Find Academic Supervisors

• High-Performance Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +9 more

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 AralView profile

Academic

• Edge Computing
• Edge AI
• Internet of Things (IoT)
• +3 more

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).

Saad MubeenView profile

Professor

• Embedded Systems
• Real-Time Communication
• Predictable Software Development
• +5 more

Saad Mubeen is a Full Professor of Computer Science at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. He holds a Master's in Electrical Engineering (Embedded Systems) and a PhD in Computer Science and Engineering from Mälardalen University (2014), with a Docent title (2018) focused on vehicular embedded systems. His research emphasizes predictable embedded systems, timing analysis for real-time communication, and component-based software design. Key areas include model-driven development for automotive systems, integration of TSN/5G networks, and fault-tolerant industrial architectures. He has led projects on end-to-end timing analysis in distributed systems, ROS 2 verification, and cognitive edge-cloud scheduling. Publications span 2021–2025, focusing on real-time systems, network protocols (TSN, AVB, 5G), and industrial automation. Notable work includes frameworks for TSN configuration, fault diagnosis tools using NETCONF, and scheduling algorithms for heterogeneous edge-cloud environments. His contributions address critical challenges in timing predictability, security, and resource optimization for cyber-physical systems. Education contributions include problem-based learning modules for vehicular software engineering. He is actively involved in bridging academia and industry through collaborative research on next-generation automotive and industrial systems.

Bengt JonssonView profile

Professor

• Formal Methods
• Real-Time Systems
• Distributed Systems
• +6 more

Bengt Jonsson is a Professor at the Division of Computer Systems, Department of Information Technology, Uppsala University. His research focuses on formal methods, real-time and distributed systems, semantics and verification of concurrent systems, and IoT security. Current Projects: UPMARC (Software Technology for Multicore Programming), aSSIsT (Secure Software for IoT), and Designed for UPDATE (Safe Embedded Software Updates) Past Projects: CoDeR-MP (Multicore Real-Time Applications), ProFun (Wireless Sensor Networks), CONNECT (Networked Component Synthesis) His work includes automated verification, model checking, and symbolic execution for concurrent systems. Recent publications address dynamic partial order reduction, IoT protocol testing, and lock-free data structures. Scientific Awards : CAV Award 2017 He advises PhD students and teaches courses like Model-Based Development of Embedded Systems and graduate-level symbolic execution. Personal interests include piano playing and orienteering.

• Control Engineering
• Process Control
• Optimal Control
• +11 more

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

Christoph KesslerView profile

Professor

• Parallel Programming
• Compiler Technology
• Optimization Algorithms
• +3 more

Christoph Kessler is a Professor and Head of the Software and Systems (SAS) division at the Department of Computer and Information Science (IDA), Linköping University, Sweden. He leads the Programming Environment Laboratory’s research group focusing on compiler technology, parallel computing, and heterogeneous systems. His work includes the development of tools like OPTIMIST, PARAMAT, and SkePU, and he has contributed over 100 publications in journals and conferences. He holds a PhD from the University of Saarbrücken and a Habilitation from the University of Trier. Research interests span parallel programming, compiler optimization, and energy-efficient scheduling for heterogeneous systems. He has secured a 30M SEK grant from SSF for the ASTECC project, advancing adaptive software for edge-cloud computing. Notable contributions include frameworks for GPU-based systems and methodologies for optimizing resource allocation on many-core architectures. His team’s work emphasizes practical applications in high-performance computing, including tools for course management (StASy) and energy-aware scheduling algorithms. The SAS division, under his leadership, focuses on software engineering and computer systems research with strong industry collaboration.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

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 HaridiView profile

Professor

• Parallel Computing
• Distributed Computing
• Cloud Computing
• +4 more

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.

Yuan YaoView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Power and Thermal Management
• +7 more

Yuan Yao serves as an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. His academic role spans teaching and research within the Computer Systems division, focusing on cutting-edge computer architecture and parallel computing systems. He maintains active collaborations across international institutions, particularly in energy-efficient hardware design and emerging computing paradigms. His educational journey includes: B.S. in Micro-electronics from Northwestern Polytechnical University, China (2009) M.S. in System-on-Chip Design from KTH Royal Institute of Technology, Sweden (2014) Ph.D. in Electrical Engineering and Computer Science from KTH Royal Institute of Technology (2019) Yao's research centers on power and thermal management for chip multi-processors, Network-on-Chips (NoCs), and GPUs. He pioneers hardware/software co-design for high-performance computing, coherency mechanisms for emerging memory technologies, and performance analysis of on-chip networks. Recent work expands into neural network acceleration and battery-less Internet of Things architectures, reflecting a trajectory toward energy-constrained specialized systems. His methodology integrates formal modeling with practical implementation for real-world impact. Publication trends reveal consistent innovation in energy efficiency across parallel architectures. From foundational DVFS techniques for NoCs (2016-2018) to recent breakthroughs in battery-less IoT (2023-2024), his work demonstrates evolutionary progression toward novel computing domains. Key thematic threads include thermal-aware optimization, memory consistency protocols, and hardware acceleration for AI workloads, with applications spanning data centers to embedded systems. Scientific recognition includes: Best paper candidate at IEEE International Symposium on High Performance Computer Architecture (HPCA) 2018 for in-network packet generation research Yao actively supervises graduate researchers and leads collaborative projects in computer architecture. His grant portfolio supports work on battery-less IoT systems and neural network accelerators, though specific funding details aren't publicly enumerated. Current projects emphasize sustainable computing through novel architectures for energy-harvesting environments. He operates within Uppsala University's Computer Systems division, contributing to research groups focused on hardware acceleration, embedded systems, and networked architectures. His lab environment fosters interdisciplinary work bridging computer architecture, energy harvesting, and machine learning for next-generation computing platforms.

Masoumeh EbrahimiView profile

Associate Professor

• Computer Architecture
• Machine Learning
• Embedded Systems
• +6 more

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.

Ming XiaoView profile

Associate Professor

• Wireless Communications
• Network Coding
• Machine Learning
• +10 more

Ming Xiao is an Associate Professor in the Division of Information Science and Engineering at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS). He is affiliated with the Digital Futures Faculty and leads research in wireless communications, machine learning, and network coding. His work focuses on 6G networks, distributed learning, and secure communications. Affiliations: KTH EECS, Digital Futures, Swedish Research Council, EU Horizon Europe projects Roles: Editor for IEEE Transactions on Wireless Communications, TPC Co-Chair for VTC Fall Research Interests: Dr. Xiao's expertise spans wireless communication systems (e.g., mmWave, NOMA), network coding, machine learning applications in communications, and energy-efficient distributed systems. He has pioneered work on intelligent reconfigurable surfaces (RIS), federated learning in edge computing, and integrated sensing-communications (ISAC). Projects: Ongoing EU-funded projects include ASCENT (autonomous vehicular networks) and COVER (unmanned aerial vehicles for emergency response). Past projects include 6G channel coding and intelligent energy management in smart communities. Grants: Over 12 active grants from VR, EU Horizon Europe, FORMAS, STINT, and VINNOVA Awards: IEEE Vehicular Technologies Society Best Paper Award (2023), World Top 2% Researcher (2020–2023), Highly Cited Researcher in Computer Science/Engineering. Labs/Teams: Leads the KTH Digital Futures initiative, collaborates with RISE Research Institutes, and manages a team of 12 PhD students/postdocs focusing on 6G, distributed ML, and secure IoT.

Corentin CadiouView profile

Research Fellow

• Dark Matter
• Star Formation
• High Resolution Simulations
• +5 more

Corentin Cadiou is a Research Fellow at Lund University's Department of Physics, specializing in Astrophysics. He holds dual positions as a Postdoctoral Fellow in both the Astrophysics division and the eSSENCE: The e-Science Collaboration initiative. His research focuses on computational astrophysics and cosmology, with particular expertise in dark matter, galaxy formation, and cosmic structure evolution. His primary research interests include: Dark matter physics and cosmic web dynamics High-resolution cosmological simulations Galaxy formation and evolution mechanisms Star formation processes in various environments Computational methods in astrophysics Large-scale structure of the universe He employs advanced simulation techniques to study galaxy-scale phenomena and cosmic evolution. Cadiou's recent publications demonstrate a strong focus on developing and optimizing astrophysical simulation codes, analyzing cosmic structures like filaments and dark matter halos, and investigating galaxy formation processes across cosmic time. His work frequently combines theoretical modeling with high-performance computing approaches to address fundamental questions in cosmology. He is currently engaged in the project: eSSENCE@LU 11:4 - Galaxy formation in the exascale era (2025-2026), where he serves as a researcher developing next-generation galaxy formation simulations.

• Control Systems
• Robotics
• Embedded Systems
• +3 more

Alessandro Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the Institute for Software Technology and Software Engineering (ITIS), University of Málaga. He leads the Complex Real-Time Embedded Systems (CORE) research group and serves as Scientific Leader for Applied AI under the AI@MDU initiative. His research focuses on control theory, robotics, and real-time embedded systems, emphasizing interconnected systems and uncertainty management. Education: BSc (2008) and MSc (2010) in Computer Engineering from Politecnico di Milano; PhD (2014) in Information Technology, Systems and Control from the same institution. Postdoctoral fellowships at Lund University’s Department of Automatic Control and Politecnico di Milano’s Dipartimento di Elettronica, Informazione e Bioingegneria. Research interests include control theory applications to computing systems, distributed systems, and AI integration. His work addresses challenges in real-time, embedded, and edge computing. Notable contributions include VR starting grant (2020) and SSF strategic mobility grant (2020), and the Most Influential Paper Award at SEAMS 2025. He has advised PhD students like Anna Friebe and Daniel Bujosa Mateu. Grants and collaborations include projects with ABB industrial automation and leadership roles in conferences (e.g., DEBS 2025 Workshop/Tutorial Chairs). His research group explores autonomous systems, cybersecurity in industrial networks, and cloud manufacturing resilience. Labs/teams: CORE group, collaborating with Prof. Thomas Nolte.

• Neural Networks
• Embedded Systems Design
• FPGA and ASIC Implementation
• +7 more

Yu Yang is a Researcher at KTH Royal Institute of Technology's Division of Electronics and Embedded Systems. He has been affiliated with KTH since at least 2020 and currently holds a postdoc position. His research focuses on neuromorphic computing, FPGA/ASIC implementation, approximate computing, and embedded systems design. He also explores ergonomic applications using wearable sensors to address workplace safety and musculoskeletal disorders. Yang has taught courses like Digital Design and Embedded Hardware Design in ASIC and FPGA , demonstrating expertise in both theoretical and applied electronics. His work bridges hardware acceleration (e.g., memristor-based neural networks) with practical applications like surgeon workload analysis and posture correction systems. Notable projects include the eBrainII ASIC implementation of a human-scale cortical model and developing smart workwear systems for real-time vibrotactile feedback. Publications span IEEE conferences (DATE, FDL, ASP-DAC) and journals like Frontiers in Neuroscience and Journal of Signal Processing Systems . His research often emphasizes low-power, high-performance computing while addressing ergonomic challenges in manufacturing and healthcare sectors.

Lars KarlssonView profile

Associate Professor

• High-Performance Computing
• Numerical Linear Algebra
• Parallel Algorithms
• +2 more

Lars Karlsson is an Associate Professor at the Department of Computing Science, Umeå University, where he serves as Assistant Head of Department with responsibilities for undergraduate education. His research focuses on developing efficient algorithms for matrix and tensor computations within high-performance computing environments. He is affiliated with the Parallel and Scientific Computing research group at Umeå. Karlsson's primary research areas include: Numerical Linear Algebra : Specializing in matrix factorizations and eigenvalue computations Parallel Algorithms : Designing scalable solutions for distributed and shared-memory systems Tensor Computations : Developing decomposition methods and completion algorithms Performance Optimization : Auto-tuning techniques for modern computing architectures His publications (2010-2025) demonstrate consistent focus on parallel algorithms for numerical problems, particularly matrix reductions, eigenvalue computations, and tensor decompositions. Recent work emphasizes auto-tuning, robustness in numerical methods, and efficient scheduling for high-performance systems. The majority of publications involve collaborative research within European computing consortia like NLAFET. Karlsson contributes to educational research, having explored mastery learning approaches in university settings. His administrative responsibilities include oversight of undergraduate programs at the Department of Computing Science.