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Showing 166-179 of 179 supervisors
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Lecturer
Hanna Holmgren is a Lecturer specializing in computer science and geospatial information systems at the University of Gävle. Her research bridges computational fluid dynamics and educational technology, with publications on microfluidic simulations and AI in learning environments. She contributes to academic discourse through conferences on engineering education innovations.
Professor
Ingemar André is a Professor at Lund University in the Department of Biochemistry and Structural Biology, affiliated with the eSSENCE: The e-Science Collaboration. His research integrates computational and experimental methods to study protein structure, evolution, and self-assembly. Key areas include AI-driven protein structure prediction, high-throughput protein stability analysis, and synthetic biology applications. Education: No specific academic degrees listed in the provided text. Research Interests: Dr. André’s work focuses on understanding protein interactions, evolution, and self-assembly pathways through computational modeling and experimental techniques. He develops AI-based tools for protein design and simulates evolutionary processes to predict structural changes. His experimental work includes high-throughput methods for characterizing protein biophysical properties in vivo. Recent Projects: Optimizing codon sequences for recombinant protein production (2023–2026, Novo Nordisk Foundation). Massively parallel protein stability measurements for evolutionary studies (2023–2027, Swedish Research Council). Awards: Sven and Ebba-Christina Hagberg Prize (2015). The Svedberg Prize (2015). Advising & Grants: Leads multiple research initiatives, including Synbio@Lund, a synthetic biology theme at the Pufendorf Institute for Advanced Studies. His grants focus on protein engineering and computational methods. Labs/Teams: Collaborates with interdisciplinary groups in synthetic biology and e-Science, emphasizing sustainable applications of bioengineering.
Associate Professor
Sindri Magnússon is an Associate Professor and Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), part of the Data Science Research Group. He holds a B.Sc. in Mathematics from the University of Iceland (2011), a Master’s in Applied Mathematics (Optimization and Systems Theory) from KTH Royal Institute of Technology (2013), and a Ph.D. in Electrical Engineering from KTH (2017). He completed a postdoctoral fellowship at Harvard University (2018–2019) and was a visiting PhD student there in 2015–2016. His research focuses on distributed optimization, decision-making, and machine learning in cyber-physical systems and IoT. Key projects include AI-driven equitable decision-making, smart converter control for renewable energy systems, federated reinforcement learning, and sustainable data-driven algorithms. His work bridges core data science with applications in critical infrastructures, energy systems, and societal challenges. Recent articles explore multi-objective optimization for satellite scheduling, federated learning for privacy-preserving AI, and reinforcement learning in non-stationary environments. His contributions span theory and practice, addressing scalability, sustainability, and ethical AI. He is actively involved in the Data Science Research Group, which develops algorithmic methods for decision-making in smart cities and energy systems. No awards or grants are explicitly listed, but his projects suggest significant research funding and collaboration.
Professor
Stefan Engblom is a Professor in Scientific Computing at Uppsala University, Department of Information Technology. His research focuses on developing computational methods and software for scientific applications, with particular expertise in numerical methods, fast algorithms, and simulation of complex systems. He is affiliated with the Scientific Computing division within the Information Technology department at Uppsala University. Engblom's research spans several key areas including fast multipole methods for efficient computation of long-range interactions, stochastic simulation of reaction-diffusion processes in complex geometries, computational epidemiology for modeling disease spread, and fluid dynamics simulations. His work combines theoretical development with practical implementation, resulting in several widely used software packages. His publications reveal a consistent focus on developing efficient numerical algorithms that address computational challenges in various scientific domains. The research shows a progression from fundamental algorithm development (fast multipole methods) to applications in biology (reaction-diffusion systems) and public health (epidemic modeling), demonstrating the versatility and applicability of his computational approaches. Engblom maintains active software development through several research codes including SimInf for epidemic modeling, URDME for reaction-diffusion processes, FMM2D/FMM3D for fast multipole methods, and other specialized tools for fluid dynamics and fiber simulations. His stenglib provides general-purpose Matlab libraries used in his research and available to the broader scientific community.
Lecturer
Ahmad Al-Shishtawy is a Lecturer at the Software Engineering and Computer Systems department within the School of Electrical and Computer Engineering (EECS) at KTH Royal Institute of Technology in Stockholm, Sweden. His contact details include phone number +46 8 790 42 42 and device address at Kistagangen 16. Teaching Roles: Data Mining (FID3016) - Teacher, Assistant Distributed Systems, Basic Course (ID2201) - Examiner, Teacher Programming of Parallel Systems (ID1217) - Examiner, Course Manager, Teacher, Assistant Scalable Machine Learning and Deep Learning (ID2223) - Assistant His research and teaching interests span software engineering, distributed systems, machine learning, data mining, and parallel programming.
Professor
Erik Elmroth is a Professor at the Department of Computing Science, Umeå University. He leads research in distributed systems, cloud/edge computing, and autonomous resource management, directing a 30+ member research group. His leadership includes transformative roles as department head (2009-2021) and Deputy Director at High Performance Computing Center North (HPC2N). Elmroth serves on executive committees for the SEK 6.2B Wallenberg AI program (WASP) and SEK 390M eSSENCE initiative, and leads multiple Kempe Foundation projects. Research interests center on: Autonomous control of cloud/edge infrastructures Software-defined systems and federated clouds AI-driven resource optimization High-performance computing architectures Robust machine learning for distributed environments His publications emphasize adaptive cloud systems, anomaly detection, and federated learning, with consistent focus on scalability and resilience in edge/cloud deployments. Awards and honors: Member of Royal Swedish Academy of Engineering Sciences (IVA) Nordea Scientific Prize (2011) SIAM Linear Algebra Prize (2000) National HPC Lecturer appointment He has supervised 40+ PhD students and secured major grants including the Swedish Research Council's second-largest award for the Cloud Control project. Elmroth founded the Control Workshops series and co-founded Elastisys AB, a cloud security firm with 50+ employees recognized as Umeå's Spin-off Company of the Year.
Researcher
Robert-Zoltán Szász is a Researcher in the Department of Energy Sciences at Lund University, Faculty of Engineering, and a member of the LTH Profile Area: The Energy Transition. His work centers on numerical modeling of fluid flows. His research interests encompass: Numerical modeling of swirling reacting and non-reacting flows Computational aeroacoustics Wind turbine aerodynamics Ice accretion phenomena He employs Large Eddy Simulations and Computational Fluid Dynamics to address energy system challenges, with recent focus on hydrogen-enriched combustion dynamics and ice accretion modeling for renewable infrastructure. Szász has supervised 6 students and contributed to key projects: Numerical and experimental investigation of a gas turbine model combustor : Dissertation project examining swirling flows in combustion systems Computations of ice throw/fall : 2018-2019 research on ice formation/detachment modeling
Senior Lecturer
Jonas Skeppstedt is a Senior Lecturer in the Department of Parallel Systems at Lund University's Faculty of Engineering. His research focuses on Computer Science and Parallel Processing. Institution: Lund University School: Faculty of Engineering Department: Parallel Systems Research Interests span multiple domains in computer science: Heuristics for optimizing computational processes Parallel Processing architectures and implementation Compiler design and optimization techniques Dataflow programming models Publications reflect his expertise in code optimization and parallel systems. Contact: jonas.skeppstedt@cs.lth.se
Professor
Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.
Richard James Glassey is a University lecturer at the Royal Institute of Technology (KTH) in Stockholm, Sweden, working in the Department of Theoretical Computer Science. His office is located at Lindstedtsvägen 5, Floor 5, and he can be contacted via phone at +46 8 790 69 91. His research interests include: Theoretical Computer Science Algorithms and Data Structures Programming Computer Science Education Parallel Programming Dr. Glassey teaches across the computer science curriculum from foundational to advanced levels, demonstrating expertise in both theoretical concepts and practical implementation. His teaching spans introductory programming, algorithms, data structures, and specialized topics in computer science education. He holds significant teaching responsibilities across multiple courses, often serving in dual roles as both teacher and course coordinator or examiner. This indicates leadership within the department's educational framework and deep involvement in curriculum development and assessment.
Lecturer
Anders Västberg is a Lecturer at KTH Royal Institute of Technology within the Department of Communication Systems. His work spans teaching and examination roles across various courses in computer science, electrical engineering, and information and communication technology (ICT) innovation, including Wireless Communication Systems , Mobile Networks , and Programming of Parallel Systems . Teaches courses like Internet of Things and Introduction to Computer Security . Examiner for advanced-level degree projects in embedded systems and communication systems. Focuses on wireless networking, heterogeneous networks, and energy-efficient network design. His research interests include energy efficiency in telecommunications , green radio systems , and network optimization . Recent work explores power consumption in backhaul systems , heterogeneous network deployment , and signal propagation in ionospheric channels . Articles highlight trends in green networking , starting with 2016 studies on cell DTX and heterogeneous networks , followed by 2013 work on backhaul optimization and wideband efficiency . Earlier papers (1997–2008) focus on ionospheric signal distortion and HF channel analysis .
Researcher
Mingzheng Chen is a Post-Doctoral Researcher at KTH Royal Institute of Technology's Division of Electromagnetic Engineering and Fusion Science, specializing in advanced antenna systems and waveguide technologies for terahertz and millimeter-wave applications. His research program centers on three interconnected pillars: quasi-optical structures for beamforming, periodic electromagnetic structures (particularly glide-symmetric holey configurations), and additive manufacturing of metal RF components. This focus enables lightweight, low-cost solutions for satellite communications and next-generation wireless systems, with experimental validation forming a critical component of his methodology. Analysis of his 15 most recent publications reveals dominant themes in W-band and sub-THz antenna design (7 papers), additive manufacturing techniques (6 papers), and waveguide physics (5 papers), demonstrating consistent innovation in geodesic horn antennas and glide-symmetric structures across both journal and conference venues. Award recognition includes: 2023 Best Paper Award from National Science Review Best Paper Award at MTTW 2023 As an active educator, he serves as Teaching Assistant for Applied Antenna Theory (EI2400) while maintaining prolific output (50+ publications). His collaborative network spans European institutions, with particular emphasis on experimental validation of theoretical models. Current research leverages KTH's advanced manufacturing facilities for metal-only RF component development, targeting applications in geostationary satellite communications and 6G infrastructure.
Associate Professor
Mattias Sandberg is an Associate Professor at the Royal Institute of Technology (KTH) , Sweden, within the School of Engineering Sciences and the Division of Numerical Analysis, Optimization and Systems Theory . He is actively engaged in teaching and examining a broad spectrum of courses in numerical methods, differential equations, and computational mathematics. His research and teaching interests span numerical analysis , partial differential equations , stochastic differential equations , and parallel computing . He is responsible for several core and advanced courses, including Analytical and Numerical Methods for Differential Equations , Computational Methods for Stochastic Differential Equations and Machine Learning , and Numerical Methods for Partial Differential Equations . These courses reflect his deep involvement in both theoretical and applied aspects of computational mathematics. Sandberg also serves as examiner for multiple degree projects and advanced courses, underscoring his role in mentoring and evaluating students at the master's level. His affiliation with KTH and his extensive teaching responsibilities indicate a strong commitment to education and research in applied and computational mathematics.
Professor
Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.
