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Ola RingdahlView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +6 more

Ola Ringdahl is an Associate Professor at the Department of Computer Science, Umeå University, specializing in robotics for agriculture and forestry. He serves as the Director of the MSc program in AI and teaches multiple courses. Research Group: Intelligent Robotics Affiliation: Umeå University, Department of Computer Science His research focuses on developing novel artificial intelligence and machine learning algorithms for autonomous and semi-autonomous robotic navigation, industrial manipulator path planning using 3D localization, and perception algorithms for semantic data extraction from sensors. Projects include EU initiatives Sweeper and CROPS for fruit harvesting robots. Publications highlight applications in precision agriculture/forestry and sensor fusion. Recent publications analyze advancements in agricultural robotics using machine learning, human-robot interaction via multi-camera systems, and grape vine detection with RGB-D data. The majority of his work addresses outdoor environments and sensor-driven decision-making in autonomous systems. Key Collaborators: Polina Kurtser, Thomas Hellström, Yael Edan Ola leads the Intelligent Robotics group and has participated in long-term projects like PRECISION (2018-2021), SOCRATES (2016-2020), Sweeper (2015-2018), and CROPS (2010-2014). His work combines academic research with practical implementation in real-world agricultural and forestry settings.

Paolo BientinesiView profile

Professor

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +8 more

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

Roman IakymchukView profile

Associate Professor

• Numerical Linear Algebra
• Parallel Programming
• High Performance Computing
• +3 more

Roman Iakymchuk is an Associate Professor at the Department of Computing Science within Umeå University, Sweden. His work focuses on numerically reliable and sustainable algorithmic solutions, emphasizing accuracy, reproducibility, and energy efficiency in high-performance computing environments. Research Interests: Numerical linear algebra, parallel programming models, sustainable computing, and optimization of distributed systems. Teaching: Courses on scientific computing, CUDA/OpenMP/MPI-based parallel programming, and floating-point error correction. The articles highlight his contributions to mixed-precision computing, numerical reliability in Krylov solvers, and energy-efficient practices in HPC systems. His work spans algorithm design, tool-assisted implementation, and European collaborative projects like CEEC.

Linnea ÖhlundView profile

Researcher

• Social Justice
• Human-Computer Interaction (HCI)
• Digital Equity
• +3 more

Linnea Öhlund is a Researcher at the Department of Informatics , Umeå University . Her work focuses on leveraging digital technology to address structural and systemic injustices, particularly through participatory methods. She is affiliated with the Design Informatics Research Group and actively contributes to advancing equity in human-computer interaction (HCI). Research Interests: Öhlund investigates how digital systems can either exacerbate or mitigate stigmatization, marginalization, and discrimination. Key areas include social justice in technology design , digital equity , participatory methodologies , and vulnerable populations . Her projects often intersect with gendered violence , ageing populations , and socio-technical systems . Recent Publications: Her work spans critical analyses of persuasive systems, pandemic-era technology use, and digital injustices in trafficking. She engages with interdisciplinary frameworks, emphasizing ethical design and societal inclusion .

• Pharmaceutical Sciences
• Pharmacology and Toxicology
• Clinical Pharmacology
• +4 more

Marine Andersson is a Senior Lecturer at the Department of Laboratory Medicine, Karolinska Institutet, with a focus on Pharmaceutical Sciences and Pharmacology & Toxicology. She holds a PhD (2014) and a Docent degree (2024) from Karolinska Institutet. Specializes in drug-drug interactions, anticoagulant pharmacology, and clinical decision support systems Developed and evaluated tools like Janusmed Risk Profile and SFINX database Active in teaching, including the course 'Introduktion till R' at Karolinska Her research interests center on clinical pharmacology, particularly interactions involving warfarin and their implications in polypharmacy and geriatric populations. She utilizes Swedish national health registers and real-world data to validate clinical decision support systems (CDSS) for pharmacological risk assessment. Her work bridges pharmaceutical sciences with medical informatics to improve medication safety. Recent publications highlight her use of population-based studies to analyze drug interactions in acute respiratory distress syndrome, postpartum pharmacology, and elderly polypharmacy. Key subfields include pharmacogenomics (CYP2C9 variants), critical care pharmacology, and real-world data integration in CDSS frameworks. While no explicit scientific awards are listed, her international collaborations and methodological innovations in drug interaction databases are notable. Marine Andersson contributes to public outreach and serves as a faculty resource for pharmacological risk assessment, with affiliations spanning clinical pharmacology divisions at Karolinska University Hospital and Huddinge campus.

• HIV Vaccine Research
• Clinical Microbiology
• Immunology
• +3 more

Charlotta Nilsson serves as an Affiliated Researcher and Docent at Karolinska Institutet's Department of Laboratory Medicine, specifically within the Division of Clinical Microbiology at Labmed. Her research spans HIV vaccine development, diagnostic testing, and immunological responses in global health contexts, with extensive fieldwork in Sub-Saharan Africa. She maintains active collaborations across Tanzania, Mozambique, and Sweden. Docent, Karolinska Institutet (2015) Doctor of Philosophy, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet (2001) Dr. Nilsson's research focuses on HIV vaccine immunogenicity, with particular expertise in prime-boost regimens (DNA/MVA), envelope-specific antibody responses, and diagnostic interference from vaccine-induced seroreactivity. Her work frequently addresses challenges in resource-limited settings, including rapid test validation, co-infection dynamics (HIV/HBV), and implementation barriers for vaccination programs. Recent expansions include SARS-CoV-2 immune response characterization following heterologous vaccination. Analysis of her 15 most recent publications reveals dominant themes in HIV envelope immunology (particularly V1V2 responses), vaccine trial methodology in African populations, and diagnostic assay validation. The work consistently bridges basic virology with clinical implementation, emphasizing translational research that addresses real-world diagnostic and vaccination challenges in endemic regions. Her laboratory-based research operates within Karolinska Institutet's Department of Laboratory Medicine infrastructure, leveraging advanced virology and immunology platforms. Collaborative networks include the Karolinska University Hospital's clinical microbiology division and international partnerships with research groups in Tanzania (Muhimbili University) and Mozambique (National Institute of Health).

• Dynamical Systems
• Number Theory
• Machine Learning
• +6 more

Karl-Olof Lindahl is a Professor in Mathematics at Linnaeus University , Sweden. He has held postdoctoral positions at Brown University, Pontífica Universidad Católica de Chile, and Universidad de Santiago de Chile, and was a guest researcher at Université de Picardie, Amiens. His research spans stochastic and deterministic dynamical systems , number theory , and machine learning , with a focus on reinforcement learning and optimization for AI applications in energy systems and agriculture. Current Teaching: Discrete Mathematics, Applied Probability and Statistics, Ordinary Differential Equations, Research Methodology Supervision: Head supervisor for PhD student Björn Lindenberg and Jonas Nordqvist (2020 graduate) Research Groups: AI and Machine Learning for Optimization , Computational Mathematics for Predictive Digital Twins (PreDiTwin) , and International Center for Mathematical Modeling (ICMM) His ongoing projects include reinforcement learning algorithms , climate-smart agriculture , and sustainable energy optimization , supported by collaborations with Kalmar Energi. International partners include Juan Rivera-Letelier (University of Rochester), Mike Zieve (University of Michigan), Charles Favre (École Polytechnique), and Youssef Fares (Université de Picardie). Key publications highlight advancements in p-adic dynamics , ergodicity , and reinforcement learning applications.

Mengchu ZhouView profile

Professor

• Petri Nets
• Automation
• Internet of Things
• +4 more

Mengchu Zhou is a Distinguished Professor at the New Jersey Institute of Technology within the Department of Electrical and Computer Engineering. With over 1200 publications, 31 patents, and numerous accolades, he is a leading figure in automation and intelligent systems. Education : Ph.D. from Rensselaer Polytechnic Institute (1990) Research Interests : Zhou specializes in Petri nets , automation , Internet of Things , and big data . His work focuses on modeling complex systems and optimizing industrial processes. Recent publications highlight advancements in scheduling algorithms, robotic grasp stability, and network throughput. Scientific Awards : Fellow, IEEE Fellow, IFAC Fellow, AAAS Fellow, CAA Fellow, NAI Collaborations : Zhou has collaborated with researchers across institutions, including Nanyang Technological University and The University of Hong Kong. He maintains an active network with 878 co-authors and over 1200 publications.

• Digital Human Modelling
• Ergonomics Evaluation
• Simulation-Based Optimization
• +5 more

Aitor Iriondo Pascual is a researcher at the University of Skövde's School of Engineering Science , specializing in digital human modeling (DHM) and simulation-based multi-objective optimization (MBOO) for ergonomics and productivity in manufacturing systems. His work bridges industrial engineering with human factors research, focusing on automating ergonomic assessments using motion capture technology and machine learning algorithms. PhD in User-Centered Product Design (University of Skövde, 2023) Active in Virtual Production Development (VPD) and Synergy Virtual Ergonomics (SVE) research profiles Collaborator in international projects like MOSIM and VIVA Key contributor to DHM toolchain development with IPS IMMA and LUA scripting His research interests center on: Integrating motion capture with DHM for objective ergonomic evaluations Automated hand posture classification using random forest algorithms Concurrent optimization of productivity and worker well-being in factory layout design Standardization of 3D body shape prediction models for virtual simulations Machine learning applications in biomechanical exposure assessments Digital twin development for sustainable manufacturing systems Recent publications demonstrate his expertise in combining genetic algorithms with DHM tools for occupant packaging design and logistics area optimization. He has also developed open-source statistical body shape prediction models shared under MIT License on GitHub. Key collaborators include: Prof. Lars Hanson (ergonomics) Dr. Dan Högberg (simulation methods) Dr. Anna Syberfeldt (optimization frameworks) Dr. Erik Brolin (DHM tools) His methodological innovations include: Automating REBA / RULA assessments via motion capture data Developing Pugh's method implementation for DHM concept evaluation Creating cross-disciplinary optimization frameworks for production systems

• Semiconductor Radiation Detectors
• Gas Sensing Technology
• X-ray Fluorescence Spectrometry
• +4 more

Göran Thungström serves as Associate Professor in Electronics within the Department of Engineering, Mathematics and Subject Didactics (IMD) at Mid Sweden University in Sundsvall. His research spans semiconductor radiation detectors, gas sensing technologies, and optical instrumentation development. His primary research interests focus on semiconductor radiation detectors for medical and industrial applications, gas sensing technologies for environmental monitoring, and optical instrumentation development. Key research areas include position-sensitive detector systems, X-ray fluorescence spectrometry, thermopile infrared detectors, and process industry sensor development. His work bridges fundamental semiconductor physics with practical industrial applications, particularly in environmental monitoring and process control. His recent publications demonstrate consistent contributions to radiation detection (2020-2023), gas sensing (2022-2024), and optical instrumentation (2019-2023). The research shows a clear progression toward more sensitive detection systems with applications in environmental monitoring, industrial process control, and medical instrumentation. Notable trends include the development of nanoscale heterostructures for gas detection and advanced signal processing techniques for radiation measurement. No scientific awards were documented in the source material. Thungström leads multiple research projects including MEQAL (methane leak detection), Chrome (chromium measurement), and LEAP (large-area energy applications). His collaborations span academia and industry, with frequent partnerships on environmental monitoring and industrial process sensor development. His work demonstrates strong integration between theoretical modeling and practical implementation. His laboratory work focuses on semiconductor detector fabrication, gas sensor development, and optical measurement systems. Current projects emphasize environmental applications including methane detection systems and chromium contamination analysis tools. The research group maintains strong connections with industrial partners in the process industry and environmental monitoring sectors.

Beata MegyesiView profile

Professor

• Computational Linguistics
• Historical Cryptology
• Artificial Intelligence in Humanities
• +3 more

Beata Megyesi is a Professor of Computational Linguistics at Stockholm University , leading groundbreaking work in Historical Cryptology and Digital Humanities . Her research bridges Artificial Intelligence with Philology to decode secret historical documents through projects like DESCRYPT (2025-2032) and DECRYPT (2018-2024), funded by Riksbankens Jubileumsfond and Vetenskapsrådet . Current Chair of Swedish Research Council's Linguistics Review Group (2024-2025) Director of the international Master's Program in AI and Language Advisor to PhD candidates Micaella Bruton and Crina Tudor Research Focus includes: Automatic analysis of 17th-19th century ciphers with AI Development of Linked Open Data infrastructure for cryptology Decryption of papal and diplomatic correspondences from 1500-1965 Creation of HistCorp multilingual historical corpus collection Key Collaborations span institutions in Sweden, Norway, Spain, Germany, Hungary, and the USA , with notable projects like DECODE2LOD and Swe-CLARIN . Her lab has pioneered automated key extraction and neural network-based alignment of encrypted manuscripts to plaintext.

Iris-Corinna SchwarzView profile

Lecturer

• Language Development
• Infant Cognition
• Neurolinguistics
• +3 more

Iris-Corinna Schwarz is a Lecturer in Special Education at Stockholm University , affiliated with the Department of Special Education and Stockholm Babylab . She teaches child language development, neurolinguistics, and research methodology in both English and Swedish. Stockholm Babylab Stockholm University Special Education Department Her research focuses on early language development in infants, toddlers, and preschoolers, with emphasis on cognitive neuroscience methods. Key projects include: L3WO : Hyperarticulation's effects on word learning HELD : Clear articulation in child-directed speech VAICA : Vocal affect in infant-directed speech DIDI : Distributional learning mechanisms LETO : Tone perception development PALL : Affect in infant learning SoundStart : Statistical learning foundations CAPSL : Auditory predictability studies Recent publications analyze lexical tone discrimination , vocal affect dynamics , and global speech exposure patterns . Her work connects speech perception with neural mechanisms using electrophysiological measures . While no specific awards are mentioned, her international collaborations and methodological innovations in infant-directed speech underscore significant contributions.

• Federated Learning
• Distributed Machine Learning
• Wireless Networks
• +2 more

Seyedmohammad Azimi Abarghouei is a Senior Researcher at the Division of Network and Systems Engineering, KTH Royal Institute of Technology, collaborating with Prof. Carlo Fischione. Previously, he served as a Postdoctoral Fellow in the same division from 2022 to 2024 under Prof. Viktoria Fodor. He completed his Ph.D. at Sharif University of Technology in Iran in November 2020, where his research on wireless networks earned the national Best PhD Thesis Award in electrical engineering and information technology. His educational background includes: Ph.D. in Electrical Engineering and Information Technology, Sharif University of Technology, Iran (2020) Dr. Azimi Abarghouei's research centers on distributed machine learning systems, particularly federated learning architectures for wireless networks. His work develops hierarchical aggregation methods, over-the-air computation techniques, and lattice coding approaches to address communication efficiency and data heterogeneity. He also contributes to molecular communication and stochastic geometry for nanonetwork modeling, bridging theoretical foundations with practical wireless applications. Analysis of his recent publications reveals a dominant focus on scalable federated learning systems, with significant innovation in hierarchical structures, quantization techniques, and interference-aware aggregation. His work consistently targets real-world challenges in edge intelligence, including bandwidth constraints, non-IID data distributions, and massive device coordination in IoT environments. His scientific recognition includes: Best PhD Thesis Award in Electrical Engineering and Information Technology (National Level, Iran) Jubilee Appropriation Scholarship from Knut and Alice Wallenberg Foundation (2023) Exemplary Reviewer of IEEE Wireless Communications Letters (2022, 2023, 2025) Exemplary Reviewer of IEEE Communications Letters (2023) Dr. Azimi Abarghouei has secured competitive research funding including the Knut and Alice Wallenberg Foundation's Jubilee Appropriation Scholarship. His active peer review contributions and conference tutorial leadership (e.g., IEEE ICMLCN 2025) demonstrate significant community engagement. While specific grant details beyond the scholarship aren't fully documented, his publication record indicates sustained support for federated learning research through institutional and foundation channels. He operates within Prof. Carlo Fischione's research group at KTH's Division of Network and Systems Engineering, focusing on networked machine learning systems. His collaborative work spans international conferences and journals, with recent emphasis on federated learning standardization through tutorials and preprints that shape emerging research directions in distributed AI.

• Wireless Communication
• Signal Processing
• Radar Systems
• +3 more

Murat Babek Salman is a Postdoctoral Fellow at the Royal Institute of Technology (KTH) , affiliated with the Communication System department. His research focuses on wireless communication systems, particularly in Massive MIMO, hybrid beamforming, and nonlinear signal processing for hardware impairment mitigation. His work addresses challenges in 5G/6G networks, full-duplex communication, and radar system optimization. Dr. Salman's recent publications highlight advancements in MIMO-OFDM radar, transitioning to gigantic MIMO for 6G, and developing low-complexity algorithms for nonlinear distortion compensation. His research spans both theoretical analysis and practical implementations for efficient physical layer design. His contributions include: Direction of Arrival (DoA) estimation techniques Power allocation strategies in massive MIMO Nonlinear detection algorithms for hybrid systems Quantization-aware ISAC system design

• Process Control
• Systems Engineering
• Dynamic Simulation
• +3 more

Veronica Olesen is a Senior Lecturer at Chalmers University of Technology, specializing in Systems and Control Engineering. She serves as the head of education for multiple Master of Science programs, including Engineering Physics, Engineering Mathematics, Biotechnology, and Chemical Engineering, while also managing the Technical Foundation Year program. Her teaching focuses on control engineering in bachelor's programs, and her research emphasizes process control, modeling, and dynamic simulations with direct industrial applications. Chalmers University of Technology Department of Systems and Control Engineering Senior Lecturer Her research spans process control systems, with a focus on reducing complex industrial models for efficient control, particularly in tank reactor processes. She has contributed extensively to control theory and educational innovation, including digital live labs and sustainability integration in curricula. Key trends in her publications include model reduction for industrial reactors, control theory applications (Quantitative Feedback Theory), and advancements in mechatronics and sustainability education. Her work bridges theoretical control engineering with practical industrial implementation, particularly in chemical and biotechnological processes. While no explicit awards are listed, her leadership in academic programs and funded projects like OPTIMERA and Prameco highlights her institutional impact. She has collaborated with industry stakeholders and European partners to enhance technical education and process control methodologies.