Alexander Karlsson is an Associate Professor at the Department of Information Technology, School of Informatics, University of Skövde. His research focuses on machine learning, information fusion, and their applications in transportation, healthcare, and industrial systems. He leads projects like I2Connect, developing advanced driver assistance systems using deep learning and situation awareness algorithms. Key research interests include driver intention recognition, predictive maintenance, telecommunication anomaly detection, and biomedical data analysis. He has published extensively in top venues like IEEE Transactions, ACM journals, and conferences like IPMU and MDAI. Education: PhD in Information Fusion (2010) from University of Skövde. Teaching: Coordinates courses in data analytics and AI at graduate and postgraduate levels. Projects: Active in industrial collaborations (e.g., automotive, steel production, telecommunication). His work bridges theoretical advances in machine learning with practical industrial challenges, emphasizing uncertainty quantification and robust decision-making frameworks.
Per-Erik Forssén is a Senior Associate Professor in the Department of Electrical Engineering at Linköping University, where he conducts research at the Computer Vision Laboratory (CVL). His academic journey includes a PhD (2004) and Docent degree (2009) from Linköping University, with a postdoctoral fellowship at the University of British Columbia's Laboratory for Computational Intelligence (2006-2007). His research focuses on visual perception and perceptual learning for robots , with specific expertise in uncertainty representation , vision for action systems , and continuous-time 3D motion modeling . He leads two major projects: Situation Aware Perception for Safe Autonomous Robotic Systems (ELLIIT-funded) and Dorsal Stream Robot Vision (Swedish Research Council-funded). His publications demonstrate strong focus on robotic vision systems and uncertainty quantification , with recent work exploring deep learning applications for autonomous driving, 3D registration, and human pose estimation. Earlier contributions include foundational work on rolling shutter compensation and video stabilization techniques.
Johanna Björklund is an Associate Professor at the Department of Computing Science , Umeå University, specializing in Formal Language Theory , Machine Learning , and Semantic Parsing of Multimodal Data . Her research focuses on translating complex media (e.g., videos with audio/subtitles) into graph-based semantic representations for algorithmic processing. Current projects include WARA Media & Language (2024–2026) and AI-Driven Contextual Communication (2021–2023). Key research areas: tree automata , graph transduction , contextual advertising , and computational linguistics . Her recent publications highlight advances in tree-to-graph transduction , multimodal semantic analysis , and privacy-preserving advertising . She has secured significant grants, including SEK 12 million (2021) and 4 million SEK (2020), for AI and deep learning initiatives. Grants: SEK 12 million (2021) for AI projects. 4 million SEK (2020) for Deep Learning methods. Scientific Awards: Lead researcher in multiple EU-funded projects. Recognized for contributions to formal languages and machine learning . She collaborates with teams in semantic analysis , tree automata , and multimodal AI , emphasizing practical applications like visual analytics and advertising systems .
Erik J Olsson is a Professor in Theoretical Philosophy at the Department of Philosophy , Lund University . He coordinates the Information Quality Research Group (LUIQ) and develops computational tools like Laputa for studying knowledge dynamics in social networks. PhD in Theoretical Philosophy (Uppsala University) Docent in Theoretical Philosophy (Uppsala, 2001) Research Fellow at University of Konstanz (1997-2003) Research Interests: His work focuses on epistemology, philosophy of science, and philosophical logic, particularly coherence theory and knowledge dissemination in digital societies. Current projects examine: Filter bubbles in Google search Knowledge value and stability Epistemic democracy in social networks Generality problem in knowledge categorization Scientific Contributions: Author of Against Coherence (OUP 2005), founder of Academic Rights Watch (2012), and principal investigator for projects funded by Riksbankens Jubileumsfond . His research connects cognitive psychology to epistemic justification frameworks.
Daniel Varro is a Professor and Head of Unit at the Department of Computer Science (IDA) of Linköping University, Sweden. He leads the Software and Systems (SAS) department, focusing on AI, software engineering, and cyber-physical systems. His research is supported by major grants like the Vinnova 5.6 million SEK project for AI-generated software quality assurance. Affiliation: Department of Computer Science (IDA), Linköping University Department: Software and Systems (SAS) Research Focus: Model-based systems, large language models for code analysis, reinforcement learning, and cyber-physical safety verification. His recent work includes empirical studies on machine learning notebooks, infrastructure code smells, and data leakage in large language models. He collaborates extensively within the Wallenberg Autonomous Systems Program (WASP) and trains doctoral students in software engineering.
Joel Brynielsson serves as an Associate Professor at KTH Royal Institute of Technology within the Division of Theoretical Computer Science, School of Electrical Engineering and Computer Science, while simultaneously holding the position of Research Director at the Swedish Defence Research Agency (FOI) since 2008. His academic credentials include a Ph.D. in Computer Science (2006) and M.Sc. in Computer Science and Engineering (2000), both from KTH, followed by achieving Docent (Habilitation) status in Computer Science in 2015. Dr. Brynielsson's research spans uncertainty management, information fusion, probabilistic expert systems, command and control systems, operations research, game theory applications, web mining, privacy-preserving data mining, and cyber security. His work bridges theoretical computer science with practical security applications, particularly in national defense contexts. His recent publication record shows consistent scholarly output through 2024, with research focusing on cybersecurity practices in Swedish administrative authorities, cyber-threat perception in the financial sector, and social media applications for crisis management. The interdisciplinary nature of his work is evident in collaborations across security, defense, and emergency management domains. Associate editor for Springer Security Informatics journal Technical program chair for IEEE EISIC conferences (2013-2019) Regular reviewer for security and informatics journals Research funding from EU, Swedish Armed Forces, and public authorities Through his dual appointments, Dr. Brynielsson maintains a vital connection between academic research and practical defense applications, contributing significantly to both scholarly knowledge and real-world security solutions.
Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.
Praveen Kumar Donta is an Associate Professor (Docent) and Senior Lecturer at the Department of Computer and Systems Sciences, Stockholm University, Sweden. His research focuses on distributed computing continuum systems, learning-driven approaches for IoT and edge computing, and intelligent data protocols. He leads the Distributed Immersive Participation research group which investigates how humans and things can be more connected and exchange information in real and virtual societies. Education: Ph.D. in Computer Science & Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad (2021) Visiting Ph.D. Fellow at Mobile&Cloud Lab, University of Tartu, Estonia (2019-2020) Master in Technology from JNTUA, Ananthapur (2014) Bachelor in Technology from JNTUA, Ananthapur (2012) Dr. Donta's research centers on distributed computing continuum systems that integrate cloud, edge, and IoT devices to deliver scalable and low-latency computing resources. His work explores learning techniques in IoT, AI/ML for computing systems, cognition and causality in computing systems, and cyber-physical continuum applications. He investigates how human body analogies can inform the design of more resilient and efficient distributed systems, as well as developing frameworks for privacy enforcement, equilibrium in computing continuum systems, and energy-efficient user interactions with smart environments. His research has significant applications in smart city management, satellite services, and intelligent transportation systems. Dr. Donta's publication record demonstrates a strong focus on the intersection of distributed systems, machine learning, and privacy-preserving technologies. His recent work shows an increasing emphasis on human-inspired approaches to distributed computing, with particular attention to making these systems more interpretable, efficient, and adaptable. His research spans theoretical foundations of computing continuum systems to practical implementations in areas like satellite services, smart environments, and anomaly detection. Scientific Awards and Recognition: IEEE Senior Member ACM Professional Member Dr. Donta serves as an editorial board member for several prestigious journals including IEEE Internet of Things Journal, Computing (Springer), Transactions on Emerging Telecommunications Technologies (Wiley), Measurement, and Computer Communications (Elsevier). He actively mentors the next generation of researchers, currently supervising PhD student Alfreds Lapkovskis and co-supervising Shubham Vaishnav. His research is supported by projects such as the Heterogeneous Computing Continuum for a Sustainable Smart City Management (HCSCM), which aims to develop scalable, secure solutions for urban environments by integrating IoT, edge, and cloud computing. As part of the Distributed Immersive Participation research group, Dr. Donta collaborates with researchers across disciplines to explore how technological advances enable humans and things to be more connected. The group focuses on application areas such as culture, transport, intelligent vehicles and e-health, developing solutions that enhance participation in both real and virtual societies.
Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.
Mattias Dahl is a Professor at the Faculty of Engineering, Blekinge Institute of Technology, affiliated with the Department of Mathematics and Natural Sciences since 1993. His research spans systems engineering, applied mathematics, and their applications in simulation, optimization, and modeling of technical systems, particularly in intelligent transport systems (ITS) through collaborations with Swedish Transport Agency and Administration. He has developed measurement systems using drones and satellites, focusing on area-wide change analyses and commercialization of research outputs. Education: B.Eng. in Electrical Engineering, Chalmers University M.Eng. in Computer Engineering, Luleå University of Technology Licentiate in Telecommunication Theory, Lund University of Technology PhD in Applied Signal Processing, Blekinge Institute of Technology (2000) His research emphasizes optimization of technical systems, self-learning methods, and artificial intelligence, with industry collaborations resulting in patents in mobile communication and computer vision. Recent work includes AI-driven weed seed reduction, railway capacity optimization (KAJT), and charging station allocation for EVs. He has contributed to projects like ADAS and Combating Reindeer Poaching with Drones, while also reviewing grants for international journals. Key scientific awards include the Teknikbrostiftelsen scholarship and Vinnova verification funds. His 15 most recent publications focus on radar interference mitigation, traffic data analysis, drone calibration, and charging infrastructure optimization.
Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).
