جست‌وجوی استاد و پژوهشگر

• Systems Security
• Applied Cryptography
• Web Security
• +۳ مورد دیگر

Pablo Picazo-Sanchez is an Associate Senior Lecturer at the School of Information Technology at Halmstad University. His research focuses on systems security and applied cryptography, with emphasis on areas like Web Security, Browser Extensions, Biometrics, and RFID Security. He has published extensively in top-tier conferences and journals since 2023. His recent work includes developing frameworks for detecting fake browser extension reviews (FakeX), analyzing parliamentary debates using computational linguistics (Policy Parsing Panthers), and enhancing password security via client-side stretching (Clipaha). He also contributes to advancing cryptographic protocols (IBE.js, Attribute-based encryption with enforceable obligations) and educational technology (Closing the Gap). His research trends highlight a blend of cybersecurity innovation and interdisciplinary applications in AI ethics, language processing, and automated systems.

• Digital Automation
• Work Environment
• Education Technology
• +۳ مورد دیگر

Maria Åkesson is a Professor at Halmstad University's School of Information Technology, specializing in digital innovation and learning technologies. She co-leads the LeaDS research program on learning in digitized societies and leads a research project on digital automation and work environments funded by Afa Försäkring. As a 40% guest professor at Umeå University, her work bridges academia and industry. Her research focuses on digital automation's societal implications, work environment challenges, and educational curricula for the digital era. Key projects include designing master's programs in Digital Service Innovation and studying participatory innovation processes in healthcare and manufacturing sectors. Publications emphasize digital service architecture, remote diagnostics, and ethical considerations in health tech. She collaborates with two PhD students and senior researchers, exploring topics like robotic process automation in public administration and value network transformations. Her work integrates design science, organizational studies, and human-centered approaches. Ms. Åkesson's studies highlight tensions between technological vision and practical implementation, particularly in legacy industries. She advocates for balanced innovation strategies that address both generative potential and control challenges in digitized systems.

Emma Ahlqvistمشاهده پروفایل

دانشیار

• Genetics
• Diabetes Research
• Endocrinology
• +۳ مورد دیگر

Emma Ahlqvist is an Associate Professor and Senior Lecturer at Lund University, affiliated with the EXODIAB Excellence Center for Diabetes Research and EpiHealth (Epidemiology for Health). Her primary role is as a Research Team Manager within the Department of Genetics and Diabetes at Lund University Diabetes Centre (LUDC). Dr. Ahlqvist holds a Docent (equivalent to Associate Professor) title and leads research on diabetes subtypes, focusing on genetic, epigenetic, and epidemiological factors. Her research interests center on classifying diabetes into clinically meaningful subtypes using data-driven approaches, particularly examining latent autoimmune diabetes in adults (LADA), type 2 diabetes, and their associated complications such as kidney disease and cardiovascular outcomes. Key projects include studying genetic susceptibility, environmental triggers (e.g., antibiotic exposure, infectious diseases), and lifestyle interventions (e.g., broccoli sprout extract trials). Dr. Ahlqvist has received notable awards including the 2019 Medeonstipendiet and the 2017 Ulf Andersson/Hjelt Foundation Award. She actively collaborates internationally, presenting at conferences like the Nordic Society for Human Genetics and Precision Medicine. Her work contributes to UN Sustainable Development Goals related to good health and well-being (SDG 3) and sustainable cities and communities (SDG 11) through improved diabetes management strategies. Current projects include the Swedish Research Council-funded initiative 'Diabetes subclassifications - new genes and novel mechanisms' (2018–2025), exploring genetic and molecular mechanisms underlying diabetes subtypes. She also co-supervises doctoral students in cardiovascular disease prevention and bioinformatics research.

Christian Balkeniusمشاهده پروفایل

استاد

• Cognitive Development
• Computational Models
• Neurophysiology
• +۷ مورد دیگر

Christian Balkenius is a Professor of Cognitive Science at Lund University's Department of Philosophy. His primary affiliation is with the Cognitive Science Department, where he leads research in computational models of cognitive development. He is based at LUX:B452 (Helgonavägen 3, Lund) and manages the Lund University Cognitive Robotics Lab. His work integrates neuroscience, robotics, and computer science to model human-like cognitive processes. Research interests focus on understanding how sensorimotor and emotional development emerge through computational modeling and robotic implementation. Notable projects include the Ikaros framework for brain-inspired robot control and studies on trust dynamics in human-robot interaction. Recent work explores time delays in social robots, haptic perception systems, and ethical aspects of AI governance. Collaborations span international teams working on topics like neurophysiological modeling and humanoid robotics. He supervises 12 documented research projects and contributes to major initiatives such as the WASP-HS program. His lab develops open platforms like the Epi humanoid robot and explores adaptive inhibition mechanisms for energy-efficient systems. Key Projects: WASP-HS, MoMa (Human-Robot Movement Analysis), Trust in Social Artefacts Labs: Lund University Cognitive Robotics Lab Grants: Swedish Foundation for Humanities and Social Sciences, Knut and Alice Wallenberg Foundation

Tomas Björklundمشاهده پروفایل

استاد

• Gene Therapy
• Parkinson's Disease
• Molecular Biology
• +۴ مورد دیگر

Professor Tomas Björklund is a faculty member at Lund University, affiliated with the Faculty of Engineering (LTH) and the MultiPark research initiative focused on Parkinson's disease. His research integrates molecular biology, biotechnology, and advanced machine learning to develop therapies for neurological disorders, particularly Parkinson's disease and glioblastomas. He leads projects such as BainSPACE and NSC-Reconstruct, aiming to translate research into clinical applications through partnerships with biotech companies and pharmaceutical industries. He has founded four biotech companies and actively collaborates on projects funded by organizations like Hjärnfonden and the European Commission's Horizon 2020. His work emphasizes spatial sequencing, stem cell organoids, and engineered AAV vectors tailored for precise cellular targeting. He advocates for entrepreneurship to accelerate therapy development, ensuring research outcomes reach clinical settings efficiently. His research aligns with UN Sustainable Development Goals, particularly in advancing health and well-being. Björklund oversees a dynamic research group that combines invention, interdisciplinary collaboration, and translational medicine. His contributions include pioneering spatial sequencing and viral vector engineering, with 52 publications and five active or recent projects. He mentors students in cutting-edge methodologies and drives initiatives to map neural circuits and improve gene therapy efficacy.

Amy Loutfiمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Amy Loutfi is a Professor of Information Technology at Örebro University, where she leads the AI, Robotics and Cybersecurity Center (ARC), and serves as Program Director for the Wallenberg AI, Autonomous Systems and Software Program (WASP) at Linköping University. Born in Saint John, Canada (1978), she earned her PhD in Computer Science from Örebro University (2006) and became a Docent (2011) and Professor (2015) in Information Technology there. Her research focuses on intelligent sensor systems, particularly their integration with artificial intelligence to convert raw data (e.g., from robotic olfaction or vision sensors) into human-interpretable semantic information. She explores human-robot interaction dynamics, emphasizing safety, collaboration, and usability across diverse environments like homes and industries. Key projects include BioLearning (water quality analysis), MicroSens (taste perception via microbiota), and TeamRob (robot teams working with humans). Amy’s work bridges technical innovation with societal impact, addressing challenges in healthcare, elder care, and smart home technologies. She advocates for Sweden’s national AI strategy and emphasizes ethical considerations in emerging robotics. Her research also involves collaboration with industry and public sectors, as seen in the E-care@home and Mednose initiatives. In 2020, she was elected to the Royal Swedish Academy of Engineering Sciences (IVA) for her contributions. Her grants and projects include strategic funding for AI applications and interdisciplinary collaborations. She collaborates with global teams and contributes to conferences like ICSR (Social Robotics) and ECAI (Artificial Intelligence).

• Machine Learning
• Bayesian Inference
• Deep Learning
• +۵ مورد دیگر

Najmeh Abiri holds roles as a Research Engineer, Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), Postdoc in the Department of Statistics, and Researcher in Computational Science for Health and Environment at Lund University. She is affiliated with eSSENCE: The e-Science Collaboration. Her primary research interests include variational inference, Bayesian inference, deep learning, computational biology, and machine learning, contributing to UN Sustainable Development Goals related to environmental sustainability and health. Abiri has conducted impactful research on probabilistic multivariate time series imputation using diffusion models, disease risk mapping with AI, and quantum mechanics applications of deep learning. She has also pioneered denoising autoencoder techniques for handling missing data across diverse datasets. Notable outputs include a 2022 study predicting West Nile virus outbreaks using eco-climatic drivers and a 2021 paper on inverse problems in quantum mechanics. Her work spans interdisciplinary collaborations, addressing global challenges through data-driven approaches. Abiri organized workshops on AI/ML technologies and GAN applications, fostering academic engagement. She led the 2014-2019 project on statistical inference with deep latent variable models, underscoring her commitment to advancing computational methodologies in science and health.

• Self-Adaptive Systems
• Cyber-Physical Systems
• Software Engineering
• +۴ مورد دیگر

Danny Weyns is a Professor at Linnaeus University's Department of Computer Science and Media Technology, Faculty of Technology. He leads the AdaptWise research group focusing on self-adaptive software systems and contributes to the Linnaeus University Centre for Data Intensive Sciences and Applications. His work spans cyber-physical systems, digital twins, and formal methods for system adaptation. Education: Extensive academic background in computer science, with a focus on multi-agent systems and distributed computing. Notable projects include the Aladino project on digital twins and PROSSES on secure systems. Research interests emphasize self-adaptive systems, runtime decision-making, and integrating AI with adaptive mechanisms. His publications address challenges like adaptation space reduction, uncertainty management, and industry applications of self-adaptation. Key contributions include the ActivFORMS framework for model-based adaptation and the development of methodologies like ASPLe for systematic reuse in self-adaptive systems. He collaborates internationally and chairs conferences like SASO and SEAMS.

Jonas Lundbergمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Program Analysis
• Software Engineering
• +۳ مورد دیگر

Jonas Lundberg is a Senior Lecturer in Computer Science at Linnaeus University since 2000, holding a Ph.D. in Theoretical Physics (Umeå University, 1994) and a second Ph.D. in Computer Science (Linnaeus University, 2012). He teaches foundational and advanced courses in programming, machine learning, and compiler design. His research focuses on data-intensive computing, program analysis, and cross-disciplinary projects like the Nordic Tweet Stream (NTS), analyzing social media data from Nordic countries. He leads the Computer Engineering program and participates in CDIO initiatives. Education: Ph.D. in Theoretical Physics, Umeå University (1994) Ph.D. in Computer Science, Linnaeus University (2012) Research interests include machine learning applications in software engineering, program analysis techniques for large codebases, and digital humanities projects leveraging big data. He co-founded the DISA research center (2016) to address challenges in data collection, analysis, and utilization. Recent work involves developing memory-efficient program analysis frameworks and detecting anti-democratic discourse in social media. Key projects include managing the Master's program in Computer Science and advancing self-adaptive systems engineering. His contributions span 70+ publications in venues like IEEE Transactions, Journal of Systems and Software, and conferences such as SAC and DHN. He is affiliated with research groups like Data Intensive Software Technologies (DISTA) and co-leads the Nordic Tweet Stream initiative. His work bridges computational methods with humanities, emphasizing real-time data analysis and cross-disciplinary collaboration.

• Internet of Things (IoT)
• Wireless Sensor Networks
• Machine Learning
• +۶ مورد دیگر

Mattias O'Nils is a Professor at Mid Sweden University's Department of Computer and Electrical Engineering (DET) and Head of the STC Research Centre. His research focuses on Internet of Things (IoT) systems, wireless sensor networks, machine learning, and computer vision with applications in industrial monitoring, forestry automation, and medical imaging. Lead author in 2025 IEEE Access paper on multi-component fault diagnostics Developed AdVision system for advertisement detection in printed media Co-inventor of Time-dependent Clustering Loss for DNN optimization His work spans edge computing, data fusion, and energy-efficient sensor networks, with recent emphasis on deep learning applications in healthcare and industrial systems. Publications highlight collaborations with researchers across multiple domains including forestry technology and skin lesion classification. Current research trends include distributed CNN optimization , contextual domain adaptation , and real-world energy consumption modeling for IoT devices. He supervises projects in the STC Research Centre related to smart industry solutions and transformative technology.

• Machine Learning
• Artificial Intelligence
• Industrial Informatics
• +۲ مورد دیگر

Gunnar Mathiason is Associate Professor in Computer Science at the University of Skövde's School of Informatics. His research bridges artificial intelligence and industrial applications, particularly in manufacturing optimization and network systems. Current projects include ProcTwin (2025-2028) developing digital twins for steel production and ShiftLabs (2023-2025) supporting digital transformation in manufacturing. Mathiason's publications demonstrate expertise in applying machine learning techniques to industrial challenges, with consistent focus on predictive modeling and process optimization in manufacturing environments. Recent work shows evolution toward explainable AI and complex system analysis.

• Supply Chain Management
• Operational Development
• Data Analysis
• +۵ مورد دیگر

Ola Johansson is a Senior Lecturer at the Faculty of Business, Kristianstad University. With over 10 years of academic experience, he bridges industry and academia, having previously worked at ABB and Ericsson in operational development roles focusing on Lean and Six Sigma methodologies. His research integrates data analysis, logistics systems, and sustainability, with a focus on supply chain management, recycling behavior, and IoT applications. He contributed to the SmartVA project (2021–2023), exploring real-time sewage water monitoring via IoT. Key research areas include: Supply Chain Optimization and Auto-ID Technologies Consumer Recycling Behavior Analysis Transportation Emissions Reduction Strategies IoT-Driven Environmental Monitoring Logistics Performance Benchmarking He has organized multiple conferences and workshops, including the RECS Workshop on Artificial Intelligence and IoT (2022) , fostering interdisciplinary collaboration. His work aligns with UN Sustainable Development Goals related to Industry, Innovation, and Climate Action. Notable projects include: SmartVA: Flood Risk Mitigation via Sewage Data Analysis Benchmarks for Auto-ID in Swedish Manufacturing (2009) IoT Applications in Waste Management (2006–2008) Publications span peer-reviewed journals like Benchmarking and Journal of Solid Waste Technology and Management , emphasizing empirical and simulation-based research. His teaching integrates interactive technologies, such as student response systems, to enhance learning outcomes.

Stefan Lindskogمشاهده پروفایل

استاد

• Cybersecurity
• Network Security
• Intrusion Detection Systems
• +۷ مورد دیگر

Stefan Lindskog is a full Professor at the Department of Computer Science, Karlstad University, Sweden. His research focuses on tunable security services, network security protocols, intrusion detection systems, and encryption methodologies. He has held academic positions at Karlstad University since 1990 and holds a PhD in Computer Engineering from Chalmers University (2005) and a Docent degree in Computer Science from Karlstad University (2008). His work emphasizes practical cybersecurity solutions, including firewall usability, multipath TCP security, and privacy-preserving technologies. Lindskog has authored over 60 peer-reviewed publications, including studies on encrypted network traffic analysis, Tor anonymity vulnerabilities, and firewall rule optimization. He collaborates extensively with industry partners and academic networks, contributing to projects like the EU-funded NEWCOM++ initiative. Teaching: Real-Time Systems (7.5 ECTS) Key Research Themes: Security service tunability, adaptive intrusion detection, and secure communication protocols Collaborations: HITS project (High Quality Networked Services in a Mobile World) Publications span topics from malware detection in encrypted traffic to systematic reviews of firewall usability. Lindskog’s work bridges theoretical security models and operational system implementation, with a focus on real-world applicability in industries like offshore energy and mobile communications.

Marc Toussaintمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Machine Learning
• +۶ مورد دیگر

Marc Toussaint is a Professor for Intelligent Systems at TU Berlin since 2020. Previously, he held a professorship for Machine Learning and Robotics at the University of Stuttgart (2012–2020). He has also held visiting positions at MIT (2017/18) and collaborated with Amazon Robotics (2017). His research focuses on the intersection of AI and robotics, emphasizing the integration of machine learning, optimization, and reasoning to address fundamental challenges in robotics. Key research areas include task-and-motion planning (logic-geometric programming), learning representations, and bridging model-based strategies with adaptive real-world behavior. Earlier work explored planning-as-inference, relational reinforcement learning, and evolutionary algorithms, with foundational contributions to gravity theory during his diploma studies.

Sindri Magnússonمشاهده پروفایل

دانشیار

• Distributed Optimization
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Sindri Magnússon is an Associate Professor and Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), part of the Data Science Research Group. He holds a B.Sc. in Mathematics from the University of Iceland (2011), a Master’s in Applied Mathematics (Optimization and Systems Theory) from KTH Royal Institute of Technology (2013), and a Ph.D. in Electrical Engineering from KTH (2017). He completed a postdoctoral fellowship at Harvard University (2018–2019) and was a visiting PhD student there in 2015–2016. His research focuses on distributed optimization, decision-making, and machine learning in cyber-physical systems and IoT. Key projects include AI-driven equitable decision-making, smart converter control for renewable energy systems, federated reinforcement learning, and sustainable data-driven algorithms. His work bridges core data science with applications in critical infrastructures, energy systems, and societal challenges. Recent articles explore multi-objective optimization for satellite scheduling, federated learning for privacy-preserving AI, and reinforcement learning in non-stationary environments. His contributions span theory and practice, addressing scalability, sustainability, and ethical AI. He is actively involved in the Data Science Research Group, which develops algorithmic methods for decision-making in smart cities and energy systems. No awards or grants are explicitly listed, but his projects suggest significant research funding and collaboration.