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).

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.

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.

Mathias HaageView profile

Senior Lecturer

• Robotics
• Automation
• Construction
• +3 more

Mathias Haage is a Senior Lecturer and Project Manager at the Department of Computer Science, Lund University, affiliated with the Faculty of Engineering (LTH). He is a member of LTH's Profile Areas in 'Circular Building Sector' and 'AI and Digitalization,' and part of ELLIIT: the Linköping-Lund initiative on IT and mobile communication. His research focuses on Robotics and Automation, particularly in construction and industrial applications. He leads projects like the 'Center for Construction Robotics' and 'RobotLab LTH,' emphasizing automation in construction processes and smart manufacturing. Key research topics include parallel-kinematic manipulators, industrial robot applications, and ontology-based knowledge representation for robotics. He has contributed to advancements in construction automation, flexible manufacturing systems, and robot skill reusability. His work aligns with UN Sustainable Development Goals related to industry, innovation, and infrastructure. Haage has supervised over 33 research outputs, including peer-reviewed conference papers and journal articles. Notable collaborations include projects with Heidelberg Materials Cement Sverige AB and FORMAS. He actively participates in initiatives like the Robotics Week for Schools and AI Lund seminars, promoting public engagement in robotics and AI.

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.

• Computational Fluid Dynamics
• High-Performance Computing
• Turbulence Simulation
• +3 more

Martin Karp is a Research Fellow and postdoctoral researcher at KTH Royal Institute of Technology's Department of Engineering Mechanics, working under Dan Henningson. His research focuses on high-fidelity numerical simulations of turbulence and transition, with a specialization in high-performance computing (HPC) and supercomputing architectures. He holds a PhD in computer science from KTH and an MSc in Engineering Physics from Lund University, complemented by studies at ETH Zürich's computer science department. His research interests explore computational limits in nonlinear chaotic systems and future computational advancements. He leads the development of the Neko framework, a scalable simulation tool for extreme-scale CFD with extensive accelerator support. Karp's work emphasizes GPU and FPGA acceleration, parallel computing, and optimizing algorithms for heterogeneous architectures. Key contributions include large-scale turbulence simulations using GPUs, reducing communication in conjugate gradient methods, and evaluating FPGA-based flow solvers. His publications span journals like Concurrency and Computation and Scientific Reports , with conference presentations at IEEE Cluster, PASC, and HPCAsia. Karp's research bridges theoretical computational limits and practical HPC implementation, addressing challenges in precision, scalability, and hardware utilization. His educational background combines engineering physics with computer science, enabling interdisciplinary approaches to fluid dynamics and high-performance simulation. Current projects aim to push the boundaries of computational fluid dynamics through novel algorithm design and leveraging emerging hardware capabilities.

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.

David Black-SchafferView profile

Professor

• Computer Architecture
• Memory Systems
• Parallel Programming
• +4 more

David Black-Schaffer is a Professor at Uppsala University's Department of Information Technology, specializing in computer systems research. As of 2023, he serves as Dean of Research for the Faculty of Science and Technology. His work bridges software and hardware innovations to enhance data movement efficiency in computer systems, with applications commercialized through a startup and integrated into industry standards like OpenCL. Black-Schaffer earned his PhD in Electrical Engineering from Stanford University in 2008, focusing on many-core processor programming. His career spans roles at Apple Inc. (contributing to OpenCL standards), postdoctoral research at Uppsala University, and academic progression from assistant to full professor (2010–2017). He has held leadership roles including Head of the Division of Computer Systems (2022) and department representative on the faculty Advisory Committee for Research (2021). His research spans computer architecture, memory systems, parallel programming, and simulation techniques. Recent publications (2024–2020) explore garbage collection, cache optimization, memory contention, NUMA systems, and instruction scheduling. Key trends include software-hardware co-design for power efficiency, reuse-aware data placement, and machine learning for performance modeling. Knut & Alice Wallenberg Foundation: Wallenberg Academy Fellowship Prolongation (2020–2025), Wallenberg Academy Fellow (2016–2021) Swedish Research Council (VR): Project Grant (2019–2024), Young Researcher Grant (2015–2018), Framework Grant (2012–2017) European Research Council: ERC Starting Grant (2017–2022) Teaching Awards: Uppsala Engineering and Science Student Union Pedagogical Prize (2012), Uppsala University Pedagogical Prize (2016), Uppsala Technical Physics Students' Teaching Award (2019) Other Grants: ScalableLearning flipped classroom project (2012–2020), Arm Ltd. collaborations on memory system designs He pioneered flipped-classroom teaching through the ScalableLearning project, impacting over 80,000 students. His research is conducted in collaboration with institutions like Arm Ltd., with past contributions to Apple's OpenCL implementation and UPMARC research center.

Michael DoggettView profile

Associate Professor

• Image Synthesis
• Computer Graphics
• Real-Time Rendering
• +3 more

Michael Doggett is an Associate Professor and Senior Lecturer in the Department of Computer Science at Lund University, affiliated with ELLIIT and the LTH Profile Area: AI and Digitalization. His research focuses on image synthesis leveraging custom and programmable hardware, with contributions to real-time rendering, GPU programming, and augmented reality. He holds roles as Director of Third Cycle Studies and Project Manager, and has led initiatives in efficient GPU programming and real-time pixel synthesis. Education details are not explicitly listed, but his work aligns with UN Sustainable Development Goals related to innovation and infrastructure. His research interests emphasize hardware acceleration, light transport algorithms, and rendering efficiency. Recent publications explore opacity micromaps, sparse shading in AR, and caustics modeling. He supervises PhD projects such as 'DLXR: Real-Time Pixel Synthesis' and 'Efficient GPU Programming'. Notable collaborations include work with Facebook (2018-2020) and organizing the ACM SIGGRAPH Symposium. His grants include projects funded by the Swedish Research Council and ELLIIT. Michael is a member of Lund University's Parallel Systems research group and actively contributes to academic conferences and peer review.

• 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.

Esther-Maria GuggenmosView profile

Professor

• Contemporary East Asian Religions
• Chinese Buddhism
• Aesthetics of Religion
• +4 more

Professor Esther-Maria Guggenmos is a leading scholar at Lund University 's Centre for Theology and Religious Studies , specializing in Contemporary East Asian Religions with focus on Chinese Buddhism . Her research explores Religious Aesthetics , Decision-Making in Religious Contexts , and the Evolution of Emotions in Buddhist Rituals .

Roghayeh HajizadehView profile

Lecturer

• Discrete Optimization
• Operations Research
• Mathematical Modeling
• +4 more

Roghayeh Hajizadeh is a University Lecturer at Linköping University's Department of Mathematics (MAI) and Applied Mathematics (TIMA) research group. Her work focuses on discrete optimization techniques for real-world problems like urban snow removal and air traffic control, using mathematical modeling and algorithm development. PhD in Optimization of Snow Removal in Cities (2023) Member of Mathematics and Algorithms for Intelligent Decision Making research group Key projects: Optimization of Snow Removal in Cities (2017-present) and OPT@ATM (2023-present) Research keywords include: Discrete Optimization, Operations Research, Sustainable Urban Planning, Air Traffic Control Algorithms . Teaching includes Combinatorial Optimization for Computer Science/Software Engineering programs and Supply Chain Optimization for Industrial Economics/Mechanical Engineering students. Publications (2020-2024) demonstrate expertise in: Lagrangian relaxation techniques , vehicle coordination algorithms , and tree elimination methods for snow removal; Dantzig-Wolfe decomposition for aircraft scheduling.