Find Academic Supervisors

• Magnetism
• Nanotechnology
• Nanomagnetic Systems
• +7 more

Vassilios Kapaklis is a Professor in the Department of Physics and Astronomy, specifically in the Materials Physics division at Uppsala University, Sweden. He leads research in advanced magnetic nanostructures, with a strong focus on artificial spin ice, nanomagnetism, and magnetoplasmonics. His work bridges fundamental condensed matter physics with applications in nanophotonics and spintronics. His research interests include magnetism in nanostructures , artificial spin ice systems , magnetization dynamics , magneto-optics , and nanomagnetic metamaterials . He investigates how magnetic textures and interactions at the nanoscale can be controlled using light and external fields, with implications for next-generation data storage and computing. His group employs experimental techniques such as nanofabrication, magneto-optical spectroscopy, and neutron scattering, complemented by theoretical modeling. Recent publications reveal a strong trend in controlling magnetism via light, especially through plasmonic enhancement and ultrafast optical excitation. His team explores emergent phenomena in frustrated magnetic systems, such as thermal dynamics in artificial spin ice and long-range interactions in mesoscopic Ising chains. There is also a growing focus on ion-implantation for creating magnetic metamaterials and on structural characterization of thin films using laboratory X-ray sources. No scientific awards were explicitly mentioned in the provided text. He actively supervises and collaborates with researchers including Samuel D. Slöetjes, Matías Pablo Grassi, Agne Ciuciulkaite, and Richard M. Rowan-Robinson, indicating a vibrant research group. While specific grants are not detailed, his consistent high-impact output suggests active funding. Kapaklis is involved in developing advanced instrumentation, such as modular magneto-optical diffractometers, and contributes to software tools for data analysis, like GENL for Laue oscillations. His research team is clearly engaged in cutting-edge experimental and theoretical work on the interplay between light and nanomagnetism.

• Deep Learning
• Multi-Agent Systems
• Grid Computing
• +7 more

Dawit Mengistu is a Senior Lecturer in Computer Engineering at the Department of Computer Science, Faculty of Natural Science, Malmö University. His research focuses on deep learning applications, edge computing, and distributed simulation systems. 2022: Deep Learning Approaches for Crack Detection in Bridge Concrete Structures 2024: Concrete Crack Detection Using Multi-Source Data Augmentation in Deep Learning Models 2018: Session Key Agreement for End-to-End Security in Time-Synchronized Networks His research spans artificial intelligence and system performance optimization , with specific interests in: Multi-agent simulation scalability Edge computing for IoT devices Concrete structural analysis via neural networks Grid environment resource management Article trends show increasing focus on infrastructure AI and distributed systems , while earlier works concentrated on simulation algorithms and middleware. No recent scientific awards listed in available data.

Håkan SundellView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Parallel and Distributed Systems
• +7 more

Håkan Sundell is an Assistant Professor of Computer Science at the University of Borås , affiliated with the Faculty of Librarianship, Information, Education and IT and the Department of Information Technology . Since 2006, he has been active as a teacher in informatics and a research group leader for the CSL@BS research group , while also serving as program manager for the System Architecture Education with a Focus on Software Development . His research spans Artificial Intelligence , Parallel and Distributed Systems , Concurrent Programming , and Machine Learning Applications , with a particular emphasis on lock-free data structures and GPU algorithms . He has developed over 30 courses and supervised multiple doctoral students to completion. Scientific contributions include advancements in non-blocking algorithms, real-time systems, and AI-driven marketing tools. His article trends reflect interdisciplinary work in data science for fashion retail , district heating analytics , and marketing campaign optimization . Key Awards: Best Paper Award at IPDPS 2003 Recognized as a pioneer in Swedish computer game development As a main supervisor since 2012, he has driven externally funded research projects (VR, SSF, KKS, HUR) and served on university committees, including chairing the education committee from 2018–2022.

Gregorio MarchesiniView profile

Researcher

• Multi-Agent Systems
• Robotics
• Control Systems
• +7 more

Gregorio Marchesini is a doctoral student at KTH Royal Institute of Technology, affiliated with the Department of Intelligent Systems under the School of Electrical Engineering and Computer Science. His research focuses on networked systems control under spatio-temporal and communication constraints, with an emphasis on formal methods for multi-agent systems in mixed autonomy environments. He is associated with the EU project SymAware , which develops situational awareness frameworks for autonomous systems. His research interests include Signal Temporal Logic (STL) for task specification Multi-agent planning and control Graph theory and predictive control Formal methods in robotics The articles he has co-authored address topics such as temporal logic planning , decentralized control , and communication-constrained optimization for multi-agent systems. These works often intersect with robotics , control theory , and autonomous navigation in complex environments. Gregorio actively mentors students, encouraging collaboration on MSc thesis projects related to his research. His software contributions and technical insights, such as optimizing Model Predictive Control (MPC) using CasADi's to_function() and warm-starting techniques, are shared on platforms like GitHub and personal blogs. He is involved in the Decision and Control Systems (DCS) division, contributing to both theoretical and applied aspects of autonomous systems.

• Intelligence Partitioning
• IoT
• Deep Learning
• +5 more

Irida Shallari is a Lecturer in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, specializing in Intelligence Partitioning for IoT systems. Her research spans Deep Learning , Embedded Systems , and Data Fusion , with applications in Edge Computing , 6G , and Smart Agriculture . Education: Doctoral Thesis (2021): "Intelligence Partitioning for IoT : Design Space Exploration for a Data Intensive IoT Node" (Mid Sweden University) Licentiate Thesis (2019): "Intelligence Partitioning for IoT : Communication and Processing Inter-Effects for Smart Camera Implementation" (Mid Sweden University) Her research focuses on optimizing IoT node architectures, balancing Communication vs. Computation trade-offs, and deploying Deep Learning in resource-constrained environments. Recent work includes Time-dependent Clustering for neural network quantization, ArUco Marker Calibration , and Generative AI in Education ethics. The 15 most recent publications (2025-2024) highlight trends in Distributed CNN Optimization , Model Evaluation for industrial IoT, and cross-disciplinary applications in Environmental Monitoring and Academic Integrity . No scientific awards are explicitly mentioned in the provided text.

• Computer Science
• Concurrency
• Programming Languages
• +7 more

Konstantinos Sagonas is a Senior Lecturer/Associate Professor at Uppsala University's Department of Information Technology. His work bridges theoretical and practical aspects of computer science, focusing on concurrency, formal verification, and functional programming in Erlang. Despite his self-identification as a "terrible e-mail responder," he encourages phone contact for direct communication. University: Uppsala University Department: Department of Information Technology Academic Rank: Senior Lecturer/Associate Professor His research spans from stateless model checking and dynamic partial order reduction for concurrent systems to static analysis and type systems in functional programming. Recent work explores IoT protocol testing via fuzzing and symbolic execution, fine-grain memory coherence for security, and automated detection of state machine bugs in network protocols. Key publication trends include formal methods (2024: "Testing IoT Protocol Requirements"; 2023: "Tailoring Stateless Model Checking for Event-Driven Programs") and concurrent data structures (2021: "Lock-free Contention Adapting Search Trees"). Earlier contributions focus on Erlang optimization (2002-2018) and logic programming tabling (1999-2006).

• Automation Engineering
• Advanced Manufacturing Technology
• Digital Twin
• +7 more

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 .

Eric ChenView profile

Associate Professor

• Coding Theory
• Secure Communications
• Optimization
• +6 more

Eric Chen is an Associate Professor in Electrical Engineering at Kristianstad University , affiliated with the Department of Computer Science under the Faculty of Natural Science . His research spans Coding Theory , Secure Communications , Internet of Things , and ICT for Sustainable Development , with secondary interests in Distributed Systems and E-health . He maintains two specialized databases: the Online Database of Quasi-Twisted Codes and Database of Two-Weight Codes . Teaching roles include Data Communications , Programming for Embedded Systems , and Master's Degree Projects . Recognized as a Merited Teacher for pedagogical excellence. Chen's recent publications focus on algebraic coding theory, including quasi-cyclic codes with improved minimum distance, bounds on codes over GF(17), and two-weight code construction using linear programming. These works bridge computer science, mathematics, and optimization, advancing error correction and secure data transmission frameworks. Scientific Awards : Merited Teacher (2025) As a key organizer of the RECS workshop series (Artificial Intelligence, Machine Learning, Cybersecurity), he actively fosters academic collaboration. His research environment aligns with UN Sustainable Development Goals, particularly in education and sustainable technology integration.

• Cloud Manufacturing
• Industrial Automation
• Cyber-Physical Systems
• +6 more

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
• +7 more

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.

• Computer Science
• Artificial Intelligence
• Distributed Real-Time Systems
• +3 more

Sanny Syberfeldt is a Senior Lecturer in Computer Science at the University of Skövde, affiliated with the School of Informatics and Department of Information Technology. His research focuses on distributed real-time systems, artificial intelligence applications, and simulation-based optimization for production and decision support. Email: sanny.syberfeldt@his.se Room: PA320P Phone: +46 500-448369 Key research areas include: Artificial Intelligence for production optimization Conflict resolution in distributed systems Continuous consistency management Game-based learning for technical training Simulation and virtual factory systems Publications span topics in distributed real-time databases , AI applications , and simulation frameworks , with notable contributions to conflict resolution and consistency protocols.

Marcus NyströmView profile

Researcher

• Eye Tracking
• Human-Computer Interaction
• Cognitive Science
• +2 more

Marcus Nyström is a Researcher at Lund University Humanities Lab and part of the eSSENCE: The e-Science Collaboration . He holds a PhD in Information Theory (2008) and was appointed Associate Professor of Ergonomics in 2015. His work focuses on eye tracking methodology , including instrument development and data analysis techniques. Research Areas : Eye Movement Physiology, Human-Computer Interaction, Cognitive Science, Deep Learning for Eye Tracking Projects : ADAPT2 (adaptive developer tools), GANDER (code review eye tracking), eSSENCE@LU (astronomy VR visualization) Recent publications analyze eye tracking fundamentals , deep learning frameworks , and multi-tracker systems . He contributes to journals like Behavior Research Methods and conferences on eye tracking research. His work intersects with UN Sustainable Development Goals related to Quality Education and Industry Innovation .

• Proteomics
• Infectious Diseases
• Host-Pathogen Interaction
• +5 more

Christofer Karlsson is a Researcher at the Infection Medicine department within Biomedical Center (BMC) at Lund University . His work focuses on proteomics and host-pathogen interactions in bacterial infections. His research applies mass spectrometry-based proteomics to study Streptococcus pyogenes and Staphylococcus aureus pathogenesis. Key projects include COVID-19 proteome profiling and computational methods development for proteomics data analysis. Recent publications highlight therapeutic antibody degradation by pathogens, lung fibrosis mechanisms , and proteomic landscape mapping in sepsis. His work contributes to UN Sustainable Development Goal 3 (Good Health) .

Hazem TorfahView profile

Assistant Professor

• cyber-physical systems
• formal verification
• runtime monitoring
• +5 more

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.

• Cyber-Physical Systems
• Embedded Control Systems
• System Reliability
• +5 more

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.