جست‌وجوی استاد و پژوهشگر

• Automation Engineering
• Advanced Manufacturing Technology
• Digital Twin
• +۷ مورد دیگر

Wei Wang is a Senior Lecturer in Automation Engineering at the School of Engineering Science , University of Skövde . His research focuses on advanced manufacturing technologies, digital twin frameworks, and sustainable production systems. Roles: Programme Coordinator for Computational Methods in Engineering – Master's Programme Projects: QWELD (2021–2025), Energy Storage & Power Electronics (2021–2023), Intelligent Simulation-Based Planning (2019–2023) Research Interests Dr. Wang specializes in human-robot collaboration , digital twin applications , and machine learning for industrial processes . His work bridges automation, sustainability, and cybersecurity in manufacturing systems. Scientific Trends Recent publications highlight multi-objective optimization in machining processes, deformation prediction using voxel-based models, and cybersecurity integration in cyber-physical production systems. Collaborative studies emphasize laser welding and green technology adoption . Education Leadership Coordinates Computational Methods in Engineering – Master's Programme and teaches courses at both bachelor's and master's levels, including Industrial Computing Technology and Simulation-Based Optimization .

• Cloud Manufacturing
• Industrial Automation
• Cyber-Physical Systems
• +۶ مورد دیگر

Göran Adamson is an Associate Professor in Automation Technology at the Department of Engineering, University of Skövde, Sweden. His research and teaching focus on advanced manufacturing technologies, particularly in the areas of cloud manufacturing, adaptive control systems, and cyber-physical production systems. With a strong publication record since 2010, his work bridges theoretical research with practical industrial applications. Adamson's research interests center on the integration of cloud computing with manufacturing processes, developing adaptive control frameworks using function blocks (particularly IEC 61499 standard), and enhancing human-machine interaction on the shop floor. His work addresses critical challenges in Industry 4.0 transformation, focusing on how manufacturing equipment can become more flexible, reconfigurable, and responsive to changing production demands through cloud-based service architectures. His publication record reveals a clear research trajectory focused on adaptive manufacturing systems. Starting with foundational work on function blocks for process planning around 2010-2012, his research evolved toward cloud manufacturing applications from 2013 onward. A significant portion of his work examines how cyber-physical systems can enable more adaptive production environments, with strong emphasis on practical implementation challenges. His research shows consistent collaboration with colleagues Lihui Wang, Magnus Holm, and Philip Moore across multiple publications.

• Automation Engineering
• Functional Programming
• Optimization
• +۷ مورد دیگر

Richard Senington is a Senior Lecturer in Automation Engineering at the School of Engineering Science, University of Skövde. He holds a PhD from the University of Leeds (UK) obtained in 2013 and has been working at the University of Skövde since 2016 after several years as a software consultant. His primary research and teaching focus on automation technologies within industrial production systems. Senington completed his doctoral studies at the University of Leeds in 2013 before working as a software consultant for several years. He returned to academia in 2016 when he joined the University of Skövde, where he has since established himself as a researcher in automation engineering and related fields. His research interests span Functional Programming, Optimization techniques, Industrial Internet of Things, and Human-Robot Collaboration. Senington's work particularly focuses on applying advanced computational methods to industrial production problems. His recent research has explored Knowledge Graphs for manufacturing decision support, Monte Carlo Tree Search applications in production systems, and containerization technologies for factory software management. He actively investigates how these technologies can improve production efficiency, decision-making processes, and system resilience in manufacturing environments. His publication record shows consistent output in industrial simulation and production engineering venues, with a focus on practical applications of computational methods in manufacturing. His most recent work (2024-2025) centers on Knowledge Graph applications for production systems, while his earlier work (2018-2021) focused on containerization technologies and Monte Carlo Tree Search applications. Senington is currently involved in two major research projects: 'Future-Proof Factories: The SMART Way to Resilient Production' (December 2025 - November 2029), which explores how factories can maintain operations during disruptions like rising energy prices or machine failures, and 'Virtual Factories of the Future' (October 2018 - September 2026), an eight-year research profile focused on knowledge-driven optimization in manufacturing. He previously contributed to the 'SUDO' project (October 2020 - September 2024), which addressed lock-in effects in IT system development for complex application domains. Within the EU-funded SYMBIO-TIC project, Senington collaborates with Magnus Holm on Human Robot Collaboration research. His teaching responsibilities include multiple courses at both bachelor's and master's levels in engineering disciplines, with a focus on advanced production technologies and automation systems.

Hazem Torfahمشاهده پروفایل

استادیار

• cyber-physical systems
• formal verification
• runtime monitoring
• +۵ مورد دیگر

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

Björn Johanssonمشاهده پروفایل

استاد

• Cognitive Science
• Human-Computer Interaction
• Complex Systems
• +۴ مورد دیگر

Björn Johansson is a Professor at Linköping University in the Department of Computer Science (IDA) , affiliated with the Human-Centered Systems (HCS) division. His research focuses on human interaction with complex systems, particularly in energy systems, crisis management, and autonomous systems, using simulation and game-based methods. Leading the "Att vända strömmen" project (funded by Swedish Energy Agency) to address sustainability challenges in energy systems Co-developing "megagames" for understanding societal polarization and climate change Collaborating with Saab on aviation interface design and autonomy research His recent publications span AI in smart grids, ERP cloud migration, and resilience in autonomous systems. He teaches courses in Cognitive Systems Engineering and Crisis Management, and co-leads the SkyLab research environment for future aviation concepts. Current grants include SEK 24.5 million from the Swedish Energy Agency and SEK 33.4 million from the Kamprad Family Foundation.

• Cyber-Physical Systems
• Embedded Control Systems
• System Reliability
• +۵ مورد دیگر

Martin Törngren is a Professor of Embedded Control Systems at the Department of Mechatronics and Department of Mechanical Engineering at KTH Royal Institute of Technology. He is actively engaged in research and teaching within Embedded Systems , Mechatronics , and Cyber-Physical Systems , focusing on architecture, model-based development, and system integration. His work bridges control systems and embedded systems, emphasizing reliability and safety in industrial applications. Research Leadership: Technical coordinator of the Artemis project iFEST, focusing on functional safety for automated systems. Education: Teaches courses like Cyber-physical Systems (MF2140) , Mechatronics Higher Course , and doctoral-level courses in embedded systems. Research Interests: Törngren's work spans model-based systems engineering , distributed control systems , co-design of control and computer systems , and system safety . He emphasizes industry-university collaboration , particularly with the automotive and automation sectors. His publications highlight trends in automated driving safety , edge computing , and toolchain integration . Collaborative Initiatives: He co-founded the Innovative Centre for Embedded Systems (ICES) and participates in networks like Artist2 and ArtistDesign. His research projects often involve partnerships with global companies and academic institutions.

Viktor Larssonمشاهده پروفایل

دانشیار مدعو

• Hydraulic Systems
• Energy Efficiency
• Hybrid Transmissions
• +۲ مورد دیگر

Dr. Viktor Larsson serves as Adjunct Associate Professor in Fluid and Mechatronic Systems at Linköping University. He holds a Ph.D. (2019) and Licentiate of Engineering (2017) from Linköping University, and an M.S. in Mechanical Engineering from the same institution (2014). His research focuses on energy-efficient motion systems for mobile machinery, particularly hybrid hydromechanical transmissions. Core expertise includes developing control strategies, hardware-in-the-loop simulation, and optimization of fluid power systems. Contributes to the open-source multi-domain simulation tool Hopsan. Publications demonstrate consistent focus on improving energy efficiency through advanced control algorithms and system redesign. Awarded best master's thesis in 2014 for innovations in hydraulic transmission technology. Research Themes: Hydraulic system electrification, real-time simulation validation, and sustainable design for construction/agricultural machinery.

• Cloud Computing
• Big Data Pipelines
• Machine Learning
• +۴ مورد دیگر

Mihhail Matskin is a Professor at the Royal Institute of Technology (KTH) within the Department of Software Logic & Computer Systems. He specializes in cloud computing, big data pipelines, machine learning, and distributed systems. His work emphasizes optimizing cloud resource allocation, developing graph-based cost models, and advancing AI-driven systems like SQL recommendation engines and relation extraction frameworks. He actively contributes to KTH's educational mission through roles as examiner and course manager in advanced computer science and engineering courses, including Distributed AI, Cloud Storage Optimization, and Big Data Workflow Design. His research spans over two decades, focusing on scalable data management solutions, edge computing applications in healthcare, and semantic analysis of social media. Notable projects include the DataCloud initiative for big data pipeline orchestration and the DEF-PIPE DSL visualization framework. Matskin’s work bridges theoretical advancements with practical implementations, addressing challenges in reproducibility of LLM-based systems, containerized edge computing, and constraint programming for robotics. His academic contributions include pioneering studies on cloud cost modeling, rule-based storage tiering, and contrastive learning for NER tasks. He has advised numerous advanced-level degree projects across computer science, embedded systems, and communication technologies. Matskin’s lab focuses on innovative solutions at the intersection of distributed systems, AI, and data engineering.

• User Experience Design
• Human-Computer Interaction
• Human-Robot Interaction
• +۳ مورد دیگر

Beatrice Alenljung is an Associate Professor of Information Technology at the University of Skövde, Sweden, leading the Interaction Lab. She specializes in human-centered technology research with a focus on user experience (UX) design, human-robot interaction, and decision support systems. Her work bridges theoretical insights with practical applications in fields like healthcare robotics, industrial automation, and educational technology. She teaches in the User Experience Design program and actively contributes to interdisciplinary projects addressing challenges in aging populations, industrial collaboration, and sustainable digital transformation. Key ongoing projects include exploring socially assistive robots for older adults (RoSA and RO-LIV), optimizing human-robot collaboration in manufacturing (KOMPARO and AIHURO), and risk reduction strategies for emergency services through digital simulation. Her research emphasizes empirical studies on UX evaluation frameworks (e.g., ANEMONE), human factors in robotics, and the societal impacts of emerging technologies. Alenljung has authored over 40 peer-reviewed publications, spanning conference papers and book chapters in HCI, robotics, and requirements engineering. She holds a PhD in Decision Support Systems from the University of Skövde and has maintained collaborative ties with industries and institutions across Europe.

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).

Volker Kruegerمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Volker Krueger is Professor at Lund University's Department of Computer Science, with joint appointments in Robotics and Semantic Systems. He leads research at the intersection of artificial intelligence, computer vision, and autonomous systems through the ELLIIT initiative and NEXTG2COM Competence Centre. His research group focuses on developing robust perception and decision-making systems for industrial and service robotics applications. Research interests span robot perception under challenging conditions, uncertainty quantification in machine learning models, and human-robot interaction frameworks. Recent projects investigate vision-language models for robotic failure handling, domain-specific languages for robot programming, and optimization methods for logistics systems. His laboratory coordinates multiple European projects including RIACT (Radical Innovation of Adaptive Cobot Technology) and NextG2Com (Next Generation Communication Infrastructures). He actively promotes robotics education through initiatives like the Robotics Week for Schools and maintains extensive industry collaborations in manufacturing and automation sectors.

Ove Edforsمشاهده پروفایل

استاد

• Radio Systems
• Statistical Signal Processing
• Massive MIMO
• +۵ مورد دیگر

Ove Edfors is a Professor at the Department of Electrical and Information Technology, Lund University, affiliated with the Faculty of Engineering (LTH). His primary research focuses on radio systems, statistical signal processing, and massive MIMO technologies. He is a core member of the LTH Profile Area: AI and Digitalization and the LU Profile Area: Natural and Artificial Cognition. Edfors leads the NEXTG2COM Vinnova Competence Centre and contributes to ELLIIT initiatives. His research spans multi-carrier systems, low-complexity algorithms, and wireless communication applications. Key projects include 6G radio testbed development and millimeter-wave channel characterization. He has co-authored over 225 publications and supervised 22 graduate students. Notable achievements include the IEEE Signal Processing Society Donald G. Fink Award (2023) and the IEEE Communications Society Best Tutorial Paper Award (2018). Recent work emphasizes indoor localization via multi-sensor fusion (LuViRA Dataset) and energy-efficient MIMO processors. Active collaborations involve global institutions and industry partners in 5G/6G infrastructure. Edfors' contributions align with UN SDGs for affordable and clean energy (Goal 7) through energy-efficient wireless systems and innovation (Goal 9).

Young Suk Leeمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Design Research
• Artificial Intelligence in Art
• +۴ مورد دیگر

Young Suk Lee is a Postdoctoral Fellow at the Umeå Institute of Design (UID), Umeå University, holding the UmArts WASP-HS Art and AI Postdoctoral Research Fellowship. Their work investigates poetic embodiments of artificial intelligence and machine learning, seeking to incorporate human values often overlooked in mainstream AI development. Education: PhD in Engineering Technology, University of Twente, The Netherlands (Thesis: 'Expressive Entities: An Exploration and Critical Reflection on Poetic Engagements with Technology') Master of Fine Arts (MFA) in Fine Art Master of Fine Arts (MFA) in Digital Art Master of Science (MS) in Human-Computer Interaction Lee's research focuses on designing human-technology relations through expressive aesthetics and a humanistic lens. They explore how delicate human values can be integrated into AI systems by creating poetic experiences that defamiliarize everyday interactions with technology. This is achieved through a hybrid methodology combining traditional craftsmanship, digital manufacturing, and physical computing. Their practice-driven research aims to re-imagine design scenarios and reconnect ordinary artifacts to reflective and imaginative engagements, fostering a critical perspective on technology in daily life. Lee's publication record includes work on mediating human-human interaction through poetic engagements, as presented at the 2024 International Conference on Social Robotics. Their research consistently bridges the fields of social robotics, interactive art, and critical design, with an emphasis on embodied and meaningful interactions. Scientific Awards: No scientific awards were mentioned in the provided information. Lee is currently leading the UmArts Art & AI research project (2023-2028), funded by the Wallenberg AI, Autonomous Systems and Software Program for Humanities and Society (WASP-HS). There is no mention of graduate student advising in the available materials.

Truls Nybergمشاهده پروفایل

پژوهشگر

• Autonomous Vehicles
• Motion Planning
• Risk-aware Planning
• +۵ مورد دیگر

Truls Nyberg is an Industrial Postdoc at TRATON Group, affiliated with the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. His research focuses on foundation models and vision-language models for decision-making and motion planning in autonomous vehicles, addressing critical challenges in occlusion handling and risk assessment. His academic background includes: PhD in Computer Science (Robotics, Perception and Learning) from KTH Royal Institute of Technology (2025), with thesis "Mind the Unknown: Risk- and occlusion-aware motion planning for autonomous vehicles" supervised by Jana Tumova and co-supervised by Patric Jensfelt MSc in Control and Information Systems from Linköping University BSc in Applied Physics and Electrical Engineering from Linköping University Nyberg's research centers on autonomous driving systems where occlusions create safety risks. He develops risk-aware motion planning algorithms using sequential reasoning about hidden objects, vehicle-to-everything (V2X) communication, and foundation models to enhance decision-making in complex traffic. His work bridges theoretical robotics with industrial applications, emphasizing real-world safety specifications and uncertainty handling in urban and highway scenarios. Analysis of his publications reveals a clear trajectory from fundamental risk-aware planning (2021) to advanced occlusion reasoning using V2X (2024). His research consistently targets safety-critical gaps in autonomous driving, with growing emphasis on cooperative perception and language-integrated vision models. Key trends include formal safety guarantees, real-time contingency planning, and leveraging hidden object predictions for proactive vehicle control. Nyberg was funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) during his doctoral studies. While no current student advising is documented, his industry-academia role at TRATON Group demonstrates strong collaboration between KTH and automotive manufacturing. His research directly addresses industrial challenges in commercial vehicle automation. As part of KTH's Robotics, Perception and Learning division, Nyberg contributes to Sweden's leading autonomous systems research environment. His work leverages state-of-the-art robotics labs and industry partnerships to translate academic innovation into practical solutions for next-generation autonomous vehicles.

Lena Wildمشاهده پروفایل

پژوهشگر

• End-to-End Explainable Map Learning
• Autonomous Driving
• Road Perception
• +۵ مورد دیگر

Lena Wild is an industrially employed PhD student at TRATON Group and the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. She is advised by Patric Jensfelt as academic supervisor and Rafael Valencia as industrial supervisor. In January 2025, she joined the Wallenberg AI, Autonomous Systems and Software Program (WASP) as an affiliated industrial PhD student. Since September 1, 2025, she has been a visiting PhD student at Stanford University's Autonomous Systems Lab led by Prof. Marco Pavone. Her research focuses on end-to-end explainable map learning for autonomous driving, with broader interests in road perception and integration of prior knowledge in deep learning systems. Her academic background bridges deep learning, engineering, physics, and the humanities, reflecting in her interdisciplinary approach to research. Her recent publications demonstrate a strong focus on explainable AI for autonomous driving systems, particularly in HD map updating and change detection. Both of her recent papers (ICCV 2025 and ECCV 2024) address critical challenges in autonomous vehicle perception systems, with an emphasis on explainability and structured priors. Lena Wild has received recognition for her literary work as well, with her debut novel "Preparing for Chance" published in October 2024 through the ZOOM-ED series by Edition Raetia, which won the fifth issue of ZOOM-ED. Her educational background includes dual expertise in technical fields and humanities, having earned Bachelor's and Master's degrees in both Technical Physics from Vienna University of Technology and Philosophy/Classical Languages from the University of Vienna. During her Master's, she conducted thesis work in collaboration with CERN on machine learning for likelihood-free inference.