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Petter HellströmView profile

Researcher

• History of Science
• Visual Culture
• Media History
• +8 more

Petter Hellström is a Researcher at Uppsala University's Department of History of Science and Ideas, specializing in the history of science, visual culture, and cartographic studies. He holds a BA Hons and MPhil from University of London and University of Cambridge, respectively, and was awarded membership in the Linnean Society of London (2024). Education : SOAS (University of London), University of Cambridge Research : Focus on scientific diagrams, evolutionary metaphors, and historical epistemology Awards : Carl Johans Förbundet Prize (2020) Academic Contributions : His publications explore topics like the Tree of Life metaphor, Enlightenment-era cartography, and the intersection of science, religion, and visual media. Hellström leads the Riksbankens Jubileumsfond-funded project ' Afrikas vita fläckar ' (2022–present), examining cartographic omissions in 18th–19th century maps. Scientific Awards Carl Johans Förbundet Prize for Meritorious Scientific Work (2020) Public Engagement : Active in science communication via radio programs, podcasts, and newspapers, including contributions to Vetenskapsradion Historia and The Guardian .

• Machine Learning
• Robotics
• Natural Language Processing
• +1 more

Mahmoud Rahat is a Senior Lecturer at the Department of Information Technology , Halmstad University , Sweden. He is affiliated with the Center for Applied Intelligent Systems Research (CAISR) since 2019, focusing on data-driven solutions for industrial applications. Research Expertise: Machine Learning, Predictive Maintenance, Robotics, Natural Language Processing Industrial Collaboration: Volvo Group Truck Technologies, Northern Illinois University (2015-2016) His work bridges academia and industry through projects in intelligent mobility and automated idea detection . He has developed innovative approaches in survival analysis and XAI (Explainable AI) for predictive maintenance systems. Current trends in his publications include: Explainable AI methods for industrial data (Shapley values, Integrated Gradients) Survival analysis in neural networks Domain adaptation for automotive sensor data Federated learning frameworks Virtual sensors via tensor completion Mahmoud supervises academic projects including: Lecturer: Machine Learning for Predictive Maintenance, Big Data Parallel Programming Course Responsible: Applied Data Mining Examiner: Degree Project in Computer Science and Engineering

Johanna PrytzView profile

Lecturer

• Syntax
• Semantics
• Verb Phrase Structure
• +4 more

Johanna Prytz is a Lecturer in Swedish at Södertörn University's Department of Culture and Learning within the School of Culture and Education. She specializes in syntax and semantics research with a focus on verb phrases and Swedish language acquisition for newly arrived students. Her research spans Syntax and semantics of verb phrases Atelic verb particles in Swedish Relationship between verb particles, action types, and transitivity Swedish as a second language education Collective learning models for school development Her publications demonstrate consistent focus on linguistic theory applied to educational contexts, particularly analyzing how structural language elements impact second-language acquisition. Research trends show increasing emphasis on practical applications in introductory programs for newly arrived students. She actively collaborates with Stockholm University's Intensivsvenska research group on projects funded by the Swedish National Agency for Education and Marcus and Amalia Wallenberg Memorial Foundation, focusing on literacy development and parallel language-knowledge growth. Teaching responsibilities include Swedish and Savinska as a second language in the Subject Teacher Program, plus supervision of Swedish B and C essays within Communication Studies.

Magnus LångView profile

Assistant Professor

• Formal Methods
• Concurrency Theory
• Software Verification
• +2 more

Magnus Lång is an Assistant Professor with doctoral duties at Uppsala University's Department of Information Technology, Division of Computer Systems. His research focuses on formal methods, concurrency theory, and software verification. Research Interests: Specializes in stateless model checking for concurrent programs. Explores dynamic partial order reduction techniques. Investigates graph-based algorithms for parallel verification. Develops optimized methods for sequential consistency. Contributes to event-driven multi-threaded program analysis. Contact: Email: magnus.lang@it.uu.se Postal Address: Box 337, 751 05 Uppsala

Ehsan MiandjiView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +4 more

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

David BromanView profile

Professor

• Programming Languages and Compilers
• Real-Time Systems and Cyber-Physical Systems
• Probabilistic Machine Learning
• +2 more

David Broman is a Professor at KTH Royal Institute of Technology in Sweden, leading the Division of Software and Computer Systems (SCS) and serving as Associate Director Faculty for Digital Futures. He holds a Ph.D. in Computer Science from Linköping University (2010), with visiting roles at UC Berkeley (2012–2016) and Stanford University (2023–2024). His research spans programming languages, real-time systems, and probabilistic machine learning, emphasizing secure and efficient compilation techniques. Education: Ph.D. in Computer Science (Linköping University, 2010) Visiting Roles: UC Berkeley (2012–2014 part-time), Stanford University (2023–2024) Research Interests: David focuses on advancing programming language theory, compilers, and probabilistic methods. Key areas include transformer-based compilation, cyber-physical systems, and formal verification. His work bridges theory and practice, addressing challenges in software reliability and efficiency. Awards: Notable recognitions include the Best ETAPS Paper Award (2023), Distinguished Artifact Award (ESOP 2022), and Teacher of the Year (KTH 2017). His contributions also include grants from the Swedish Foundation for Strategic Research and Wallenberg AI, Autonomous Systems and Software Program (WASP). Advising & Grants: David mentors graduate students (e.g., Gizem Caylak, Viktor Palmkvist) and leads projects funded by WASP, focusing on next-generation compilation techniques. He also contributed to tools like Miking, Timed C, and FlexPRET. Labs & Teams: His team develops frameworks for probabilistic programming and real-time systems, emphasizing collaboration with industry and academia through initiatives like Digital Futures at KTH.

Sohaib Ayaz QaziView profile

Researcher

• Magnetic Resonance Imaging (MRI)
• Medical Image Processing
• Cardiovascular Imaging
• +3 more

Sohaib Ayaz Qazi is a Principal Research Engineer at Linköping University, Sweden, affiliated with the Department of Health, Medicine and Caring Sciences (HMV) and the Center for Medical Image Science and Visualization (CMIV). His research focuses on accelerating MR turbulence mapping for cardiac and vascular imaging through advanced undersampling and k-space reconstruction techniques. Ph.D. in Medical Image Processing, COMSATS University Islamabad (2018) MS in Communication Electronics, Linköping University (2012) BS in Electrical Engineering, COMSATS-IIT (2009) His work bridges computational methods and clinical imaging, targeting rapid heart and vessel imaging. Recent publications highlight his expertise in dynamic MRI reconstruction using SVD and parallel architectures. Swiss Government Excellence Scholarship Two Student Support Program Awards from ESMRMB HEC IRSIP Fellowship at Linköping University At CMIV, he programs pulse sequences, develops scanner patches, and collaborates with colleagues like Petter Dyverfeldt, Tamara Bianchessi, and Twan Bakker.

August ErnstssonView profile

Lecturer

• Parallel Programming
• Heterogeneous Computing Systems
• High-Performance Computing
• +3 more

August Ernstsson is a Lecturer at Linköping University’s Department of Computer and Information Science (IDA). He is affiliated with the Graduate School in Computer Science (CUGS) and research groups Programming Environments (PELAB) and Software and Systems (SAS). His research focuses on parallel programming models, heterogeneous computing systems, and optimizing software for FPGA and GPU architectures. Notable contributions include frameworks for high development productivity (EXA2PRO) and deterministic parallel algorithms. Ernstsson’s work emphasizes performance portability, compiler design, and abstraction layers for heterogeneous systems. He collaborates with international teams on projects like pattern-based programming abstractions and FPGA-accelerated systems.

Flavius GruianView profile

Senior Lecturer

• Embedded Systems
• Real-Time Systems
• Hardware-Software Co-Design
• +5 more

Flavius Gruian is a Senior Lecturer and Reader Project Manager at the Department of Computer Science, Lund University (LTH). He holds affiliations with ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the LTH Profile Area: AI and Digitalization. His research focuses on resource-constrained embedded systems, real-time scheduling, and energy-efficient hardware-software co-design, with applications in IoT, radar systems, and radio astronomy. Education and Academic Roles: No explicit education details provided, but his roles include senior lecturer, project manager, and principal investigator (PI) in multiple research projects. He advises PhD students (Boulasikis, Callanan, Waldemarson) and contributes to initiatives like GPAI (AI hardware for computing) and NordicIoT (Nordic IoT hub). Research Interests: His work spans embedded systems modeling, real-time algorithms, custom hardware architectures, Java in embedded systems, and machine learning for industrial applications. He explores energy-efficient designs and reliability in distributed embedded systems. Key Projects: Active projects include GPAI (2021–2026), TaLMiRRA (machine learning in radar/astronomy, 2019–2022), and NordicIoT (2018–2023). He also leads research on scalable actor networks and GPU programming for visual systems. Grants and Funding: PI roles in TaLMiRRA (STINT-funded) and NordicIoT (Nordic collaboration). Projects involve collaboration with international teams and industry partners. Labs/Teams: Affiliated with ELLIIT and the LTH AI profile area. Collaborates on hardware-software co-design and embedded systems optimization.

Emma Forsén MantillaView profile

Senior Lecturer

• Eating Disorders
• Mental Health
• Sports Psychology
• +4 more

Emma Forsén Mantilla is a Senior Lecturer and Director of Studies for the Bachelor's Programme in Psychology at the Swedish School of Sport and Health Sciences (GIH). She belongs to the Department of Physical Activity and Health , specifically within the Section of Health Sciences . Her work bridges teaching, research, and collaboration in eating disorders and mental health. PhD in Psychology Swedish licensed psychologist Research Interests focus on: Psychological mechanisms in eating disorders (self-image, attachment, emotion regulation) Compulsive exercise in sports contexts Preventive interventions for at-risk populations International collaborations (Warwick University, UNC, Karolinska Institutet) Notable Research Trends : Development of the LEAP (Compulsive Exercise Activity Therapy) program Population-level studies of eating disorders Cross-disciplinary analysis of compulsive behaviors Longitudinal tracking of treatment outcomes

• Programming Languages
• Type Systems
• Compiler Optimization
• +2 more

Lars Hummelgren is a doctoral student at the Royal Institute of Technology , affiliated with the Division of Software and Computer Systems. His research focuses on programming languages, type systems, and efficient compilation techniques for CPUs and GPUs. He is supervised by David Broman, with co-supervision from Philipp Haller and Matthias Becker. Lars serves as an assistant for courses including Compilers and Execution Environments (ID2202), Computer Technology and Components (IS1500), and Computer Technology, Basic Course (IS1200). Email: larshum@kth.se

Jose Nunez-YanezView profile

Professor

• Hardware Architectures
• Heterogeneous Systems
• Machine Learning Hardware
• +12 more

Jose Nunez-Yanez is a Professor in hardware architectures for Machine Learning at Linköping University, affiliated with the Department of Electrical Engineering and the Division of Electronics and Computer Engineering (ELDA). He has over 20 years of experience in high-performance embedded hardware design and is associated with the Wallenberg Autonomous Systems Program (WASP). His research focuses on energy-efficient, adaptive, and parallel hardware systems for machine learning and signal processing. Key areas include FPGA-based acceleration, run-time reconfiguration, and edge AI. He has led significant research projects with industry and academic partners such as ARM, ST Microelectronics, ESA, and Intel. The recent publications highlight a strong trend toward hardware-aware optimization of Graph Neural Networks for edge deployment, emphasizing quantization, dynamic range analysis, and heterogeneous computing. This reflects a broader shift toward efficient deep learning on resource-constrained platforms. Best Paper Award, Adaptive Hardware Systems (AHS) Conference, ESA/NASA, 2014 Best Paper Award, Adaptive Reconfigurable Computing (ARC) Conference, 2016 He has supervised 20 PhD students and served as the program director of the Advanced Microelectronics Systems Engineering (AMSE) program at the University of Bristol, significantly expanding its reach. He has secured substantial funding as Principal Investigator and Co-Investigator with partners including Sensata Systems, DSTL, Qioptiq, and Xilinx. His industry fellowships include Royal Society and Marie Curie awards. He is a member of the Royal Society Grants Board, the EPSRC College, and the Marie Curie Alumni Association. His work bridges academia and industry, with active consultancy in FPGA optimization and lossless data compression.

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.

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.