Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
Johan Sidén is a Lecturer and Associate Professor at Mid Sweden University , employed in the Department of Computer and Electrical Engineering (DET) . His work focuses on RFID technology , antenna design , and printed/flexible electronics , with a particular emphasis on industrial IoT and welfare technology applications. Research Keywords : Radio Frequency Identification, Antenna Design, Flexible Electronics, Wireless Sensor Networks, Microwave Engineering, Electronic Design Key Projects : DRIVEN (data-driven industrial transformation), SmartArea (functional surfaces), Pressure (ulcer monitoring), MakeSense! (welfare technology) Publications : 15+ recent works on wearable antennas, smart packaging, UWB antenna design, and RFID sensor integration Collaborations include partnerships with industrial and academic institutions, focusing on sustainable electronics, sensor systems, and smart infrastructure. His technical expertise spans antenna optimization , printed circuits , and edge computing for harsh environments.
Professor Ola Isaksson is a faculty member in Product Development at Chalmers University of Technology, where he leads the Systems Engineering Design research group. With over 40 research projects nationally and internationally, his work bridges academic research and industrial application, particularly in aviation and transport-related manufacturing sectors. His expertise spans digitalization, sustainability, and advanced manufacturing methods in product development. Ola Isaksson received his PhD in Computer Aided Machine Design from Luleå University of Technology in 1999. Prior to his academic career, he had a specialist career at GKN Aerospace Engine Systems (formerly Volvo Aero) in Trollhättan, focusing on design and product development until 2015. Professor Isaksson's research focuses on developing new product development capabilities to address societal and industrial needs through digitalization and advanced manufacturing. His primary interests include platform-based development, Set Based Engineering, multidisciplinary engineering methods, Value-driven development, and knowledge-intensive system support. He has particular expertise in additive manufacturing integration, design space exploration, and sustainability transition in product development. Analysis of Professor Isaksson's recent publications reveals a strong focus on integrating digital technologies with sustainable manufacturing practices. His work demonstrates a progression from traditional design methodologies toward AI-assisted design, digital twins, and advanced data analytics. Key thematic areas include additive manufacturing implementation, design margin management, sustainability integration, and aerospace component optimization, reflecting his commitment to bridging theoretical research with industrial applications. Professor Isaksson is one of the founders of the Swedish Product Development Academy and maintains active membership in the Design Society, ASME, and SIG PM, reflecting his significant contributions to the field of engineering design. With over 100 scientific publications and leadership in more than 40 research projects, Professor Isaksson has established himself as a leading figure in product development research. His work frequently involves close collaboration with industry partners, particularly in the aviation sector, securing substantial research funding for projects addressing digitalization, sustainability, and advanced manufacturing challenges. Professor Isaksson leads the Systems Engineering Design research group at Chalmers University of Technology. His team focuses on developing methodologies for complex product development, with particular emphasis on digital tools, sustainability integration, and manufacturing innovation. The group maintains strong industry connections, especially with aerospace manufacturers, facilitating the translation of research into practical applications.
Stefano Bonetti is an Associate Professor in the Department of Physics at Stockholm University , leading the Ultrafast Condensed Matter Dynamics Group . His research focuses on manipulating quantum materials using terahertz (THz) and near-infrared laser fields to study spin dynamics and ultrafast phenomena at nanoscale and femtosecond timescales. PhD in Materials Physics (KTH Royal Institute of Technology, Sweden) MSc in Engineering Physics (KTH) BSc in Technical Physics (Politecnico di Milano, Italy) Recent research efforts involve time-resolved X-ray microscopy to visualize spin currents and magnetization dynamics, leveraging facilities like free-electron lasers. His work bridges experimental physics and applied materials science, aiming to enhance energy efficiency in data storage technologies by understanding ultrafast spin-lattice interactions . Key scientific awards and grants: ERC Starting Grant (2017-2021) Wallenberg Academy Fellow (2018-2023) VR's free grant (2019-2023) International Career Grant (COFUND) (2015-2019) He has contributed to developing THz-based techniques for magnetic control and authored foundational work on spin-wave solitons and nonlinear magnetoelastic coupling . His group collaborates internationally, utilizing advanced synchrotron and free-electron laser facilities.
Lars Lund is a Professor/Senior Physician at Karolinska Institutet's Department of Medicine, Solna, leading research on heart failure mechanisms and clinical management. He specializes in heart failure with reduced and preserved ejection fraction (HFrEF/HFpEF), focusing on translational research, clinical trials, and registry-based studies. His work bridges molecular biology, epidemiology, and clinical practice, aiming to improve treatment strategies and patient outcomes. Key affiliations include the Swedish Heart Failure Registry (SwedeHF) and leadership of the Heart Failure Research Group. Education: Economics (College of Charleston, USA), Medicine (Duke University), Molecular Biology PhD (Karolinska Institutet, 2005). Roles: Professor of Cardiology since 2018, Associate Professor (2011–2018), and clinical training at Columbia University and Karolinska University Hospital. Research interests include new treatments for HFrEF, mechanisms of HFpEF, and optimization of guideline-directed therapy. His team investigates drug repurposing, biomarkers, and quality of life in heart failure patients. Notable grants include the Swedish Research Council-funded SPIRRIT-HFpEF trial and studies on inotropic therapies. Recent publications emphasize clinical trial design, registry analyses, and therapeutic gaps in heart failure subtypes. He was recognized as a Clarivate Highly Cited Researcher in 2024 for impactful contributions to cardiology. Advises over 10 PhD students and leads multi-institutional collaborations in Europe and globally. His lab focuses on translational research, bridging basic science and clinical application in heart failure management.
Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.
Feras M. Awaysheh is an Associate Professor at the Department of Computing Science , Umeå University , Sweden. He leads the Autonomous Distributed Systems Lab (ADSLab) and focuses on research areas including Edge AI , Federated Learning , Distributed Data Privacy , Cloud Computing , and Big Data (BD). Affiliation : Department of Computing Science, Umeå University Research Leadership : ADSLab His research explores: Edge AI for decentralized intelligence Federated Learning architectures Distributed Data Privacy mechanisms Cloud Computing scalability Big Data resource allocation Recent publications focus on: Metaheuristic optimization for cloud systems Secure client selection in federated learning Multi-objective scheduling in IoT environments Elastic resource allocation frameworks He works in the MIT House (room MIT.B.225), Umeå, Sweden (901 87).
Katalin Dobra is Professor and Senior Consultant in Molecular Pathology at Karolinska Institutet's Department of Oncology-Pathology, where she leads the Molecular Pathology of the Lung and Pleura research group. Her work bridges clinical pathology with cutting-edge molecular research focused on lung cancer and pleural diseases. Her educational background includes: 1994 MD; Summa Cum Laude, Semmelweis University Budapest, Hungary 2002 PhD Experimental Pathology, Karolinska Institutet 2008 Specialist in Clinical Pathology and Cytology, Karolinska University Hospital 2010 Associate Professor in Pathology 2014-2020 Senior Lecturer in Molecular Pathology 2021 Adjunct Professor in Clinical Pathology Dr. Dobra's research centers on optimizing integrated strategies for personalized cancer treatment through computer-assisted tumor grading, molecular mapping, and biomarker validation. Her team integrates and validates diagnostic and predictive biomarkers through comprehensive molecular characterization of lung tumors, including primary lung cancer, malignant mesothelioma, and pleural and brain metastases of non-small cell lung cancer. They develop methods for early diagnosis using molecular characterization of soluble proteins, free DNA, and exosomes from exudate and peripheral blood. Recent work combines artificial intelligence with morphological image analysis in digital pathology to better stratify patients with aggressive tumor phenotypes. Analysis of her recent publications reveals a strong focus on biomarker validation, particularly PD-L1 expression, mesothelioma diagnostics, and the role of syndecan-1 in tumor biology. Her work spans molecular characterization, diagnostic techniques, and therapeutic applications, with increasing emphasis on AI-assisted diagnostics and personalized treatment approaches. Her scientific recognition includes: 2008 Young Investigator award, KI Founds 2017 National Molecular Pathology Personalized Cancer Medicine Award from Astra Zeneca 2017 Magnus Nasiel Stipend 2021 Magnus Nasiel Stipend As an educator, Dr. Dobra has extensive experience teaching at undergraduate, graduate, and postgraduate levels. She designed and launched the master's program in diagnostic cytology and currently integrates pathology into Karolinska's six-year medical program. Her research group receives funding from multiple sources including The Cancer and Allergy Foundation, which recently awarded SEK 2.3 million to KI researchers. Her Molecular Pathology of the Lung and Pleura research group operates within Karolinska Institutet's Department of Oncology-Pathology, focusing on discovering, integrating, and validating novel diagnostic and predictive biomarkers. The team studies fundamental molecular mechanisms of tumor invasion and resistance development while conducting ex-vivo testing of tumor properties with ultrasensitive imaging analysis.
Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.
Fredrik Sandin is a Professor in the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology, where he leads the Machine Learning research group with approximately thirty members. His work focuses on neuromorphic technologies and the intersection of machine learning with computational physics to solve challenging real-world interaction problems. He coordinates the 'Teknisk fysik och elektroteknik' program at LTU and has been instrumental in establishing neuromorphic research activities at the university. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering Member of WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIS (European Laboratory for Learning and Intelligent Systems) Coordinator of Neuromorphic Innovation Platform Sweden with KTH, Lund University, Uppsala University, FOI, ABB, Ericsson, and SAAB Fredrik earned his PhD in Physics from Luleå University of Technology in 2007, with thesis work focusing on dense states of matter in neutron stars. His academic journey began with an MSc diploma work in ATLAS at CERN in 2001, followed by postdoctoral research in computational physics at IFPA in Belgium (2008-2009) and brain-like computing at EISLAB with Prof. Jerker Delsing (2010-2011). Professor Sandin's research interests center around neuromorphic technologies, particularly neuromorphic computing and spiking neural networks. He investigates sensor/detector and intelligent systems co-design where constraints like energy, power, latency, and dynamic range challenge conventional digital approaches. His work spans mixed-signal neuromorphic circuits, algorithms, and systems, as well as machine learning projects involving industrial data and collaboration. He has been a key figure in establishing neuromorphic research at LTU, supported by The Kempe Foundations, particularly through the 2014 Gunnar Öquist Fellowship. His recent publications demonstrate a strong interdisciplinary focus spanning quantum phase transitions, particle physics detector optimization, renewable energy materials, and the integration of large language models into control systems. This diverse portfolio reflects his approach connecting machine learning with fundamental physics and practical engineering applications, particularly in neuromorphic computing and intelligent systems design, with emphasis on solving real-world problems through co-design of hardware and algorithms. Gunnar Öquist Fellowship Award and 3 MSEK grant from The Kempe Foundations ISSP award for an Original Work in Theoretical Physics (signed by Prof. 't Hooft and Prof. Zichichi) New-Talents award for original work in theoretical physics at the International School of Subnuclear Physics in Erice Professor Sandin has supervised numerous PhD students working on topics ranging from neuromorphic TinyML to materials for neuromorphic computing, privacy-preserving machine learning at the edge, and intelligent fault diagnosis. He has secured substantial research funding from various sources including Vinnova, ÅForsk, Kempe Foundations, WASP-WISE, and EU programs like ECSEL JU Arrowhead Tools and ITEA3 AutoDC. His current major projects include the Neuromorphic Innovation Platform Sweden and several initiatives focused on neuromorphic condition monitoring and computing, with total funding exceeding 30 MSEK in the past five years. He leads the Machine Learning group at LTU, which collaborates extensively with industry partners including ABB, Ericsson, SAAB, SKF, and RISE. The group is active in developing neuromorphic technologies for wireless sensor networks, condition monitoring systems, and next-generation intelligent systems that address energy, power, and latency constraints that challenge conventional digital approaches.
Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.
Dr. Muhammad Humayoun is a Senior Lecturer at Karlstad University, Sweden, specializing in informatics education and computational linguistics research. He holds a Ph.D. from University of Grenoble Alpes (2012) and M.Sc. from Chalmers University (2006). His academic career spans over 15 years across Pakistan and Sweden, including teaching roles at COMSATS University, University of Central Punjab, and Higher Colleges of Technology. Research Focus: His work centers on NLP applications for under-resourced languages like Urdu/Punjabi, including text summarization, abusive language detection, and formalization of mathematical texts. He has developed linguistic resources (corpora, lexicons) and contributed to plagiarism detection systems in programming courses. Teaching Contributions: Designed and taught over 15 courses across multiple universities, including advanced programming, AI, and cloud computing modules. Currently teaches graduate-level courses like 'Legal Tech, AI and Rules-as-Code' and undergraduate software engineering courses at Karlstad University. Awards: Recognized for Urdu threat detection system (3rd place, 2021) and top rankings in Urdu fake news competitions. His work on Urdu summarization corpora has been widely cited in computational linguistics research.
Anna Brunström is a Full Professor and Head of the Distributed Intelligent Systems and Communications Research Group (DISCO) at Karlstad University's Department of Computer Science. She holds a part-time role as a Researcher at the University of Malaga's Institute of Software Engineering and Technologies (ITIS). Her research focuses on computer networking, Internet architectures, low latency communication, and 5G/6G mobile systems. She leads the nationally funded DRIVE initiative and collaborates on European projects like 6G-PATH. She actively contributes to IETF standardization, notably as a former rmcat WG chair. Her work spans over 200 publications, emphasizing network measurement, latency optimization, and multipath protocols. Education: Ph.D. (1996) and M.Sc. (1993) from College of William & Mary, B.Sc. (1991) from Pepperdine University. Research Interests: Distributed systems, IoT networking (NB-IoT), satellite communication (Starlink), machine learning for positioning, and transport protocols (QUIC, MPTCP). Recent work includes latency-aware scheduling, 5G/6G performance analysis, and edge computing frameworks. Publications highlight trends in: 1) Satellite network throughput modeling, 2) 5G/6G architecture validation, 3) Machine learning applications for positioning and network analysis, 4) Cross-layer optimization of latency-critical services. Collaborations with industry and academia drive applied research in smart grids, healthcare, and automotive communication. Labs/Teams: DISCO group at Karlstad University, leading the DRIVE research profile and 6G-PATH consortium involvement.
Joakim Jaldén is a Professor at the Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He holds a Ph.D. in Electrical Engineering from KTH (2007) and completed post-doctoral studies at Vienna University of Technology (2007-2009). With affiliations at Stanford University and ETH Zürich, his academic journey reflects global expertise. 2002: M.Sc. in Electrical Engineering, KTH 2007: Ph.D. in Electrical Engineering, KTH 2007-2009: Post-Doctoral Researcher, Vienna University of Technology Jaldén's research spans Signal Processing , Wireless Communications , and Biomedical Data Analysis . He pioneered MIMO communications and later developed ELISpot/FluoroSpot analysis algorithms commercialized by Mabtech AB. His work on cell migration tracking (IEEE ISBI 2012) and distributed optimization (ECO-PANDA method) demonstrates interdisciplinary impact. Key publication trends include Hidden Markov Models for DNA sequencing, Reinforcement Learning in communication systems, and Low-Complexity Beamforming for MU-MIMO networks. His 2024 work on mmWave MIMO beam coherence showcases continued leadership in wireless channel modeling. Scientific recognition includes: IEEE Signal Processing Society 2006 Young Author Best Paper Award Ingvar Carlsson Career Award 2009 (Swedish Foundation for Strategic Research) IEEE ISBI 2012 Best Paper Award Bitplane Awards (2013-2015) for cell tracking challenges As Program Director of KTH's 5-year Electrical Engineering Degree Program (CELTE) since 2016 and Vice-Chair of EECS Faculty Board , Jaldén leads academic initiatives. His collaborations with industry (e.g., Mabtech AB) and roles as examiner for advanced courses in communication systems highlight his educational impact.
Emil Björnson is a Professor of Wireless Communications and Head of the Communication Systems Department at KTH Royal Institute of Technology since 2024. He received his Master of Science in Engineering Mathematics from Lund University (2007) and PhD in Telecommunications from KTH (2011). After postdoctoral work at SUPELEC, France (2012-2014), he held faculty positions at Linköping University (2014-2021) before returning to KTH in 2020. Research Focus: MIMO communications, reconfigurable intelligent surfaces, radio resource allocation, machine learning for communications, and energy efficiency Editorial Roles: Editor for multiple IEEE transactions and magazines His research has significantly advanced wireless communication technologies, particularly in Massive MIMO and cell-free systems. He has authored four textbooks, including Massive MIMO Networks (2017) and Introduction to Multiple Antenna Communications and Reconfigurable Surfaces (2024). Scientific awards include: IEEE Fellow Clarivate Highly Cited Researcher Wallenberg Academy Fellow Digital Futures Fellow Multiple IEEE and EURASIP awards (2014-2024)
