization
• +۳ مورد دیگر

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

Malin Tvärånaمشاهده پروفایل

مدرس ارشد

• Civics and Citizenship Education
• Education Development Research
• Critical Digital Literacy
• +۲ مورد دیگر

Malin Tväråna is a Senior Lecturer at Stockholm University in the Department of Teaching and Learning . Her expertise lies in Social Science Education and Civics Education , with a focus on developing critical reasoning and digital literacy skills. Specializes in citizenship education and education development research Projects on visual literacy and critical digital literacy Collaborates with Uppsala University on subject-specific methods Malin employs Phenomenography , Variation Theory , and Activity Theory in her work. Her research includes: Designing teaching for critical analysis of fairness and justice Progression models in samhällsanalytiskt tänkande (social science analytical reasoning) Empirical studies across primary, middle, and upper secondary education She is actively involved in: Editorial board of Skola och Samhälle journal Board member of the Society for Educational Development Research

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +۸ مورد دیگر

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

• Digital Humanities
• Text Mining
• Artificial Intelligence in Education
• +۳ مورد دیگر

Dr. Ahmed Taiye Mohammed holds a Postdoctoral research fellowship at Linnaeus University's Department of Cultural Sciences, Faculty of Arts and Humanities in Växjö, Sweden. He earned his Master's and PhD from Northern University of Malaysia (UUM) with a thesis on text anomaly detection. His research focuses on Digital Humanities (DH), AI applications in education, and computational thinking for non-engineers. He teaches courses like Digital Humanities Research Methods, Programming for DH, and AI in Healthcare. Key research projects include InKuiS (innovative cultural entrepreneurship) and AI for ISP (supporting academic writing via ChatGPT). He collaborates with Linnaeus University Centre for Data Intensive Sciences (DISA) on e-health initiatives. Recent publications explore AI in K-12 education, automated library classification, and text mining for archaeology. His work spans AI ethics, DH methodologies, and interdisciplinary applications of computational tools. Teaching responsibilities include over 10 courses at undergraduate/master's levels, emphasizing practical skills in DH technologies. Active in developing AI-based educational tools like CHAT4ISP-AI and exploring generative AI for academic writing support. Research also involves social media ecosystems, data mining practices, and satellite image classification using SVM techniques.

• 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.

• Control Engineering
• Telecommunications
• Cloud Computing
• +۳ مورد دیگر

Johan Eker is a Professor at the Department of Automatic Control at Lund University and an Adjunct Professor at ELLIIT: the Linköping-Lund initiative on IT and mobile communication . He is also a member of the LTH Profile Area: AI and Digitalization and LU Profile Area: Natural and Artificial Cognition . His research focuses on control engineering, telecommunications, cloud computing, real-time systems, IoT, and anomaly detection. He actively contributes to UN Sustainable Development Goals through his work. He has received notable awards including the Best paper runner-up award at IEEE CloudNet 2023 , Best Paper Award at RTCSA 2004 , and Best Student Paper Award at RTCSA 1999 . Key projects include: AORTA: Advanced Offloading for Real-Time Applications (2023–2025) ICS: Industrial Cloud Sandbox (2019) AutoDC: Autonomous datacenter for long-term deployment (2018–2021) He has organized workshops such as the Real-Time Cloud Workshop and serves on the advisory board for Internet of Things and People (IoTaP) .

Maud Lanauمشاهده پروفایل

استادیار

• Industrial Ecology
• Socioeconomic Metabolism
• Circular Economy
• +۴ مورد دیگر

Maud Lanau is an Assistant Professor at Chalmers University of Technology's Department of Building Technology within the School of Architecture and Civil Engineering. Her research focuses on sustainable built environments, integrating industrial ecology, socioeconomic metabolism, and circular economy principles to reduce environmental impacts. She specializes in material stock and flow analysis, carbon accounting, and urban resource cadasters. Education: PhD in Environmental Engineering (2020), University of Southern Denmark MSc in Industrial Ecology (2014), Chalmers University of Technology Mastère in Housing Engineering (2011), University Paul Sabatier of Toulouse BSc in Mathematics, Physics, and Chemistry (2012), University Paul Sabatier of Toulouse Research Interests: Lanau’s work examines how built environment stocks can support circular economy transitions. She develops methods for material stock modeling, integrates data collection frameworks for construction practitioners, and explores urban morphology-material stock relationships. Her projects include the NUMMS initiative linking urban form to material stocks and the Future URCs project addressing circular logistics in construction. Collaborations & Teaching: She co-supervises PhD students on topics like construction plastics circularity and sustainable mass handling. Lanau teaches the master’s course 'Sustainability Analytics and Visualization,' emphasizing industrial ecology tools and visualization techniques. She actively participates in interdisciplinary projects like CREATE and the Swedish Circular Economy Colloquium.

Ida Bergvallمشاهده پروفایل

مدرس ارشد

• Mathematics Education
• Mathematical Linguistics
• Multimodal Analysis in Education
• +۳ مورد دیگر

Ida Bergvall is a Senior lecturer at the Department of Pedagogy, Didactics and Educational Sociology at Uppsala University. Her academic work focuses on the intersection of mathematics education, linguistic analysis, and multimodal communication in educational contexts. Dr. Bergvall's research primarily investigates mathematical subject language and its impact on diverse student populations. Her work examines: How linguistic features in mathematics tests affect different student groups The multimodal nature of mathematical communication (text, symbols, images) Information density in mathematical materials Differences between language use in mathematics and science The role of dynamic elements in digital teaching platforms Collaborative problem-solving approaches in mathematics education Her publication history reveals an evolving research trajectory from foundational linguistic analysis of mathematics education toward contemporary investigations of digital learning environments. Bergvall consistently applies social semiotic perspectives to understand how multiple modes of communication function together in mathematical education. Recent work demonstrates increasing focus on dynamic elements in digital teaching platforms and their influence on students' reading behavior and comprehension processes. While specific grant information isn't detailed in the provided materials, her research appears to focus on collaborative projects examining mathematical language, particularly through the lens of TIMSS (Trends in International Mathematics and Science Study) data analysis and contemporary digital learning environments.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +۶ مورد دیگر

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.