Martin Sjölund is an Associate Professor at Linköping University's Department of Computer and Information Science (IDA), part of the Software and Systems (SAS) division. His work focuses on software engineering and cyber-physical systems, with a specialization in Modelica compiler development and open-source simulation tools. He contributes to the OpenModelica project, advancing compiler frameworks, integration with Julia, and standardization efforts. Research interests include compiler design, formal methods, and domain-specific languages. Recent work emphasizes modular compiler architectures, structural variability handling, and interoperability between Modelica and other systems like Julia and Python. He has co-authored over 30 peer-reviewed publications since 2017, with a focus on compiler optimization, co-simulation, and educational applications of Modelica. As part of the SAS division, he collaborates with researchers like Lena Buffoni, Adrian Pop, and Peter Fritzson on projects funded by the MODPROD Center and other initiatives. His contributions to open-source tools have been recognized through conference proceedings and industry partnerships.
Saad Mubeen is a Full Professor of Computer Science at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. He holds a Master's in Electrical Engineering (Embedded Systems) and a PhD in Computer Science and Engineering from Mälardalen University (2014), with a Docent title (2018) focused on vehicular embedded systems. His research emphasizes predictable embedded systems, timing analysis for real-time communication, and component-based software design. Key areas include model-driven development for automotive systems, integration of TSN/5G networks, and fault-tolerant industrial architectures. He has led projects on end-to-end timing analysis in distributed systems, ROS 2 verification, and cognitive edge-cloud scheduling. Publications span 2021–2025, focusing on real-time systems, network protocols (TSN, AVB, 5G), and industrial automation. Notable work includes frameworks for TSN configuration, fault diagnosis tools using NETCONF, and scheduling algorithms for heterogeneous edge-cloud environments. His contributions address critical challenges in timing predictability, security, and resource optimization for cyber-physical systems. Education contributions include problem-based learning modules for vehicular software engineering. He is actively involved in bridging academia and industry through collaborative research on next-generation automotive and industrial systems.
Johan Eker is a Professor at the Department of Automatic Control at Lund University and an Adjunct Professor at ELLIIT: the Linköping-Lund initiative on IT and mobile communication . He is also a member of the LTH Profile Area: AI and Digitalization and LU Profile Area: Natural and Artificial Cognition . His research focuses on control engineering, telecommunications, cloud computing, real-time systems, IoT, and anomaly detection. He actively contributes to UN Sustainable Development Goals through his work. He has received notable awards including the Best paper runner-up award at IEEE CloudNet 2023 , Best Paper Award at RTCSA 2004 , and Best Student Paper Award at RTCSA 1999 . Key projects include: AORTA: Advanced Offloading for Real-Time Applications (2023–2025) ICS: Industrial Cloud Sandbox (2019) AutoDC: Autonomous datacenter for long-term deployment (2018–2021) He has organized workshops such as the Real-Time Cloud Workshop and serves on the advisory board for Internet of Things and People (IoTaP) .
Dr Katerina Cerna is an Assistant Professor (Associate Senior Lecturer) at the School of Information Technology, Halmstad University, Sweden. She leads research on participatory and more-than-human design, embedding learning sciences, sustainability and care ethics into technology development. She coordinates work in the REBEL research programme, is Area Leader for Participation in CAISR Health, and drives projects such as PadAI, Grow 2B Well and PAS-SAP. Education: PhD in Education Sciences, University of Gothenburg Research Interests: Her scholarship converges around enabling meaningful participation in design processes, especially for marginalised or vulnerable groups. Current strands include: Mental-health technology co-designed with young adults (PadAI) Embodied and multispecies engagements with plants for urban wellbeing (Grow 2B Well) Care-full participatory design with older adults (ACCESS, Demokit) Proxy-user methods for hard-to-reach populations (PAS-SAP) Publications at a glance: The 2022-2025 corpus reveals an intensifying focus on mental health support systems , multispecies and more-than-human design , and care-centred participatory methods . Qualitative, ethnographic and design-research methodologies dominate, often applied in contexts of ageing, disability, youth mental health and environmental sustainability. Scientific Awards & Fellowships: No specific awards listed; recognition is implicit through sustained funding (REBEL, CAISR Health, ACCESS, PadAI, PAS-SAP, Grow 2B Well). Advising & Grants: Principal investigator/supervisor on PadAI (participatory AI for youth mental health) Area Leader for Participation – CAISR Health Work-package leader – REBEL programme “Re-imagining Future Smart Living” Coordinator – ACCESS (postdoc project on older adults’ digital literacy) Workshop leader – I.N.S.E.C.T. summer camps on multispecies design Labs & Teams: She is embedded in the REBEL collective – an interdisciplinary group of researchers, designers and practitioners re-imagining inclusive futures. She collaborates closely with the CAISR Health research environment and has ongoing partnerships with Chalmers University of Technology, University of Gothenburg, Umeå University, Bergen University and Salzburg University.
Paolo Monti is a Professor and Head of the Optical Networks Unit at Chalmers University of Technology's Department of Communications, Antennas and Optical Networks. With extensive expertise in optical communication infrastructures, he leads research focusing on energy efficiency, network resiliency, programmability, automation, and techno-economics of optical networks. His work spans multiple international collaborations with funding from major research bodies across EU, USA, and Asia. Professor Monti's research interests center around next-generation optical networking technologies. His work explores the integration of artificial intelligence and machine learning with optical networks, quantum-classical network convergence, 6G infrastructure development, and network automation. His research addresses critical challenges in network energy consumption, reliability under failure conditions, and cost-effective deployment strategies for emerging communication technologies. The research group under his leadership develops frameworks for optical network monitoring, security, and resource optimization using advanced computational techniques. Analysis of his recent publications reveals a strong trend toward AI/ML integration with optical networking, with significant focus on quality of transmission estimation, network automation, and 6G readiness. His work increasingly combines quantum technologies with classical optical networks while addressing practical implementation challenges in multi-band elastic optical networks. The publications demonstrate a progression from theoretical network design to practical implementations with real-world validation. Professor Monti has received recognition as a Senior Member of IEEE, highlighting his contributions to the field of communications and networking. As an academic leader, Professor Monti has been involved as Principal Investigator, co-PI, and main technical leader in numerous national and international projects. His educational contributions include teaching courses at undergraduate, Master's, and PhD levels, as well as developing ICT-focused education programs. His research has been supported by major funding bodies including the European Commission, VINNOVA, and Wallenberg Centre for Quantum Technology. The Optical Networks Unit under Professor Monti's leadership operates as a vibrant research environment focusing on both theoretical and experimental aspects of next-generation optical communications. The unit maintains strong collaborations with industry partners and academic institutions worldwide, participating in multiple EU-funded projects and national initiatives focused on quantum communications and 6G infrastructure.
Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.
James Gross is a Professor at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm. He leads research in mobile systems and networks, with a focus on 5G/6G, edge computing, and performance evaluation. He is Associate Director of KTH's Digital Futures center and a board member of the Innovative Centre for Embedded Systems. Previously, he directed the ACCESS Linnaeus Centre (2016–2019) and was Assistant Professor at RWTH Aachen University. PhD, TU Berlin (2006) Studies: TU Berlin, UC San Diego His research lies at the intersection of wireless networking, edge computing, and mathematical performance modeling. Key areas include ultra-reliable low-latency communications (URLLC), age-of-information, network calculus, and resource allocation. He applies these to 5G/6G, cyber-physical systems, and industrial IoT. His work combines theoretical modeling with real-world implementation and standardization impact. The recent publications highlight a strong focus on deterministic and reliable communications for future networks. Topics include hierarchical inference at the edge, age-of-information optimization, finite blocklength coding, and integration of TSN with wireless systems. There is a clear trend towards AI/ML for resource management and semantic communications, reflecting the evolution of intelligent edge networks. Best Paper Award, ACM MSWiM 2015 Best Demo Paper Award, IEEE WoWMoM 2015 Best Paper Award, IEEE WoWMoM 2009 Best Paper Award, European Wireless 2009 ITG/KuVS Dissertation Award, 2007 James Gross has supervised PhD students such as Samie Mostafavi and advises numerous master's projects. His research has been funded by national science foundations in Germany and Sweden, the ICT TNG SRA, Linnaeus ACCESS Centre, DFG-funded UMIC Centre, German Ministry of Science, and various industry partners. His work has led to patents and influenced wireless standards. He is involved in initiatives like the TECoSA project on trustworthy edge computing and organizes summer schools on Edge AI and 6G. His lab conducts experimental research on edge computing testbeds (e.g., Ainur, ExPECA) and wireless performance evaluation.
Dr. Jesper Andersson is a Professor of Computer Science and Dean of the Faculty of Technology at Linnaeus University. He holds a PhD from Linköping University (2007) and has extensive leadership experience, serving as Department Chair from 2013–2020. His research focuses on self-adaptive software systems, software reuse, and cyber-physical systems. He has published widely in top venues like ACM Transactions on Autonomous and Adaptive Systems and Computing , and actively contributes to organizing international conferences. Education: Responsible for advanced courses in software design and development processes. Engaged with industry through technical advising for global companies. Completed major projects include developing a master’s program in computer science and the PROSSES project on self-protecting systems. Current research projects include Digital Twin of Organizations (DTO), DIACCESS for sustainable cities, and Aladino for adaptable architectures. His work emphasizes resilience frameworks, decentralized control, and industrial adaptation practices. Key collaborations include leading the AdaptWise research group and co-chairing SEAMS 2023. His articles span self-adaptive patterns, trust-aware systems, and IoT applications, reflecting a strong focus on both theoretical and applied software engineering challenges.
Federico Ciccozzi is an Associate Professor in Computer Science at Mälardalen University's School of Innovation, Design and Engineering, where he leads the ASSO research group and the VR ORPHEUS project. He also serves as Head of Research Education in Computer Science and Electronics at the university. His academic journey includes a M.Sc. in Global Software Engineering (via the GSEEM program) and a Ph.D. in Computer Science and Engineering from Mälardalen University (2014), followed by promotion to Docent (Associate Professor) in 2017. Education: M.Sc. in Global Software Engineering (GSEEM program, joint between Mälardalen, L'Aquila, and Amsterdam) Ph.D. in Computer Science and Engineering (Mälardalen University, 2014) Docent (Associate Professor) in Computer Science (Mälardalen University, 2017) His research focuses on model-driven engineering, robotics software engineering, and software architecture. He has pioneered work in blended modeling approaches, EAST-ADL extensions, and formal verification of complex systems, particularly in robotics and automotive domains. Recent projects emphasize industrial collaborations, such as optimizing ROS2 multi-robot systems and enhancing safety-critical software through model-based methodologies. Research Contributions: Developed frameworks for transforming surface languages into augmented EAST-ADL models Advanced blended modeling techniques across JetBrains MPS and Eclipse tools Examined consistency management in industrial model-driven development Edited special issues on model-driven engineering and low-code development His work bridges academic innovation and industrial practice, with notable projects like the Rubus Component Model for vehicular systems. He actively organizes workshops (e.g., RoSE, ASYDE) and contributes to standardization efforts like the Portable Test and Stimulus Standard. Labs/Teams: Leads the ASSO research group, focusing on advanced software engineering methodologies and robotics systems development.
Professor Daniel Axehill is affiliated with the Department of Electrical Engineering (ISY) at Linköping University, specializing in planning and optimization-based control for autonomous systems. His research bridges theoretical developments and industrial applications. His recent work focuses on robust motion planning for autonomous vehicles, optimal task and motion planning algorithms, execution-time analysis for model predictive control, and high-performance solvers for multi-parametric quadratic programming. Key methods include lattice-based planning, disturbance estimation, and real-time optimization. He contributes to the Wallenberg Autonomous Systems Program (WASP), collaborating on advancements in robotics, sensor fusion, and complex network control systems.
Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.
Cristian R. Rojas is a Professor at the Division of Decision and Control Systems within the School of Electrical Engineering and Computer Science at Kungliga Tekniska Högskolan (KTH Royal Institute of Technology) in Stockholm, Sweden. He has been affiliated with KTH since October 2008, advancing from his initial position to his current professorship. His academic career focuses on control theory, system identification, and related fields. Dr. Rojas received his M.S. degree in electronics engineering from the Universidad Técnica Federico Santa María in Valparaíso, Chile, in 2004, followed by his Ph.D. in electrical engineering from The University of Newcastle, NSW, Australia, in 2008. Professor Rojas's research spans system identification, signal processing, and machine learning, with particular emphasis on developing methods for optimal input design, sparse system identification, and continuous-time system modeling. His work bridges theoretical foundations with practical applications in control systems engineering, focusing on creating efficient algorithms for system identification that balance computational complexity with estimation accuracy. He has made significant contributions to understanding coherence properties in system identification and developing methods for unstable system identification in closed-loop configurations. An analysis of Professor Rojas's recent publications reveals a strong focus on sparse system identification techniques, continuous-time system modeling, and application-oriented input design. His work consistently addresses the challenge of balancing theoretical rigor with practical implementation constraints, particularly in the areas of coherence minimization, computational efficiency, and closed-loop system identification. The research demonstrates a clear evolution toward increasingly sophisticated methods for handling nonlinear systems and unstable dynamics while maintaining statistical consistency. Associate Editor for IFAC journal Automatica Associate Editor for IEEE Control Systems Letters (L-CSS) Member of IEEE Technical Committee on System Identification and Adaptive Processing (since 2013) Member of IFAC Technical Committee TC1.1. on Modelling, Identification, and Signal Processing (since 2013) As an educator, Professor Rojas supervises numerous degree projects across various specializations including Machine Learning, Systems Control and Robotics, and ICT Innovation. He teaches core courses such as Machine Learning Theory (EL2810) and Modelling of Dynamical Systems (EL2820), demonstrating his commitment to both theoretical foundations and practical applications in control systems education. His academic leadership extends to course development and examination responsibilities across multiple engineering programs. Professor Rojas is embedded within the Division of Decision and Control Systems at KTH, a research environment dedicated to advancing the theoretical and practical aspects of control theory, system identification, and decision-making systems. His collaborative work with researchers like Håkan Hjalmarsson, James S. Welsh, and others has established him as a key contributor to the international control systems community.
Ibrahim Orhan serves as a Lecturer at KTH Royal Institute of Technology within the Division of Health Informatics and Logistics. His primary responsibilities include teaching core courses such as Communication Networks (HE1033), Mobile Communications and Wireless Networks (HI1035), and Routing in IP Networks (HI2002), while also supervising first-cycle degree projects in Computer and Electrical Engineering. His research centers on wireless sensor networks with specialized applications in healthcare and education. Key focus areas include time synchronization protocols for multi-sensor systems, performance monitoring in contention-based networks, and data fusion techniques for health monitoring applications like fall detection. Recent work demonstrates a strategic expansion into educational technology through serious games for engineering education. Analysis of his 15 most recent publications (2006-2023) reveals an evolving research trajectory: initial work concentrated on fundamental wireless network performance (2008-2011), shifting toward healthcare applications (2012-2016), and culminating in educational technology innovations (2023). Persistent themes include Bluetooth synchronization, mobile sensor integration, and quality-of-service management in resource-constrained environments. Dr. Orhan actively supervises undergraduate degree projects in computer and electrical engineering, though specific student names aren't publicly listed. His research has been supported through KTH institutional channels, with publications spanning IEEE conferences, specialized journals like the International Journal of Serious Games, and collaborative projects focused on ambient assisted living solutions.
Yuan Yao serves as an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. His academic role spans teaching and research within the Computer Systems division, focusing on cutting-edge computer architecture and parallel computing systems. He maintains active collaborations across international institutions, particularly in energy-efficient hardware design and emerging computing paradigms. His educational journey includes: B.S. in Micro-electronics from Northwestern Polytechnical University, China (2009) M.S. in System-on-Chip Design from KTH Royal Institute of Technology, Sweden (2014) Ph.D. in Electrical Engineering and Computer Science from KTH Royal Institute of Technology (2019) Yao's research centers on power and thermal management for chip multi-processors, Network-on-Chips (NoCs), and GPUs. He pioneers hardware/software co-design for high-performance computing, coherency mechanisms for emerging memory technologies, and performance analysis of on-chip networks. Recent work expands into neural network acceleration and battery-less Internet of Things architectures, reflecting a trajectory toward energy-constrained specialized systems. His methodology integrates formal modeling with practical implementation for real-world impact. Publication trends reveal consistent innovation in energy efficiency across parallel architectures. From foundational DVFS techniques for NoCs (2016-2018) to recent breakthroughs in battery-less IoT (2023-2024), his work demonstrates evolutionary progression toward novel computing domains. Key thematic threads include thermal-aware optimization, memory consistency protocols, and hardware acceleration for AI workloads, with applications spanning data centers to embedded systems. Scientific recognition includes: Best paper candidate at IEEE International Symposium on High Performance Computer Architecture (HPCA) 2018 for in-network packet generation research Yao actively supervises graduate researchers and leads collaborative projects in computer architecture. His grant portfolio supports work on battery-less IoT systems and neural network accelerators, though specific funding details aren't publicly enumerated. Current projects emphasize sustainable computing through novel architectures for energy-harvesting environments. He operates within Uppsala University's Computer Systems division, contributing to research groups focused on hardware acceleration, embedded systems, and networked architectures. His lab environment fosters interdisciplinary work bridging computer architecture, energy harvesting, and machine learning for next-generation computing platforms.
Jorge Solis is an Associate Professor and Docent in Electrical Engineering at Karlstad University, Sweden. He specializes in robotics, automation, and renewable energy integration. His research focuses on assistive robots, biologically-inspired control systems, and energy storage solutions. He collaborates with industries like ABB, Camanio Care AB, and international institutions such as the University of Southern Denmark and Waseda University. He has authored over 150 publications, including peer-reviewed journals and conference papers, with a focus on human-robot collaboration, solar energy systems, and medical robotics. Key projects include gesture-based cobot programming, greenhouse energy optimization, and autonomous UAV monitoring systems. His work has earned finalist awards at major robotics conferences and a best paper award in medical engineering. Jorge teaches courses in industrial automation, control systems, and embedded control at both bachelor’s and master’s levels. He leads research teams and chairs technical committees in bio-robotics. His contributions span academia-industry partnerships, emphasizing practical applications in healthcare, agriculture, and energy sectors.
