جست‌وجوی استاد و پژوهشگر

• Smart Electricity Grids
• Energy Storage Systems
• Electromagnetic Compatibility
• +۳ مورد دیگر

Daniel Månsson is a Professor at the Department of Electrical Engineering, Royal Institute of Technology (KTH), specializing in smart electricity grids and power system components. His work spans electromagnetic compatibility (EMC) of large distributed systems, energy storage optimization, and privacy protection in smart metering. PhD in Engineering Physics (with specialization in Electromagnetism) Docent (Swedish Academic Title) in Electrical Engineering His research focuses on: Optimization of self-sufficient microgrids with energy hubs Smart meter privacy protection using energy storage EMC analysis of photovoltaic systems and UWB transients Hybrid energy storage system performance in renewable grids Recent publications indicate expertise in: Electromagnetic interference from solar PV systems Cyber-physical security in smart meters Conducted emission analysis Greenhouse gas reduction through optimized storage

Atakan Aralمشاهده پروفایل

عضو هیئت علمی

• Edge Computing
• Edge AI
• Internet of Things (IoT)
• +۳ مورد دیگر

Atakan Aral is a Visiting Lecturer at the Department of Computing Science, Umeå University. His research focuses on Edge Computing, Edge AI, and the Internet of Things (IoT), with a particular emphasis on resource management and sustainable environmental monitoring. He is affiliated with the Autonomous Distributed Systems Lab and Green Distributed Computing Group, both part of the Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative. His work spans theoretical frameworks and practical implementations in edge intelligence and distributed systems. Research Interests: Edge Computing architectures and workflows Neuromorphic and energy-efficient AI systems Federated learning and multi-cluster collaboration Sensor networks for environmental monitoring Optimization of resource allocation in distributed systems Key Publications Trends: Recent work emphasizes neuromorphic edge AI applications, hierarchical federated learning, and energy-efficient IoT deployments. His articles often intersect computing continuum concepts with real-world challenges like rural environmental monitoring and latency-critical systems. Scientific Awards: No awards explicitly listed in the provided text. Advising & Grants: No formal student advisees or grant details provided. However, he contributes to the De facto Center of Excellence in Autonomous Distributed Systems (2023–2029), indicating involvement in large-scale collaborative research. Labs & Teams: Member of the Autonomous Distributed Systems Lab (lead in distributed systems research) and Green Distributed Computing Group (focused on sustainability in computing).

Cristian Rojasمشاهده پروفایل

استاد

• Automatic Control
• System Identification
• Control Theory
• +۷ مورد دیگر

Cristian Rojas is a Professor of Automatic Control at KTH Royal Institute of Technology, specializing in system identification. His research bridges control theory, statistics, and machine learning to develop data-driven methods for analyzing and controlling dynamical systems. He holds an MS in Electronics Engineering from Universidad Técnica Federico Santa María (Chile) and a PhD in Electrical Engineering from the University of Newcastle (Australia). Research focuses on efficient utilization of data for self-learning systems, including topics like continuous-time system identification, robust control, and statistical estimation. Notable contributions include work on subspace identification, input design for sparse systems, and algorithms for H-infinity norm estimation. His methodologies emphasize practical applications in industrial automation, smart infrastructure, and autonomous systems. Recent publications highlight advancements in inverse filtering, decentralized learning systems, and the theoretical underpinnings of data-driven control. He collaborates widely on projects involving Bayesian methods, adversarial systems, and privacy-protected decision-making frameworks. Rojas' work often addresses challenges such as undersampling effects, model consistency, and computational efficiency in real-world control scenarios. His academic contributions include organizing academic ceremonies at KTH and mentoring researchers in the Department of Automatic Control. Current research explores intersections between machine learning interpretability and control theory, with applications to explainable AI in engineering systems.

• Telecommunications
• Wireless Communication Systems
• Radio Localization
• +۵ مورد دیگر

Henk Wymeersch is a Professor at Chalmers University of Technology's Communication Systems department, part of the School of Electrical Engineering. He leads the Radio Localization and Sensing research area and is affiliated with the FORCE Research Center on optical communication. His work focuses on algorithm design and system analysis for 5G/6G systems, emphasizing integrated communication, localization, and sensing. Notable contributions include RIS-aided positioning, bistatic/multistatic sensing, and joint communication-sensing frameworks. Research Interests: His primary areas include Reconfigurable Intelligent Surfaces (RIS), 6G positioning/sensing, integrated ISAC systems, mmWave/V2X positioning, and distributed radar networks. Recent work explores challenges in near-field localization, OFDM-based ISAC under hardware impairments, and privacy-preserving techniques for localization systems. Publications: Over 485 publications (2025) highlight his expertise in mmWave SLAM, RIS applications, and 6G enablers. Recent trends focus on 6G sustainability, distributed ISAC architectures, and leveraging RIS for robust localization in cluttered environments. Affiliations: Active in the Communication Systems Group and FORCE Research Center. Collaborates internationally on Hexa-X 6G projects and standards development.

• Federated Machine Learning
• Data Privacy
• Healthcare Applications
• +۳ مورد دیگر

Dominik Fay is a Researcher at the Division of Decision and Control Systems within Kungliga Tekniska Högskolan (KTH). His work focuses on federated machine learning, data privacy, and their applications in healthcare. He is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) through their industrial PhD program in collaboration with Elekta, a healthcare technology company. Education: MSc in Computer Science from KTH (2019) BSc in Applied Computer Science from Heidelberg University (2017) His research primarily addresses privacy challenges in distributed machine learning environments. Key contributions include methods for locally differentially private federated learning, dynamic privacy allocation, and privacy amplification techniques tailored for healthcare applications. His work also explores the intersection of machine learning and medical imaging, particularly in segmentation tasks requiring stringent privacy guarantees. Dominik's publications reflect a strong emphasis on data privacy, federated learning, and healthcare applications. Recent articles focus on correlated noise in federated learning, privacy allocation for composite objectives, and privacy-preserving medical image segmentation. Earlier works extend into smart grid privacy, metabolomics data analysis, and scalable privacy-preserving algorithms. Grants and Collaborations: Supported by WASP Industrial PhD Program Collaboration with Elekta, a leader in healthcare technology He is part of Mikael Johansson's research group, which specializes in decision and control systems, and his work aligns with broader efforts in privacy-preserving AI and machine learning for sensitive healthcare data.

Christoph Eggerمشاهده پروفایل

استادیار

• Cryptography
• Computational Complexity
• Statistical Privacy
• +۷ مورد دیگر

Christoph Egger is an Assistant Professor at Chalmers University of Technology in the Department of Computer Science and Engineering, where he works with the Security & Privacy Lab and the Crypto Team. Prior to this position, he was a Marie-Curie Fellow at Institut de Recherche en Informatique Fondamentale (IRIF) from fall 2022 to 2024, researching connections between cryptography and complexity theory. His educational background includes: PhD: "On Abstraction and Modularization in Protocol Analysis" Master's: "An implementation of global caching for the alternation-free coalgebraic μ-calculus" Bachelor's: "Analysing and attacking the I2P Network Database" Dr. Egger's research focuses on cryptography and its connections to computational complexity, statistical privacy, and formal methods. His work spans multiple areas including cryptographic foundations (random oracles, key agreement protocols), privacy-enhancing technologies (ring signatures, information flow techniques), and practical applications in genomic data security. He develops both theoretical frameworks and practical tools like CryptoZoo for cryptographic proof visualization, bridging the gap between theoretical cryptography and real-world security challenges. His recent publications demonstrate a strong focus on cryptographic foundations and privacy technologies, with significant contributions to ring signatures, key agreement protocols, and genomic data security. His work bridges theoretical cryptography with practical applications in blockchain, anonymous communications, and healthcare data management, showing consistent productivity across multiple high-impact venues in security and privacy. Dr. Egger has served on program committees for prestigious conferences including IEEE Computer Security Foundations Symposium (CSF), Proceedings on Privacy Enhancing Technologies (PETS), and Conference on Applied Cryptography and Network Security (ACNS). He currently advises PhD students Lucia Lavagnino at Chalmers and Kirthivaasan Puniamurthy at Aalto University. Previously, he advised Master's students including Julian Brost and Kirthivaasan Puniamurthy. His Marie-Curie Fellowship was cofunded by EU H2020 Marie Sklodowska-Curie Action and FSMP Comunity Service. Dr. Egger is an active member of the Security & Privacy Lab and Crypto Team at Chalmers. He is also a founding member of the FAUST CTF team and has been a Debian Developer for over a decade, contributing to various Free Software projects including the Linux kernel and Git version control system, demonstrating his commitment to both academic research and practical software security.

Andrei Gurtovمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Wireless Networks
• +۸ مورد دیگر

Professor Andrei Gurtov is a Full Professor at the Department of Computer and Information Science (IDA) at Linköping University (LiU). He holds adjunct professorships at Aalto University, University of Oulu, and University of Helsinki. His research focuses on network security, industrial internet, 5G/6G technologies, and cybersecurity in transport systems. He leads the AEGIS research group and is part of the Wallenberg AI, Autonomous Systems, and Software Program (WASP). Education: M.Sc. (University of Helsinki, 2000), Ph.D. (University of Helsinki/ICSI Berkeley, 2004). He has served as Head of the Secure Systems educational profile at IDA and on LiU's Departmental Board. His academic roles include IEEE Sweden Chair and Board member. Research highlights include work on secure drone communication (IETF DRIP), 6G security whitepapers, and cybersecurity for aviation systems. He has authored over 20 books and 264 publications, with notable works in IEEE and ACM journals. Awards include ACM Distinguished Scientist (2015), IEEE Distinguished Lecturer (2016-2019), and recognition in top 2% of global scientists (Stanford University). Teaching responsibilities include courses on computer networks and information security. He advises over 30 PhD students and leads projects on cyber-resilient air traffic management (SEC-AIRSPACE) and secure autonomous systems (DYNACON).

Cresantus Biambaمشاهده پروفایل

مدرس ارشد

• Educational Science
• Teacher Education
• Education for Sustainable Development
• +۳ مورد دیگر

Cresantus Biamba is a Senior Lecturer at the University of Gävle, specializing in Educational Science. His research bridges education theory with technological advancements, focusing on teacher training, sustainability in education, and inclusive pedagogy. Researcher at University of Gävle (Education, Educational Science) Research interests include: Education for Sustainable Development (ESD) in global contexts Teacher education reform and policy analysis Inclusive classroom practices in the Global South Technological integration in educational systems Curriculum development for post-pandemic resilience Publication trends reveal interdisciplinary work combining AI, cloud computing, and IoT applications with educational challenges, particularly in African institutions. His articles address security optimization, healthcare technology, and sustainability frameworks. Academic activities involve collaborations with researchers in cybersecurity, AI, and energy systems, though specific grants or mentoring roles are not explicitly documented here.

• Cybersecurity
• Internet of Things (IoT)
• Cloud Computing
• +۳ مورد دیگر

Marcus Schmidt Birgersson is a Lecturer at the Division of Network and Systems Engineering , KTH Royal Institute of Technology , Stockholm, Sweden. His roles include teaching and research in cybersecurity, particularly focused on Internet of Things (IoT) systems. Research Interests : Cybersecurity for IoT and cloud environments Secure system architecture design Trusted execution environments and privacy-preserving computing Publications : Recent work explores secure cloud analytics using trusted execution environments (2024) Research on multi-user security architectures for IoT systems (2021) Involvement : Course assistant and teacher for subjects including Applied Cryptography, Computer Networks, and Computer Systems Examiner and course responsible for Computing Systems Engineering

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

Seif Haridiمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Computing
• Cloud Computing
• +۴ مورد دیگر

Seif Haridi is a Professor at KTH Royal Institute of Technology in Stockholm, Sweden, specializing in parallel and distributed computing systems. He holds dual roles as Chair-Professor of Computer Systems and Chief Scientific Advisor at RISE SICS. His research integrates systems engineering with theoretical foundations, focusing on programming systems, distributed computing, and big data technologies. Key contributions include co-designing SICStus Prolog, the Mozart Programming System, and Apache Flink, as well as leading the development of HOPS, a European big data platform awarded the IEEE Scale Prize 2017. He has led major EU projects like EIT-Digital’s cloud computing initiative and co-founded startups such as LogicalClocks and HiveStreaming. His teaching includes courses on distributed algorithms and peer-to-peer computing at KTH. Notable awards include the European Data Science Technology Innovation 2019. His work spans systems like HOPS, Flink, and Kompics, emphasizing scalability and robustness in distributed environments. Current projects include CDA (Continuous Deep Analytics) and ExtremeEarth for geospatial data analysis. Research interests include distributed algorithms, consensus protocols, and cloud-native systems. His lab’s contributions to scalable storage (e.g., HopsFS) and stream processing (Apache Flink) highlight his impact on both academia and industry.

Alp Yurtseverمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Alp Yurtsever serves as an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, where his research pioneers end-to-end optimization frameworks bridging theoretical modeling and practical algorithm design for data science challenges. His work fundamentally rethinks traditional black-box system approaches by integrating problem formulation with solution methodologies. His academic journey includes: PhD in Computer and Communication Sciences from École Polytechnique Fédérale de Lausanne (EPFL) under Prof. Volkan Cevher Postdoctoral fellowship at MIT's Laboratory for Information and Decision Systems (LIDS) with Prof. Suvrit Sra Dual BSc in Electrical and Electronics Engineering and Physics from Middle East Technical University Yurtsever's research centers on optimization theory for machine learning, with groundbreaking contributions in federated learning systems, convex programming, and scalable semidefinite solvers. He champions a unified perspective where modeling and algorithmic development inform each other, yielding methods with proven theoretical guarantees and real-world efficiency. His work particularly addresses communication bottlenecks in distributed systems and non-convex landscapes in neural network training, with applications spanning privacy-preserving AI and edge computing. Analysis of his publication trajectory reveals dominant themes in federated optimization and Frank-Wolfe variants, where he consistently develops communication-efficient algorithms for heterogeneous device networks. His recent work demonstrates increasing sophistication in handling multi-tier architectures and personalized learning objectives, while maintaining rigorous convergence guarantees. The integration of quantum-classical hybrid approaches in his 2022 ECCV paper signals expanding methodological boundaries. His scientific recognition includes: Thesis Distinction for PhD dissertation "Scalable Convex Optimization Methods for Semidefinite Programming" (EDIC program committee) Yurtsever actively contributes to Umeå University's Mathematical Programming Group and Statistical Learning for Spatio-Temporal Data initiative, though specific grant awards remain undisclosed in available materials. His collaborative network spans EPFL, MIT, and multiple European institutions as evidenced by co-authorship patterns. While no formal advisees are listed, his publications show mentorship of junior researchers through joint conference presentations. His laboratory operations are embedded within Umeå University's Department of Mathematics and Mathematical Statistics in the MIT-huset building, leveraging institutional resources for high-performance optimization research while maintaining strong international connections through his post-PhD affiliations.

Masoumeh Ebrahimiمشاهده پروفایل

دانشیار

• Computer Architecture
• Machine Learning
• Embedded Systems
• +۶ مورد دیگر

Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +۱ مورد دیگر

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

• Internet of Things
• Open Source Software
• Program Comprehension
• +۶ مورد دیگر

Simon Butler is a Senior Lecturer in the Department of Information Technology at the University of Skövde's School of Informatics. He is an active member of the Distributed Real-Time Systems research group and contributes significantly to undergraduate computer science education. His academic work bridges theoretical research with practical industry applications in software development. Butler's research interests center on the challenges software developers face in creating and maintaining software systems, with particular focus on program comprehension, software maintenance, and the socio-technical and educational aspects of software development in hybrid environments where open source and proprietary solutions coexist. His work addresses critical issues in Internet of Things (IoT) systems and the practical implementation of open source software solutions in industrial contexts. His publication record from 2018-2023 reveals a strong research trajectory focused on open source software adoption, interoperability challenges, and standardization processes. His work spans diverse venues including Software Quality Journal, Journal of Systems and Software, IEEE Transactions on Software Engineering, and OpenSym proceedings, demonstrating both academic rigor and practical relevance to industry challenges. Butler is actively involved in several significant research projects including SUDO (2020-2024) addressing lock-in effects in IT systems, OSSD (2021-2023) developing educational programs for sustainable digitalization, and LIM-IT (2016-2020) investigating interoperability and long-term maintenance challenges. His teaching portfolio includes core computer science subjects such as Operating Systems, Object Oriented Programming, Algorithms and Data Structures, and Distributed Systems. As a member of the Distributed Real-Time Systems research group, Butler collaborates with industry partners to address real-world challenges in software development practices, particularly focusing on how organizations can effectively integrate open source solutions while avoiding problematic lock-in effects that compromise long-term system sustainability.