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Magnus MähringView profile

Professor

• Digital Innovation
• Artificial Intelligence
• Transformation Processes
• +5 more

Magnus Mähring serves as the Erling Persson Professor of Entrepreneurship and Digital Innovation at the Stockholm School of Economics (SSE), Head of the Department of Entrepreneurship, Innovation and Technology, and Director of the House of Innovation. He holds additional affiliations as a Fellow at Cambridge Digital Innovation (Hughes Hall and Cambridge Judge Business School), Co-director of the Swedish Center for Digital Innovation, and member of the Swedish Government’s Digitalization Council. His research centers on digital innovation dynamics, with key interests in organizational decision-making, transformation processes, AI workplace integration, and innovation ecosystems. Recent work explores algorithmic justice in public sector decision-making, sustainability applications of AI, refugee social capital formation, and digital transformation strategies for incumbent organizations. His scholarship reveals a trajectory from technical project management toward broader sociotechnical systems analysis, particularly examining how organizations navigate digital disruption while maintaining core physical operations. Scientific recognition includes: JSIS 2009 Best Paper Award Decision Sciences 2008 Best Article Award Multiple European Research Paper of the Year nominations VHB 2016 Best Paper Award Finalist Mähring actively contributes to academic governance as Associate Editor for Information Systems Research and International Advisory Board member at California Management Review. His industry engagement spans consulting for commercial enterprises, public healthcare organizations, and Swedish government entities, complemented by a decade-long tenure as CIO Executive Club leader and Swedish CIO/Tech Awards jury member.

Pär J. ÅgerfalkView profile

Professor

• digital agency
• artificial intelligence
• digital infrastructure design
• +8 more

Pär J. Ågerfalk is a Professor at the Department of Informatics and Media , Uppsala University. He is internationally recognized for his work on open source, distributed agile development, and digital innovation. His research spans digital agency, artificial intelligence, digital sustainability, e-health, and pragmatism. PhD in Information Systems Development (Linköping University) Previous roles at Örebro University, University of Limerick, Jönköping International Business School, and Lero – The Irish Software Research Center Research Interests Digital agency and AI Digital infrastructure design Sustainable development in IT Pragmatism in systems development E-health applications Open innovation practices Recent Publications focus on conceptual modeling, digital health applications, workarounds in IS, and privacy/accountability frameworks. His work has appeared in MIS Quarterly, European Journal of Information Systems, and Health Informatics Journal. Scientific Awards AIS Fellow Distinguished Member Cum Laude, Association for Information Systems

• Prostate Cancer
• Translational Research
• Biomarkers
• +4 more

Anders Bjartell is a distinguished Professor and Principal Investigator at Lund University, holding multiple leadership positions including Manager of Urological Cancer Research in Malmö, Professor in the eSSENCE: The e-Science Collaboration, and Professor in EpiHealth: Epidemiology for Health. He serves as a Principal Investigator at the Lund University Cancer Centre (LUCC), where he directs significant research initiatives in prostate cancer. Dr. Bjartell's research focuses on translational prostate cancer studies, with particular emphasis on identifying molecular targets for therapies in chemo- and hormone-resistant prostate cancer. His work spans in vitro studies, animal models, biomarker development and validation in tissue, blood and urine (including circulating tumor cells), biobanking, and clinical studies on imaging biomarkers and robotic surgery. His fingerprint analysis reveals strong activity in Prostate Cancer (100%), Patient care (61%), Prostatectomy (29%), Therapeutic Procedures (23%), and Man-related studies (23%), with significant contributions to Prostate-Specific Antigen research (16%), Robot-Assisted Prostatectomy (12%), and Biological Markers (11%). The recent publication trends in Dr. Bjartell's work demonstrate a strategic shift toward integrating artificial intelligence with traditional prostate cancer research, particularly in digital pathology, imaging analysis, and risk stratification. His involvement in major international collaborations like the PIONEER big data platform shows commitment to leveraging real-world evidence for advancing prostate cancer care. The research spans from basic molecular investigations to large-scale clinical implementation studies, reflecting a comprehensive translational approach. EAU Frans Debruyne Life Time Achievement Award (2025) Referenced in multiple patents and Wikipedia pages Extensive scholarly impact with over 370 research outputs Dr. Bjartell has supervised 16 research projects and students, demonstrating strong mentorship capacity. His current projects include 'Biomarkers and targeted therapies for advanced prostate cancer' (2024-2028), 'Digital Optimisation of Gleason Scoring (DOGS)', and development of new imaging biomarkers for PET/CT. He actively organizes academic events including the LUCC - Malmö Cancer Seminars (March 2024), LUCC - UroCAN conferences, and AI-focused cancer research seminars, establishing himself as a central figure in the prostate cancer research community. His laboratory infrastructure supports comprehensive prostate cancer research from molecular analysis through clinical implementation, with particular strengths in biomarker validation, digital pathology, and integration of AI technologies into prostate cancer diagnostics and management.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +1 more

Farzaneh Etminani is a Senior Lecturer at the School of Information Technology, Halmstad University. Her research focuses on Artificial Intelligence , Machine Learning , and Healthcare Informatics , with specific projects including CAISR Health , Evaluation of deep learning for LBD diagnosis , AIR – Artificially Intelligent use of Registers , and iMedA – Improving Medication Adherence . Her recent work includes explainable AI frameworks, synthetic electronic health records evaluation, and temporal modeling of clinical data. Key collaborations span neuroimaging, chronic disease prediction, and human-centered AI systems. She contributes to interdisciplinary research bridging computer science and clinical applications. Her publications cover topics such as: 3D deep learning for dementia diagnosis Transfer learning in neuroimaging Temporal fidelity in synthetic medical data Graph neural networks for clinical risk prediction Behavior change strategies in digital health interventions

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +6 more

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

• User-Centered Design
• Collaborative Digital Innovation
• Human-Computer Interaction
• +5 more

Jesper Lund is a Senior Lecturer at Halmstad University's School of Information Technology, where his academic work focuses on user-centered and collaborative digital design and innovation. His research spans multiple domains including information systems, human-computer interaction, and open and user-driven innovation, with particular emphasis on practical applications within industry contexts. Lund's research interests center around user studies, design, and evaluation of digital products and services, with significant focus on open and user-centered digital innovation processes. His work has examined these processes within the newspaper and health technology industries, and he is currently engaged in research concerning digital innovation connected to smart cities and communities, with specific focus on future mobility. His methodological approach often involves Living Labs as research environments for co-creation and user involvement. His publication record demonstrates a consistent trajectory from early work on e-newspapers and digital media services to contemporary research on urban mobility, autonomous vehicles, and socially resilient planning. Lund's research shows strong interdisciplinary connections between design, technology, and social sciences, with particular emphasis on how users interact with and shape digital innovations in real-world contexts. Lund maintains active engagement with practical applications of his research, as evidenced by his work with Urban Living Labs and community-based innovation approaches. His work bridges theoretical insights with practical design implications, particularly in the domains of smart city development and future mobility solutions.

• Software Reliability
• Static Analysis of Energy Usage
• Software Engineering
• +1 more

Wojciech Mostowski is a Senior Lecturer at the School of Information Technology, Halmstad University. His academic role involves both research and teaching, with a focus on software reliability and energy-efficient software analysis. Research interests: Mostowski leads the ELLIIT funded project on "Static Analysis of Energy Usage in Software" and contributes to software reliability research. His work bridges theoretical analysis with practical applications in energy-conscious software development. Teaching: He is responsible for courses including Advanced Object-Oriented Programming , Distributed Systems Programming , and Data Security for Embedded Systems , emphasizing technical rigor and pedagogical engagement. Advising: Mostowski supervises PhD students Joel Nyholm (main), Sina Entekhabi (main), and Sundas Munir (co-supervisor), fostering the next generation of researchers in software engineering and embedded systems. Contact: Reach him via email at wojciech.mostowski@hh.se or mobile +46 72-977 36 22.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +4 more

Zahra Taghiyarrenani is a postdoctoral researcher at Halmstad University , affiliated with the School of Information Technology and the IT - Computer Department . Her work bridges theoretical advancements in machine learning with practical applications in industrial and cybersecurity domains. Education : PhD in Machine Learning (2024, Halmstad University), Licentiate Thesis on Multi-Domain Learning (2022) Her research focuses on domain adaptation , federated learning , and explainable AI , with emphasis on handling data scarcity and heterogeneity. Key projects include fault identification in industrial systems, intrusion detection, and physics-informed models for diagnostics. Her publications highlight trends in cross-domain learning , combining privacy-preserving AI with edge computing , and addressing data efficiency through few-shot/supervised methods . Recent work explores Industry 5.0 applications of Explainable AI. Projects: EVE : Extending vehicle lifespan in electromobility Federated Learning for Well-being : Privacy and fairness in AI FeelAI : Edge-based predictive logistics models

• Continuous Casting Processes
• Mathematical Modeling of Industrial Systems
• Fluid Dynamics
• +3 more

Michael Vynnycky is an Affiliated Professor at KTH Royal Institute of Technology , specializing in mathematical modeling and numerical analysis of industrial metallurgical processes. His research focuses on continuous casting , electromagnetic stirring , and fluid-structure interactions in manufacturing systems. Key Research Areas: Continuous casting of metals, fluid dynamics, heat transfer, computational methods (FEM, CFD), inverse Stefan problems, and oscillation mark formation. Collaborations: Frequent collaboration with researchers like H. Fredriksson, B. Glaser, and A. Safavi Nick. Applications: Steel production, die casting, redox flow batteries, and polymer electrolyte fuel cells. Recent publications highlight work on blast furnace dynamics , muon radiography for structural analysis, and asymptotic modeling of gas-solid flows. His methodologies emphasize mathematical rigor and industrial relevance , as seen in studies on macrosegregation and electromagnetic flow control. Techniques: Leverages asymptotic analysis multiphysics simulation finite element methods computational fluid dynamics boundary reconstruction algorithms experimental validation to solve complex industrial problems. Email Contact: michaelv@kth.se

• Automatic Control
• Transportation Systems
• Sustainable Transport
• +5 more

Jonas Mårtensson is Professor of Automatic Control with applications in Transportation Systems at KTH Royal Institute of Technology in Stockholm, Sweden. He works in the Division of Decision and Control Systems within the School of Engineering and serves as the director of the Integrated Transport Research Lab (ITRL), a multi-disciplinary research center focused on sustainable transport systems in collaboration with Swedish industry and public actors. Dr. Mårtensson received his MSc degree in vehicle engineering in 2002 and his PhD degree in automatic control in 2007, both from KTH. He was awarded the docent title in 2016. His educational background combines deep expertise in both vehicle engineering and control systems, forming a strong foundation for his interdisciplinary research approach. His research spans efficient and sustainable transport systems with particular emphasis on cooperative traffic control and control of connected and automated vehicles, especially for freight and heavy vehicles. His work bridges theoretical control systems engineering with practical transportation applications. Recent publications reveal a strong focus on model predictive control for autonomous vehicles, platoon coordination systems, and safety mechanisms for connected vehicles, with increasing emphasis on sustainability and efficiency in freight transportation. Analysis of his 15 most recent publications shows consistent work across transportation safety, vehicle control systems, and sustainable logistics. His research integrates theoretical control methods with real-world transportation challenges, particularly in platooning, vehicle coordination, and energy-efficient driving strategies for heavy-duty vehicles. Director of the Integrated Transport Research Lab (ITRL) Professor of Automatic Control with applications in Transportation Systems Active researcher with publications in top journals like IEEE Transactions on Intelligent Transportation Systems and Automatica Dr. Mårtensson teaches Automatic Control courses (EL1000, EL1010, EL1020) and supervises numerous degree projects in Electrical Engineering and Computer Science specializing in Systems, Control and Robotics. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications in control systems for transportation. His laboratory work focuses on developing and testing control algorithms for heavy-duty vehicles, platooning systems, and sustainable transportation solutions. The Integrated Transport Research Lab serves as a hub for collaboration between academia, industry, and government agencies working toward more efficient and sustainable transportation systems.

• Control Theory
• Game Theory
• Machine Learning
• +4 more

Muhammad Umar B Niazi is a Marie-Curie Postdoctoral Fellow at both KTH Royal Institute of Technology and Massachusetts Institute of Technology , working under the guidance of Karl H. Johansson and Munther Dahleh respectively. Holding a Ph.D. in Automatic Control Engineering from Université Grenoble Alpes, Niazi's research spans secure monitoring of cyber-physical systems and dynamic incentive design for sociotechnical systems . Education : Ph.D. (Grenoble INP, 2021); M.Sc. & B.Sc. (Bilkent University, COMSATS) Research Directions : Secure estimation against cyberattacks in transportation networks and epidemic models Physics-informed learning for observer design in nonlinear systems Eco-driving incentives using Stackelberg game theory Aggregated monitoring of large-scale systems Scientific Contributions : 2025 publications on distributed observers and reachability analysis 2024 work on incentive mechanisms and sensor fault detection 2023 developments in physics-informed epidemic control 2022-2019 foundational work on network observability and opinion dynamics Awards : Marie-Curie Postdoctoral Fellowship (2022-2025) Best Student Paper Finalist, European Control Conference 2019 Niazi's interdisciplinary work combines control theory , game theory , and machine learning to address resilience in transportation, epidemic monitoring, and social network dynamics. His methods integrate theoretical rigor with practical implementations through tools like SUMO simulations and physics-informed neural networks.

Zifan WangView profile

Researcher

• Decision-making under uncertainty
• Machine Learning
• Optimal Transport
• +3 more

Zifan Wang is a doctoral student and researcher at the Division of Decision and Control Systems (DCS), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology. He is jointly advised by Prof. Karl H. Johansson and Prof. Michael M. Zavlanos at Duke University. He holds a Master's and Bachelor's degree from the Honors School of Harbin Institute of Technology. His research focuses on decision-making under uncertainty, leveraging tools from Machine Learning , Optimal Transport , Game Theory , and Control Theory , with a special interest in generative models and risk-averse optimization . Recent publications highlight his work on risk-averse learning in online convex games, constrained optimization with decision-dependent distributions, and distributional reinforcement learning for LQR systems. His methodological contributions include zeroth-order gradient estimation, one-point sampling strategies, and residual feedback for variance reduction. Honors include a 2024 Travel scholarship from Björns Foundation and a 2023 Travel grant from Karl Engvers Foundation . He actively participates in peer review for top conferences (NeurIPS, ICLR, L4DC, CDC, ACC) and journals (IEEE Transactions on Automatic Control, Automatica).

Nicola BastianelloView profile

Researcher

• Optimization
• Machine Learning
• Multi-Agent Systems
• +3 more

Nicola Bastianello is a post-doctoral researcher at KTH Royal Institute of Technology in the Division of Decision and Control Systems under Prof. Karl H. Johansson. Previously, he held post-doctoral positions at the University of Padova (2021-2022) and conducted visiting research at the University of Colorado Boulder (2019-2020). PhD in Information Engineering (2021), University of Padova Master in Automation Engineering (2018), University of Padova Bachelor in Information Engineering (2015), University of Padova His research focuses on the intersection of optimization and machine learning , particularly in multi-agent systems and decentralized learning . Applications include mobile robotics, HVAC systems control, and sustainable technologies. He is affiliated with Digital Futures at KTH and has collaborated with leading researchers including Prof. Karl H. Johansson, Prof. Sandro Zampieri, and Prof. Emiliano Dall’Anese. Current MSc thesis opportunities explore cooperative learning, physics-informed learning, and sustainable control systems.

• Deep Learning
• Sensor Systems
• Data Fusion
• +3 more

Mazhar Hussain serves as a Lecturer in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, Sundsvall. He is affiliated with the STC Research Centre and actively contributes to research in deep learning, sensor systems, and educational technology. His research spans deep learning-based data fusion, sensor applications for industrial measurement (including molten glass gob viscosity analysis and hazardous gas detection), and generative AI's impact on engineering education. His work bridges theoretical AI advancements with practical industrial monitoring and pedagogical innovation, particularly in addressing ChatGPT's implications for academic integrity. Analysis of his 2021-2025 publications reveals a dual focus: multi-sensor data fusion for environmental/industrial applications (gas detection, fuel consumption prediction, glass manufacturing) and educational technology innovations. His recent shift toward AI ethics in academia demonstrates responsiveness to emerging technological disruptions in higher education. No information is available regarding his advisees or research grants. He operates within the STC Research Centre at Mid Sweden University, which facilitates interdisciplinary science and technology research with emphasis on practical sensor system implementations.

• Networking
• Telecommunications
• Network Congestion Control
• +7 more

Rahim Rahmani is a Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University , affiliated with the STC Research Centre . His work focuses on network congestion control, quality of service (QoS), and protocol optimization in heterogeneous and dynamic network environments.