Tommy Persson is a Research Engineer at the Department of Computer and Information Science (IDA) within Linköping University. His research focuses on artificial intelligence, parallel computing, and robotics, with contributions to autonomous systems, distributed architectures, and high-performance computing. He is affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division, which emphasizes theoretical and applied AI research. His work spans domains such as unmanned aerial vehicle experimentation, parallel graphics code generation, and radar system optimization. Collaborations include projects with colleagues like Patrick Doherty and Peter Fritzson, reflecting a multidisciplinary approach to computational challenges. No scientific awards are explicitly mentioned, but his contributions to technical domains like MIMD systems and sensor technology highlight his expertise. Advising roles or grants are not detailed in the provided information. Persson is based at Campus Valla, contributing to both research and the academic community through his role in IDA and AIICS.
Sindri Magnússon serves as an Associate Professor in Machine Learning at Stockholm University's Department of Computer and Systems Science. His research focuses on distributed optimization, machine learning, and data-driven decision-making within complex network systems, with applications spanning power grids, IoT infrastructure, and satellite operations. His educational background includes a B.Sc. in Mathematics from the University of Iceland (2011), an M.Sc. 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 postdoctoral research at Harvard University (2018-2019) following a 9-month visiting PhD stint there in 2015-2016. Magnússon's research integrates theoretical optimization frameworks with practical machine learning implementations, particularly emphasizing reinforcement learning, federated systems, and distributed algorithms for resource-constrained environments. His work bridges theoretical guarantees with real-world applications in critical infrastructure and industrial systems. Analysis of his recent publications reveals a strong focus on communication-efficient distributed learning, non-stationary reinforcement learning environments, and predictive maintenance systems. His team consistently develops novel algorithms addressing asynchronous updates, model mismatch in federated settings, and multi-objective optimization challenges across IoT and satellite networks. Major awards include the IEEE ICASSP best student paper award (as supervisor) and a prestigious Swedish Research Council (VR) Starting Grant. VR Starting Grant: Resource Constrained Machine Learning in Complex Networks (PI, 4 MSEK, 2021-2024) Digital Futures: DEMOCRITUS project on critical societal infrastructures (Co-PI, 4 MSEK, 2021-2024) Vinnova: Smart Converters for Climate-neutral Society (PI, 3 MSEK, 2022-2025) He actively supervises seven PhD candidates including main supervision for Ali Beikmohammadi, Shubham Vaishnav, and Mohsen Amiri, plus industrial PhD projects with Spotify. As Associate Editor for IEEE/ACM Transactions on Networking, he contributes significantly to the networking and machine learning research communities.
Johan Karlsson is a Professor in the Department of Mathematics at KTH Royal Institute of Technology, Sweden. He serves as Associate Director Executive Research at Digital Futures, a cross-disciplinary research center focusing on digital technologies for societal challenges. He holds a PhD in Optimization and Systems Theory from KTH (2008) and an MSc in Engineering Physics (2003). His research focuses on inverse problems, optimization, model reduction, and their applications in remote sensing, signal processing, and control theory. He leads the Decision-making in Critical Societal Infrastructures (DEMOCRITUS) project and collaborates on initiatives like the Lindquist Symposium in Systems Theory. Teaching includes advanced courses such as Optimal Control and Convexity and Optimization in Linear Spaces . He supervises PhD students and has authored/co-authored numerous papers in journals like SIAM Journal on Control and Optimization and IEEE Transactions on Automatic Control . His work integrates optimal transport theory, control systems, and computational methods to address complex engineering and environmental challenges. Key affiliations include KTH’s Department of Mathematics and Digital Futures, with collaborations across academia and industry. His research group actively engages in workshops, conferences, and interdisciplinary projects to advance theoretical and applied aspects of optimization and systems theory.
Ingo Sander is a Professor in Electronic Systems Design at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty and the Division of Electronics and Embedded Systems. He joined KTH in 1993 and has held his current professorship since 2018. His research focuses on formal system design methodologies like ForSyDe, emphasizing embedded systems, mixed-criticality applications, and design automation. He co-founded the cross-disciplinary Digital Futures research center, which addresses societal challenges through digital technology innovation. Education: MSc in Electrical Engineering (Technical University of Braunschweig, 1990), PhD and Docent at KTH (2003, 2009). Professional experience includes work at Ericsson (1991–1993). Research Interests: Design methodologies for embedded systems, models of computation (MoCs), formal verification, and cyber-physical systems. Key contributions include the ForSyDe framework and design space exploration techniques for multiprocessor platforms. His work bridges theoretical foundations with practical implementations, targeting safety-critical and high-performance embedded systems. Teaching: Ingo Sander supervises numerous master’s degree projects in computer engineering, electrical engineering, and ICT innovation. He leads courses on embedded software, systems design, and simulation. Labs & Projects: Digital Futures collaborates with Stockholm University and RISE, advancing innovations in digital technologies. Sander’s projects include the SAFEPOWER initiative for energy-efficient mixed-criticality systems and CONTREX for control systems design.
Xi Wang is an Associate Professor in the Department of Production Engineering at KTH Royal Institute of Technology, Sweden, and serves as the Division Head of Industrial Production Systems (IPS). He holds a PhD from the University of Auckland (2013) and a Bachelor's from Tianjin University (2008). His research focuses on Cloud-based Manufacturing, Sustainable Manufacturing, Robotics, Digital Twin Technology, and Computer-aided Design. He is actively involved in editorial roles for journals like the International Journal of Manufacturing Research and the Journal of Manufacturing Systems. Education: PhD in Mechanical Engineering, University of Auckland, 2013 Bachelor of Mechanical Engineering, Tianjin University, 2008 Research Interests: Dr. Wang’s work emphasizes integrating advanced technologies into manufacturing systems, including: Cloud Manufacturing: Cyber-Physical Systems and Service Composition Sustainable Practices: Energy Efficiency and Recycling Systems Robotics: Collaborative Systems and Calibration Methods Digital Twin: High-Fidelity Modeling for Industry 5.0 His recent publications highlight optimization algorithms, predictive modeling, and blockchain applications in manufacturing. Professional Contributions: Managing Editor, International Journal of Manufacturing Research (IJMR) Editorial Board Member of 3+ international journals Lead researcher on EU-funded projects like SYMBIO-TIC and CAPP-4-SMEs Lab/Team Involvement: His research groups focus on Human-Robot Collaboration and Smart Production Logistics , with projects addressing real-time adaptive systems and safety frameworks.
Magnus Wiktorsson is a Professor at KTH Royal Institute of Technology's Department of Sustainable Production Development within the Digital Futures Faculty. His work focuses on smart production logistics, sustainability, and digital transformation in manufacturing systems. He explores applications of machine learning, IoT, blockchain, and digital twins to enhance supply chain visibility, optimize production processes, and achieve circular economy goals. His research integrates technical and socio-technical perspectives, addressing challenges in human-robot collaboration, data-driven decision-making, and Industry 4.0/5.0 transitions. Key themes include material efficiency, real-time data utilization, and participatory modeling in urban and industrial logistics. Recent work emphasizes frameworks for adaptive scheduling, explainable AI in logistics, and blockchain-based collaboration models. Wiktorsson collaborates with industry partners to validate frameworks through case studies, pilot projects, and simulation-based methodologies. His contributions span academic publications and contributions to strategic innovation programs like Produktion2030, focusing on sustainability and resilience in manufacturing systems.
Patrik Jansson is a Professor of Computer Science at Chalmers University of Technology since 2011, with joint affiliation to Gothenburg University. His work bridges Functional Programming, Domain-Specific Languages (DSLs), and applications to Climate Impact Research, Fusion, and Physics.
Yi Wang is a Professor of Embedded Systems at the Department of Information Technology, Uppsala University , Sweden. He leads research in real-time and embedded systems with a focus on modeling, analysis, and implementation of safety-critical applications. He is affiliated with the Embedded Systems Group and serves as a Principal Investigator (PI) in major research centers such as UPMARC and projects like CUSTOMER (ERC Advanced Grant), CoDeR-MP, and CERTAINTY. Research Interests: Yi Wang’s work centers on Embedded Systems Design, Real-Time Scheduling, Multicore Programming, and Model-Checking of Real-Time Systems . His research addresses fundamental challenges in timing predictability, schedulability analysis, and the verification of complex real-time systems. He has made significant contributions to the digraph real-time task model, mixed-criticality systems, and timing analysis of ROS 2 systems. His work bridges theory and practice, often resulting in deployable tools and formal methods for industrial applications. Recent Research Trends: His most recent publications (2023–2025) focus on optimizing real-time performance in ROS 2, managing parallel task graphs with resource contention, improving GPU-based inference on embedded platforms, and enhancing timing predictability in multithreaded executors. These works reflect a strong trend toward applying formal real-time theory to modern robotics, AI integration, and multicore embedded architectures. Scientific Tools and Leadership: He is a key contributor to foundational tools in real-time systems: UPPAAL – Model checking for timed automata TIMES – Schedulability analysis and code generation CATS – Compositional analysis of timed systems TIMES-Pro – Based on the digraph real-time task model Advising and Research Funding: Yi Wang has supervised numerous PhD students and postdocs. He has led or participated in multiple large-scale funded projects supported by the Swedish Research Council (VR), the Swedish Foundation for Strategic Research (SSF), and the European Commission (FP7, ERC). These include UPMARC (10-year Linnaeus center), CoDeR-MP (with ABB and SAAB), SAVE++ (with VOLVO), and CREDO. Laboratories and Research Groups: He is a core member of the Embedded Systems Group at Uppsala University and leads research within the UPMARC center, which focuses on programming models and analysis techniques for multicore architectures. His lab develops formal methods and tools to ensure correctness and timing guarantees in embedded and cyber-physical systems.
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 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.
Jacob Lindbäck is a doctoral student at KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on the analysis and development of scalable optimization algorithms for machine learning, particularly computational optimal transport and its applications. Key research areas: Machine Learning Optimization Algorithms Optimal Transport Computational Mathematics Publications highlight advancements in GPU-accelerated splitting methods for optimal transport, novel algorithms, and computational efficiency. He also contributes as an assistant for the course Modelling of Dynamical Systems (EL2820) .
Joakim Andén-Pantera is an Associate Professor in the Division of Probability, Mathematical Physics and Statistics at the Department of Mathematics, KTH Royal Institute of Technology. His research spans signal processing, statistical data analysis, and machine learning with applications in cryo-electron microscopy, biomedical signal analysis, and audio classification. His educational background includes advanced training in mathematics and signal processing leading to his current academic position. Though specific degree details aren't provided in the text, his research profile indicates deep expertise in mathematical methods for signal representation. Professor Andén-Pantera's work focuses on developing mathematical frameworks that extract discriminative information from signals while remaining invariant to irrelevant variations like translation, frequency-shifting, and noise. His research bridges theoretical mathematics with practical applications across multiple domains: Developing wavelet scattering transforms for robust signal representation Applying these techniques to cryo-EM for molecular structure analysis Creating methods for audio and music classification through time-frequency analysis Designing algorithms for biomedical signal processing, particularly ECG analysis Contributing to computational methods for cosmological parameter estimation His publication record shows consistent contributions in both theoretical signal processing and practical implementations. The research trajectory demonstrates increasing sophistication in applying scattering transforms and deep learning to diverse signal processing challenges across biology, medicine, and acoustics. Notable scientific recognition includes: Best Paper Award (2nd Place) at IEEE International Workshop on Machine Learning for Signal Processing (2015) Best Paper Award (1st Place) at International Conference on Digital Audio Effects (2012) Best Paper Award at IPDPS for cuFINUFFT implementation Professor Andén-Pantera advises graduate students on degree projects in financial mathematics, mathematical statistics, and engineering mathematics. His research group develops computational tools including ASPIRE for cryo-EM and Kymatio for wavelet scattering transforms. He teaches courses in Applied Statistics, Probability Theory, and Statistical Learning at KTH. He leads the development of several influential open-source software projects that have become standard tools in their respective fields, with Kymatio particularly gaining widespread adoption across multiple research communities.
Hao Chen is a researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS), specifically within the Computer Science department focusing on Communication Systems and the Optical Network Laboratory (ON Lab). He also contributes to research in Intelligent systems and Information Science and Engineering. Chen completed his doctoral dissertation titled 'Reliable and Efficient Distributed Machine Learning' in 2022, establishing himself as an emerging expert in distributed machine learning systems. Chen's research interests center around distributed and federated machine learning architectures, with particular emphasis on optimizing communication efficiency in decentralized systems. His work addresses critical challenges in distributed learning including communication bottlenecks, straggler nodes (devices with slow responses), and privacy preservation. His research spans applications in wireless IoT networks, satellite communications, and edge computing environments. His publication record shows a clear trajectory of increasingly sophisticated approaches to distributed machine learning. Starting with foundational work on coded stochastic ADMM methods in 2021, he progressed to developing asynchronous parallel algorithms (2023) and exploring applications in specialized domains like speech fatigue recognition (2024). A consistent theme across his work is the optimization of communication resources while maintaining learning performance, with particular attention to real-world constraints in wireless and satellite networks. Chen has collaborated extensively with researchers including Ming Xiao, Mikael Skoglund, Yu Ye, and others across multiple publications. His research has been supported by funding sources including the EU Horizon 2020 program (grant 825272) and the Swedish Foundation for Strategic Research (APR20-0023). His work appears in high-impact journals including IEEE Transactions on Big Data, IEEE Internet of Things Journal, and IEEE Wireless Communications. As a researcher at KTH, Chen contributes to cutting-edge work at the intersection of machine learning and communication systems, developing techniques that enable efficient distributed intelligence across networked devices while addressing practical constraints of real-world deployment.
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).
Simon Larsson is a Researcher in Social Anthropology at the Center for Multidisciplinary Research on Religion and Society (CRS), Department of Theology, Uppsala University. His work bridges colonial history, Christianity, and the social implications of emerging technologies including AI, nanotechnology, and cybersecurity, with a strong focus on governance frameworks in natural resource management. His research interests reveal a distinctive interdisciplinary trajectory examining how technological systems intersect with religious practices and colonial legacies. Key themes include the anthropology of Christianity in legal-historical contexts (evident in Congo mission studies), digital technology's societal impacts (particularly machine learning's parallels with traditional oracles), and collaborative governance models in environmental conflicts across Sweden and sub-Saharan Africa. Fieldwork spans diverse geographical settings from New Mexico's geological landscapes to Côte d'Ivoire's ritual practices, emphasizing local knowledge systems. Recent publications demonstrate a pronounced shift toward analyzing technology's ethical dimensions through anthropological frameworks, while maintaining continuity in environmental governance research. His 2024-2025 works particularly highlight comparative studies of contingency reduction mechanisms across magical, religious, and algorithmic systems, alongside practical applications in maritime logistics and wildlife management. Larsson's research is primarily funded by the Swedish Research Council, Formas, Mistra, and the Swedish Transport Administration, with additional support from multiple foundations. His collaborative projects frequently involve cross-institutional teams addressing real-world governance challenges in conservation, transportation, and human-technology interaction. As a core member of the Center for Multidisciplinary Research on Religion and Society (CRS), he contributes to an integrated research environment that connects theological studies with anthropological fieldwork and policy-oriented governance analysis, facilitating dialogue between religious traditions and contemporary technological societies.
