Jorge Fernandez Pendas is a Researcher at the Applied Quantum Physics Division , Chalmers University of Technology , Sweden. He is affiliated with the Quantum Computer Theory Group at the Wallenberg Centre for Quantum Technology and participates in the European consortium OpenSuperQ for open superconducting quantum computers. His research focuses on modeling and optimal control of superconducting qubits , with expertise in quantum anomalies , quantum transport phenomena , and topological matter . He received his PhD in Theoretical Physics from the Universidad Autónoma de Madrid in 2019. Quantum Computing Theoretical Physics Quantum Control Topological Materials Quantum Transport Phenomena Computational Physics His recent work includes optimizing superconducting qubit gates , mitigating frequency collisions , and analyzing quantum dissipation effects . He collaborates with institutions like Volvo Group , Lund University , and RISE Research Institutes of Sweden .
Fredrik Gunnarsson is an Adjunct Professor at Linköping University's Department of Electrical Engineering (ISY), specializing in Automatic Control (RT). His research focuses on control systems, signal processing, and sensor fusion, with applications in positioning, navigation, and optimization for autonomous systems. University: Linköping University Department: Department of Electrical Engineering (ISY) Academic Rank: Adjunct Professor Research Interests Fredrik's work spans advanced signal processing, sensor fusion, and control algorithms. He contributes to 5G positioning, Gaussian process state-space models, and indoor localization. His research intersects with optimization-based control for autonomous systems and machine learning techniques in dynamic environments. Article Trends His recent publications emphasize wireless positioning, statistical filtering, and sensor fusion methodologies. Topics include Kalman filtering, Gaussian process modeling, particle methods for indoor navigation, and optimization algorithms for control systems. Keywords span Control Systems , Signal Processing , Machine Learning , and Wireless Communication . Labs and Collaborations LINK-SIC - Linköping Center for Sensor Informatics and Control Automotive Systems research SEDDIT competence center
Björn Wallner is a Professor and Head of Division at the Department of Physics, Chemistry and Biology (IFM) at Linköping University. His research focuses on computational structural bioinformatics, protein structure prediction, and dynamics, leveraging AI and machine learning. He is renowned for pioneering multiple sequence embeddings foundational to AlphaFold and contributions to the Rosetta software suite. His work integrates experimental data with computational methods to study proteins like MexR (antibiotic resistance regulator) and the disordered Myc protein-Pin1 interaction. Key innovations include error prediction tools for structural models and advancements in AlphaFold for large protein complexes. Research interests span protein modeling, AI-driven structural biology, and applications in drug discovery. Notable contributions include AFsample2 for ensemble predictions, MassiveFold for large protein structures, and integrating experimental constraints into AlphaFold. His methods have improved molecular replacement in crystallography and protein-protein docking. Wallner collaborates with SciLifeLab Linköping, advancing data-driven life science research. His recent work highlights advancements in multimeric complex modeling, structural variation analysis, and the mechanistic understanding of proteins involved in cancer and antibiotic resistance. These contributions underscore the intersection of computational methods and experimental biology in advancing structural biology's frontiers.
Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.
Associate Professor Daniel Jönsson works at the intersection of visualization and machine learning at Linköping University's Department of Science and Technology (ITN) and Media and Information Technology (MIT) division. He leads research in transforming complex datasets into intuitive visual representations, with applications in medical imaging and neuroscience. As a core developer of the Inviwo visualization software, he emphasizes interdisciplinary collaboration across fields like AI and radiology. His work includes developing tools like VisualNeuro for brain cohort studies and exploring neural network interpretability. Research focuses on visualization techniques for high-dimensional data, including fMRI analysis and neural network exploration. Notable achievements include an honorable mention at the IEEE VIS conference (2016) for brain imaging visualization work. Collaborations with CMIV (Center for Medical Image Science and Visualization) highlight his commitment to clinical and medical research applications. Publications emphasize combining visualization with machine learning, addressing challenges in data readiness for AI projects and improving neural network analysis. His contributions span software development, algorithm design, and cross-disciplinary methodological advancements in data-driven decision-making.
Andreas Tillmar is an Adjunct Associate Professor and Docent at Linköping University, affiliated with the Department of Biomedical and Clinical Sciences (BKV) and the Faculty of Medicine and Health Sciences (MEDFAK). He is part of the Strategic Research Area in Forensic Sciences, a collaboration between Linköping University and the National Board of Forensic Medicine in Sweden. His research focuses on advancing DNA methods for forensic applications, including distant kinship analysis, identification of human remains, and predicting phenotypic traits from DNA. Key achievements include developing the FORCE method for sensitive DNA analysis and solving the 2004 Linköping double murder case using forensic genetic genealogy. Research Interests Enhanced DNA Analyses (e.g., FORCE method) Forensic Investigative Genetic Genealogy (FIGG) Population Genetics (e.g., Swedish and northern Swedish populations) Facial Prediction from DNA Media & Outreach Featured in SVT and Universitetsläraren for contributions to forensic DNA techniques and solving high-profile cases. Project on predicting facial images from DNA, supported by the Strategic Research Area. Grants & Collaborations Active in interdisciplinary projects, including international collaborations for mass victim identification and forensic method development. His work bridges academic research and practical crime-solving applications. Labs/Teams Associated with the Division of Molecular Medicine and Virology (MMV) and the Department of Biomedical and Clinical Sciences (BKV) at Linköping University.
Ming Zhao is a Lecturer at the University of Gävle's Academy of Technology and Environment, teaching industrial economics, statistics, reliability engineering, and logistics simulation. Research focuses on reliability modeling, statistical methods, and quality management, with applications in software, manufacturing, and sensor networks. Key interests include masked data analysis for reliability estimation, copula-based modeling of dependent failures, and Bayesian methods for system reliability. Publications emphasize practical frameworks for software reliability and manufacturing system performance. Article trends show consistent innovation in reliability estimation techniques, particularly for systems with incomplete data. Recent work integrates statistical methods with industrial applications like reconfigurable manufacturing and wireless sensor networks. No awards, supervised students, or lab details are documented.
Ingemar André is a Professor at Lund University in the Department of Biochemistry and Structural Biology, affiliated with the eSSENCE: The e-Science Collaboration. His research integrates computational and experimental methods to study protein structure, evolution, and self-assembly. Key areas include AI-driven protein structure prediction, high-throughput protein stability analysis, and synthetic biology applications. Education: No specific academic degrees listed in the provided text. Research Interests: Dr. André’s work focuses on understanding protein interactions, evolution, and self-assembly pathways through computational modeling and experimental techniques. He develops AI-based tools for protein design and simulates evolutionary processes to predict structural changes. His experimental work includes high-throughput methods for characterizing protein biophysical properties in vivo. Recent Projects: Optimizing codon sequences for recombinant protein production (2023–2026, Novo Nordisk Foundation). Massively parallel protein stability measurements for evolutionary studies (2023–2027, Swedish Research Council). Awards: Sven and Ebba-Christina Hagberg Prize (2015). The Svedberg Prize (2015). Advising & Grants: Leads multiple research initiatives, including Synbio@Lund, a synthetic biology theme at the Pufendorf Institute for Advanced Studies. His grants focus on protein engineering and computational methods. Labs/Teams: Collaborates with interdisciplinary groups in synthetic biology and e-Science, emphasizing sustainable applications of bioengineering.
Sindri Magnússon is an Associate Professor and Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), part of the Data Science Research Group. He holds a B.Sc. in Mathematics from the University of Iceland (2011), a Master’s 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 a postdoctoral fellowship at Harvard University (2018–2019) and was a visiting PhD student there in 2015–2016. His research focuses on distributed optimization, decision-making, and machine learning in cyber-physical systems and IoT. Key projects include AI-driven equitable decision-making, smart converter control for renewable energy systems, federated reinforcement learning, and sustainable data-driven algorithms. His work bridges core data science with applications in critical infrastructures, energy systems, and societal challenges. Recent articles explore multi-objective optimization for satellite scheduling, federated learning for privacy-preserving AI, and reinforcement learning in non-stationary environments. His contributions span theory and practice, addressing scalability, sustainability, and ethical AI. He is actively involved in the Data Science Research Group, which develops algorithmic methods for decision-making in smart cities and energy systems. No awards or grants are explicitly listed, but his projects suggest significant research funding and collaboration.
Stefan Engblom is a Professor in Scientific Computing at Uppsala University, Department of Information Technology. His research focuses on developing computational methods and software for scientific applications, with particular expertise in numerical methods, fast algorithms, and simulation of complex systems. He is affiliated with the Scientific Computing division within the Information Technology department at Uppsala University. Engblom's research spans several key areas including fast multipole methods for efficient computation of long-range interactions, stochastic simulation of reaction-diffusion processes in complex geometries, computational epidemiology for modeling disease spread, and fluid dynamics simulations. His work combines theoretical development with practical implementation, resulting in several widely used software packages. His publications reveal a consistent focus on developing efficient numerical algorithms that address computational challenges in various scientific domains. The research shows a progression from fundamental algorithm development (fast multipole methods) to applications in biology (reaction-diffusion systems) and public health (epidemic modeling), demonstrating the versatility and applicability of his computational approaches. Engblom maintains active software development through several research codes including SimInf for epidemic modeling, URDME for reaction-diffusion processes, FMM2D/FMM3D for fast multipole methods, and other specialized tools for fluid dynamics and fiber simulations. His stenglib provides general-purpose Matlab libraries used in his research and available to the broader scientific community.
Erik Elmroth is a Professor at the Department of Computing Science, Umeå University. He leads research in distributed systems, cloud/edge computing, and autonomous resource management, directing a 30+ member research group. His leadership includes transformative roles as department head (2009-2021) and Deputy Director at High Performance Computing Center North (HPC2N). Elmroth serves on executive committees for the SEK 6.2B Wallenberg AI program (WASP) and SEK 390M eSSENCE initiative, and leads multiple Kempe Foundation projects. Research interests center on: Autonomous control of cloud/edge infrastructures Software-defined systems and federated clouds AI-driven resource optimization High-performance computing architectures Robust machine learning for distributed environments His publications emphasize adaptive cloud systems, anomaly detection, and federated learning, with consistent focus on scalability and resilience in edge/cloud deployments. Awards and honors: Member of Royal Swedish Academy of Engineering Sciences (IVA) Nordea Scientific Prize (2011) SIAM Linear Algebra Prize (2000) National HPC Lecturer appointment He has supervised 40+ PhD students and secured major grants including the Swedish Research Council's second-largest award for the Cloud Control project. Elmroth founded the Control Workshops series and co-founded Elastisys AB, a cloud security firm with 50+ employees recognized as Umeå's Spin-off Company of the Year.
Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.
Richard James Glassey is a University lecturer at the Royal Institute of Technology (KTH) in Stockholm, Sweden, working in the Department of Theoretical Computer Science. His office is located at Lindstedtsvägen 5, Floor 5, and he can be contacted via phone at +46 8 790 69 91. His research interests include: Theoretical Computer Science Algorithms and Data Structures Programming Computer Science Education Parallel Programming Dr. Glassey teaches across the computer science curriculum from foundational to advanced levels, demonstrating expertise in both theoretical concepts and practical implementation. His teaching spans introductory programming, algorithms, data structures, and specialized topics in computer science education. He holds significant teaching responsibilities across multiple courses, often serving in dual roles as both teacher and course coordinator or examiner. This indicates leadership within the department's educational framework and deep involvement in curriculum development and assessment.
Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.
