جستجوی استاد راهنمای دانشگاهی

• Solid Mechanics
• Materials Science
• Fracture Mechanics
• +۳ مورد دیگر

Jonas Faleskog is a Professor in the Department of Materials and Structural Mechanics at KTH Royal Institute of Technology. His research focuses on mathematical modeling of material deformation and failure mechanisms, particularly in metallic and polymeric materials. Key areas include ductile and brittle fracture analysis, fracture mechanics, and computational modeling of material behavior under various stress conditions. He leads a research group collaborating internationally to develop models describing material failure at microscopic scales. Faleskog teaches courses such as Fracture Mechanics (SE2139) and Modeling in FEM (SE2860), emphasizing practical applications of theoretical models. His work spans experimental and numerical methods, addressing challenges in material heterogeneity, porosity effects, and environmental degradation. Notable contributions include advancements in weakest-link modeling for brittle failure, probabilistic fracture models, and strain gradient plasticity analysis. His research bridges material science, applied mechanics, and numerical methods to optimize material utilization in engineering systems like reactor tanks, aircraft, and vehicles. Key collaborations involve international teams exploring microstructural influences on fracture behavior. While no specific awards are listed, his extensive publication record reflects sustained contributions to mechanical and materials engineering.

Monowar Bhuyanمشاهده پروفایل

دانشیار

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +۶ مورد دیگر

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

Zebo Pengمشاهده پروفایل

استاد

• Embedded Systems
• Electronic Design Automation
• SoC Testing
• +۶ مورد دیگر

Zebo Peng is a Professor and Deputy Head of Department at Linköping University's Department of Computer and Information Science (IDA), leading the Software and Systems (SAS) division. His research focuses on embedded systems design, electronic design automation, SoC testing, and real-time systems with emphasis on fault tolerance and hardware/software co-design. He has contributed to projects like the ASTECC initiative, funded by the Swedish Foundation for Strategic Research, addressing adaptive software in edge-cloud continuum systems. Key research interests include cyber-physical systems security, time-sensitive networking (TSN), and optimization techniques using genetic algorithms. Recent work explores thermal-aware design for reliability, security-aware scheduling, and stability guarantees in control systems. His publications span journals like IEEE TPDS and ACM TECS, alongside conference contributions on topics like resource management and fault detection in distributed systems. Prof. Peng collaborates extensively within the SAS division, which bridges academic and industrial research in software engineering and computer systems. His team's projects address challenges in real-time systems, embedded security, and parallel computing architectures.

Feras M. Awayshehمشاهده پروفایل

دانشیار

• Edge AI
• Federated Learning
• Distributed Data Privacy
• +۲ مورد دیگر

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).

Per-Olov Östbergمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Autonomous Resource Management
• +۷ مورد دیگر

Per-Olov Östberg is an Associate Professor at the Department of Computing Science, Umeå University, and a research leader in the Autonomous Distributed Systems Lab (ADSLab). His work focuses on resource management for distributed cloud environments using AI/ML-based techniques, with a particular emphasis on ethical reasoning integration for responsible AI solutions. Research Themes: Cloud-edge continuum optimization, serverless frameworks, 6G computing challenges, data fabric architectures, and energy-aware systems Projects: COGNIT (cognitive serverless framework), WARA Common Information Bridge (data-driven cloud operations), De facto Center of Excellence in Autonomous Distributed Systems His publications (2011-2024) demonstrate consistent contributions to cloud resource management, including fairshare scheduling, decentralized prioritization, and power-performance tradeoffs. He has collaborated on interdisciplinary projects with institutions across Europe. Scientific Awards: None explicitly stated in provided information.

• Cloud Computing
• Distributed Computing
• Parallel Computing
• +۱۰ مورد دیگر

Professor Javid Taheri is a leading academic at Karlstad University (2019–present), previously serving as Associate Professor (2015–2019) and Senior Lecturer (2015). His research focuses on cloud computing, edge computing, distributed systems, and AI-driven networking. He holds a Ph.D. in Information Technologies from The University of Sydney (2007) and an M.Sc./B.Sc. in Electrical Engineering from Sharif University of Technology (2000/1998). Research interests include cloud-edge continuum systems , resource optimization , 5G/6G networking , and AI for IoT . Notable contributions include frameworks like PerfSim (microservice performance simulation) and MultiScaler (auto-scaling for cloud applications). Publications highlight innovations in edge computing optimization, security for distributed systems, and machine learning for resource management. He has co-authored over 150 papers across top venues like IEEE Transactions and ACM conferences. Academic leadership includes roles as conference chair (IC2E 2023) and editorial work for journals on cloud and edge computing.

• High-Performance Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +۹ مورد دیگر

Niclas Jansson is a researcher at the PDC Center for High Performance Computing at KTH Royal Institute of Technology. He holds an M.S. in Computer Science (2008) and a Ph.D. in Numerical Analysis (2013) from KTH. His career spans roles such as postdoctoral researcher at RIKEN Advanced Institute for Computational Science (2013-2016) and visiting scientist at RIKEN (2018-2021), where he contributed to the Japanese exascale program Flagship 2020. A core focus of his research involves extreme-scale computing and numerical method development. He is a key developer of RIKEN's multiphysics framework CUBE , the HPC branch of FEniCS , and the spectral element flow solver Neko . His work is currently supported by a Swedish Research Council Starting Grant aimed at enhancing high-order spectral element methods for exascale fluid simulations. Niclas has published extensively on topics such as GPU acceleration , adaptive finite element methods , in situ visualization , and extreme-scale turbulence modeling . He also teaches Computational Fluid Dynamics (SG2212) at KTH.

Atakan Aralمشاهده پروفایل

دانشگاهی

• 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).

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

Auday Al-Dulaimy is a Senior Lecturer at the Department of Information Science, School of Information and Engineering, Dalarna University. His teaching responsibilities include coordinating courses on Distributed Computing , Internet of Things (IoT) , and Internship in Data Science . His research focuses on cloud computing, IoT, and smart production systems. Distributed Computing (GIK2NX) Internet of Things (GMI2MD) Internship in Data Science (AMI23J) His recent publications explore trends in cloud-based services for smart production, fault tolerance mechanisms, and computing continuum architectures integrating IoT and cloud technologies.

• Fluid Dynamics
• Multiphase Flow
• Reactive Flow
• +۳ مورد دیگر

Henrik Ström is an Assistant Professor in Fluid Dynamics at Chalmers University of Technology, specializing in multiphase flow modeling, reactive flow analysis, and rarefied flow simulation. His research spans molecular to continuum levels, emphasizing interdisciplinary collaboration. He has contributed to over 160 publications, focusing on automotive catalysis, bubble dynamics, biomass conversion, and CFD-DEM modeling. Research Interests: Multiphase flow dynamics, computational fluid dynamics (CFD), reactive flow modeling, automotive exhaust systems, and biomass thermochemical processes. He explores phenomena such as lift forces on deformable bubbles, soot generation in gasifiers, and flow distribution optimization in catalytic converters. Projects include advanced modeling of catalytic converters, particle-fluid coupling in biomass conversion, and carbon capture technologies. He collaborates on automotive pollution control, heat exchanger design, and nanoscale catalytic systems. His work integrates experimental validation with high-resolution numerical simulations.

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Computer Engineering
• Feedback Computing
• +۵ مورد دیگر

Karl-Erik Årzén serves as Professor and Head of the Department of Automatic Control at Lund University's Faculty of Engineering. He concurrently holds the position of Co-director for the Wallenberg AI, Autonomous Systems and Software Program (WASP) and maintains Fellow status with the Royal Swedish Academy of Engineering Science (IVA), alongside advisory roles at SMaRC and Aalto University. His research operates at the convergence of control theory and computer engineering, specializing in dynamic feedback-based resource management (feedback computing) for embedded systems and cloud infrastructures. Additional expertise spans embedded control, real-time systems, cyber-physical systems, and domain-specific programming languages for control applications, demonstrating consistent bridging of theoretical control frameworks with computational implementation challenges. Recent publication trends reveal intensive focus on applying control methodologies to distributed resource allocation, evidenced by works on real-time application offloading, auction-based storage allocation, and reinforcement learning-driven cloud auto-scaling. These contributions critically address scalability and performance challenges across computer science, telecommunications, and next-generation 6G network architectures. Scientific recognition includes: Best Paper Award (2018) for Predictability and Cloud Application research Best paper award (2018) Best Paper Award at RTNS 2016 Best Paper Award at RTCSA 2004 With 35 supervised students to date, Årzén currently serves as primary supervisor for PhD candidate Ahmed Al Bayati. His research portfolio includes active leadership in the Vinnova-funded AORTA project (2023-2025), Robust and Secure Control over the Cloud initiative (2021-2026), and the long-term WASP program (2015-2029), alongside completed projects like AutoDC and Testing Autonomous Control-Based Software Systems. He actively contributes to the ELLIIT research environment and shapes Lund University's AI and Digitalization profile area alongside LU's Natural and Artificial Cognition initiative.

Hans Hanssonمشاهده پروفایل

استاد

• Real-Time Systems
• Cybersecurity
• Industrial Control Systems
• +۶ مورد دیگر

Hans Hansson is a Professor at Mälardalen University since 1997, leading the Mälardalen Real-Time Research Centre (MRTC) and the strategic profile area Trusted Smart Systems (TSS). He holds multiple degrees from Uppsala University, including an MSc in Engineering Physics and a Doctor of Technology in Computer Systems. His research focuses on real-time systems, cybersecurity, industrial control systems, and safety-critical systems. He has advised over 20 PhD students, five of whom became university professors. Hans has secured over 500 MSEK in external funding from national and European grants. His work spans fault-tolerant systems, embedded software verification, and compositional safety analysis in complex systems. Key projects include the development of the Pkl language for operational design domains, security frameworks for industrial IoT systems (e.g., ICSSIM testbeds), and real-time storage solutions for fog computing. He also chairs the PROGRESS Centre for Predictable Embedded Software Systems. Publications emphasize cybersecurity in industrial systems, safety-critical software design, and real-time scheduling in time-triggered networks. His research bridges theory and practice, addressing challenges in autonomous systems, mining automation, and Industry 4.0.

• Materials Science
• Mechanical Engineering
• Plasticity Modeling
• +۵ مورد دیگر

Carl Dahlberg is a Researcher at Kungliga Tekniska Högskolan (KTH) within the Department of Materials and Structural Mechanics. His roles include teaching and course responsibility for advanced engineering courses such as FEM for Engineering Applications and Material Mechanics . His research focuses on plasticity modeling, dislocation dynamics, fracture mechanics, and advanced materials characterization. Key research areas include strain gradient plasticity, hydrogen embrittlement, and crystal plasticity. He has extensively studied dislocation density effects in metals, fracture mechanisms in steels, and mechanical behavior under extreme conditions. His work integrates experimental techniques like neutron imaging with computational models for material analysis. Publications span over 15 years, emphasizing topics such as: Length-scale dependent plasticity phenomena Fracture mechanics in polycrystals Multi-mechanism failure modeling Corrosion fatigue in stainless steels His educational background includes a PhD in Solid Mechanics from KTH (2011) and a Licentiate thesis on strain gradient plasticity (2009). He has contributed to courses at both undergraduate and graduate levels, balancing academic research with pedagogical responsibilities.

• Production Engineering
• Industrial Automation
• Digital Twins
• +۳ مورد دیگر

Dr. Anna Syberfeldt is a Professor in Production Engineering at the Department of Engineering Science, University of Skövde. She leads the Virtual Production Development research group and serves as research director at the ASSAR Industrial Innovation Arena. With a background in computer science from De Montfort University and habilitation in automation engineering, her work bridges AI, robotics, and immersive technologies with industrial production systems. Professor in Production Technology Research Director at ASSAR Industrial Innovation Arena University of Skövde Affiliation Her research focuses on developing innovative industrial solutions through: Artificial Intelligence and Machine Learning Collaborative Robotics and Human-Robot Interaction Digital Twins and Simulation-Based Optimization Augmented/Virtual/Mixed Reality Applications Smart Manufacturing and Industry 4.0 Worker Well-Being and Productivity Optimization Recent publications demonstrate her leadership in simulation-based optimization, collaborative robotics, and mixed reality applications in manufacturing. Her work emphasizes creating ultra-flexible production systems through cyber-physical approaches while prioritizing environmental sustainability and human-centric design. Key trends in her research include: Unified frameworks for virtual commissioning Knowledge graph applications in production systems Multi-objective optimization balancing productivity and ergonomics Smart glasses evaluation for industrial operators Evolutionary algorithms for complex manufacturing problems Her projects demonstrate practical implementations of: MAXLabs distributed cyber-physical testbed ERAIVA posture identification software Virtual environments for human-robot collaboration testing Digital support functions for factory layout planning

Angela Consuelo Checa Hurtadoمشاهده پروفایل

پژوهشگر

• Cloud Computing
• Edge Computing
• Fog Computing
• +۵ مورد دیگر

Angela Consuelo Checa Hurtado is a Researcher at the Autonomous Distributed Systems Lab within the Department of Computing Science at Umeå University . She is also a doctoral student, focusing on large-scale computing continuum systems. Research Interests: Design and optimization of distributed systems across cloud, fog, and edge computing paradigms. Specialization in resource monitoring models and architectures for the Cloud Continuum , integrating heterogeneous resources from edge to cloud for seamless application delivery.