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Lina von SydowView profile

Professor

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +2 more

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

• Space Robotics
• Control Theory
• Computer Vision
• +4 more

Pedro Roque is a Postdoctoral Researcher at KTH Royal Institute of Technology in Stockholm, affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP) and associated with the Division of Decision and Control Systems (DCS). He obtained his Ph.D. in 2024 from the same division under the supervision of Prof. Dimos Dimarogonas, Prof. Mikael Johansson, and Prof. Jana Tumova. His research focuses on practically applicable theoretical results in robotics and control, with emphasis on space and aerial systems. Dr. Roque is particularly interested in developing algorithms that directly contribute to system performance and enhanced capabilities. He currently leads the setup of a Space Robotics Laboratory at KTH, associated with the Space Center and the WASP NEST DISCOWER project. He is an advocate for open-source software and hardware, contributing to NASA Astrobee and PX4 projects, with his research tested on the International Space Station and indoor flight arenas. Dr. Roque's work demonstrates a clear progression from theoretical foundations to practical implementation in space environments. His recent publications show an increasing focus on multi-agent coordination in microgravity, with significant contributions to model predictive control for space robotics applications. The research spans from fundamental control theory to complete system implementation, reflecting his commitment to bridging theory and practice. ICRA 2022 Outstanding Coordination Award for work on decentralized model predictive control for collaborative UAV bar transportation Dr. Roque actively mentors Master's students in Space Robotics, Control, and Vision, with supervision details available on his personal website. He has collaborated extensively with NASA Astrobee and PX4 projects, and his DISCOWER project involves collaboration with 3 Ph.D. students, 2 Master's students, 6 Professors, and one Post-doc. He also completed a 4-month internship at JPL within the Maritime and Multi-Agent Systems group. He leads the Space Robotics Laboratory at KTH, associated with the Space Center and the WASP NEST DISCOWER project, which has already demonstrated capabilities to Digital Futures, SAAB AB, SAAB Inc., and Purdue scholars. The laboratory focuses on weightless robotics, collaborative robotics (Space Cobot), and exploration robotics (MoonHopper), with practical testing on the International Space Station.

Andreas J. KasslerView profile

Professor

• Software Defined Networking
• Programmable Dataplanes
• Network Virtualization
• +7 more

Andreas J. Kassler is a Full Professor of Computer Science at Karlstad University, Sweden, where he has been since 2005. He co-chairs the Distributed Systems and Communication (DISCO) group and focuses on networking, cloud computing, and wireless networks. His research includes software-defined networking, future internet architectures, and network optimization. He has authored/co-authored over 130 peer-reviewed publications, holds 6 patents, and serves on editorial boards of journals like Journal of Internet Engineering . Education : Ph.D. in Computer Science, Universität Ulm (2002) Docent (Habilitation), Karlstad University (2007) M.Sc. in Mathematics/Computer Science, Universität Augsburg (1995) Research Interests : Software Defined Networking (SDN) Programmable Dataplanes Wireless Mesh Networks Time-Sensitive Networking (TSN) Edge Computing Machine Learning for Network Optimization Recent Directions : His work spans TSN scheduling, hybrid P4 solutions for 5G, and explainable AI in energy communities. He explores network resilience, latency optimization, and multi-objective control in microgrids. Service Contributions : Track co-chair for VTC 2015 General chair for Wired/Wireless Internet Communications (WWIC) 2013 Editor-in-Chief of IARIA Journal on Advances in Internet Technology Labs/Teams : Leads DISCO group at Karlstad University. Collaborates with global teams on projects like mmWave backhaul networks and SDN-enabled industrial control systems.

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

Louis OhlView profile

Research Fellow

• Machine Learning
• Unsupervised Learning
• Clustering Algorithms
• +3 more

Louis Ohl is a Research Fellow and Postdoc at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on unsupervised learning methods, particularly clustering algorithms and PU learning, with applications to medical data (e.g., aortic stenosis phenogroups) and material science (e.g., MAX-phase to MXen structure changes). He holds a PhD from Côte d'Azur University (France) and Laval University (Canada), where he developed discriminative clustering techniques for cardiac disease analysis. Education: PhD in Computer Science from Côte d'Azur University and Laval University (France/Canada). Research interests include: Unsupervised and semi-supervised machine learning Clustering algorithm development Medical informatics applications in cardiology Material science computational modeling PU learning frameworks His recent work emphasizes bridging theoretical advancements in clustering with practical applications in healthcare and materials research. Collaborations include projects on aortic stenosis progression prediction and computational studies of structural phase transitions in materials. No scientific awards listed. Active in advising and research grants related to his current projects at STIMA. Associated with the STIMA division's international master's program in Statistics and Machine Learning.

• Voice Acoustics
• Choir Acoustics
• Voice Analysis
• +5 more

Sten Ternström is a Professor at KTH Royal Institute of Technology's Division of Speech, Music and Hearing. He holds a MScEE (1982), PhD (1989), and has been a Professor since 2003. His research focuses on voice acoustics, particularly singing voice analysis and synthesis, with emphasis on clinical applications. Current interests include addressing voice variability, electroglottography, and audio technologies for music and voice clinics. He has led projects like FP7 EUNISON and SkAT-VG, and is a Fellow of the Acoustical Society of America. Education: All degrees at KTH—MScEE (1982), PhD (1989). Research Interests: Voice acoustics, choir acoustics, voice synthesis, biomedical signal processing. His work combines technical innovation with clinical relevance, e.g., developing FonaDyn software for real-time voice analysis. He explores how vocal fold dynamics influence voice quality and investigates non-invasive methods for voice assessment. Publications: Over 100 peer-reviewed articles, including recent work on WaveNet-based EGG prediction (2025), voice mapping post-thyroidectomy (2024), and pediatric voice analysis (2021). His research spans voice disorders, Parkinson’s disease voice therapy, and choral singing acoustics. Awards: Fellow of the Acoustical Society of America, Guest Editorships, and editorial roles in Acta Acustica. His contributions bridge engineering and clinical practice in voice science. Teaching: Leads courses in music acoustics, sound engineering, and supervises MSc projects on voice clinic software and singing synthesis. Active in outreach for choir acoustics and pedagogy. Labs/Teams: Involved in FonaDyn software development for voice analysis. Collaborates internationally on voice biomechanics and clinical voice measurement.

• Scientific Computing
• Computational Fluid Dynamics
• Finite Element Methods
• +3 more

Johan Jansson is an Associate Professor in Scientific Computing at KTH Royal Institute of Technology and BCAM (Basque Center for Applied Mathematics). He leads research in predictive Direct FEM Simulation (DFS) for aerodynamics and multiphase flows, and co-founded Icarus Digital Math as CEO. His work includes the FEniCS open-source finite element software project and MOOC-HPFEM educational initiatives. He holds roles as Director of the Center for Digital Math and collaborates internationally in computational science. Research focuses on high-performance computing (HPC), fluid-structure interaction (FSI), biomedical modeling, and renewable energy systems. Notable contributions include adaptive FEM frameworks for turbulent flow, vocal fold simulations, and wave energy converter modeling. His work bridges academic research with industrial applications, leveraging FEniCS-HPC and Unicorn solvers. Key achievements include election to the IVA Royal Swedish Academy of Sciences 100-list and securing the Severo Ochoa Center of Excellence Award. He has pioneered open-source tools like SimTek and contributed to major projects like the Salter Sink and vocal production modeling. Teaching responsibilities include courses on database technology, computational fluid mechanics, and research methodology. He actively engages in large-scale simulation projects involving marine energy, cardiac ablation protocols, and aerodynamic optimization.

• Automation Systems
• Control Engineering
• Robotics
• +5 more

Petter Falkman is an Associate Professor in Systems and Control Engineering at Chalmers University of Technology, where he leads research in the Automation research group. With 68 publications spanning over two decades, his work bridges theoretical control systems with practical industrial applications, particularly in manufacturing and robotics. His research has been supported by major funding bodies including VINNOVA and European Commission projects. Falkman's research primarily focuses on intelligent automation systems, with key contributions in sequence planning, virtual commissioning, and human-robot interaction. His work integrates formal methods with practical industrial applications, developing frameworks like the Sequence Planner for control of intelligent automation systems. His recent publications demonstrate a strong emphasis on data-driven approaches, digital twin technologies, and the application of virtual reality for industrial applications. The research shows a clear trajectory toward increasingly sophisticated integration of human factors with automation systems, particularly through eye tracking and movement prediction technologies. Falkman has led or participated in 10 major research projects from 2011-2025, including CLOUDS (2022-2025) on circular solutions for sustainable production systems, UNICORN (2017-2021) on robotic refuse handling, and several VINNOVA-funded projects on virtual preparation and industrial automation. His collaborative network includes researchers across Chalmers and industry partners like Volvo Group, demonstrating strong industry-academia connections. Falkman has established himself as a key contributor to the development of frameworks for intelligent automation, with particular expertise in translating theoretical control concepts into practical industrial applications. His work on the Sequence Planner framework represents a significant contribution to the field, enabling more efficient preparation and commissioning of automation systems.

• Renewable Energy
• Fluid-Structure Interaction (FSI)
• Aeroacoustics
• +5 more

Huadong Yao is an Assistant Professor at the Department of Marine Engineering, Chalmers University of Technology. His research spans renewable energy , fluid-structure interaction (FSI) , and transportation systems , with a focus on offshore wind farms, wave energy, and aero/hydroacoustics. Multi-University Collaboration : Guest professorships at international institutions Leadership Roles : Coordinator of Horizon 2020 projects (e.g., IVANHOE) and guest editor for journals Key Organizations : Member of AIAA, SAE International, RINA, ICNMT, and ICES Working Group on Offshore Renewable Energy His research integrates CFD and FSI coding (OpenFOAM, in-house codes) with turbulence modeling (LES, SNGR) to address problems in marine hydrodynamics (e.g., rim-driven thrusters, wind-powered ship propulsion) and terrestrial transportation (high-speed train aerodynamics, urban air mobility). Recent work explores biomechanics (whiplash injury hydrodynamics) and battery cooling systems for electric vehicles. Current projects focus on: Optimization of wave energy converter farms (hexagon layouts, mooring fatigue) Hubless rim-driven thruster design (gap geometry, concave cavities) Hydrographic impacts of offshore wind turbines on marine environments Urban air mobility (UAM) aerodynamics Multidisciplinary Design Optimization (MDO) using machine learning He collaborates with institutions like AIAA, SAE, and ICES, and has received funding from Horizon 2020 and Swedish national agencies.

• Fusion Plasmas
• Tokamak Experiments
• Electromagnetic Field Theory
• +4 more

Pär Strand is a Professor at Chalmers University of Technology, affiliated with the Department of Astronomy and Plasma Physics. His research focuses on transport in fusion plasmas , particularly through analysis of experiments at JET and development of simulation tools for ITER and other tokamak facilities. A key contributor to EU projects, he directs the Chalmers e-Science Centre, emphasizing data-driven methodologies and large-scale simulation technologies. Expertise: Fusion plasma dynamics, electromagnetic field theory, integrated modeling frameworks Projects: Code development for ITER/JET, FAIR data principles in fusion research, turbulence transport simulations Research Trends: Recent publications highlight advancements in: Tokamak power exhaust solutions (divertor shaping, neutral baffling) Machine learning applications for pedestal dynamics and disruption prediction High-order solvers for plasma transport equations Validation of D-T fusion power predictions against JET experiments

• 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.

Gemma AtkinsonView profile

Associate Professor

• Antibiotic resistance
• Bacteriophages
• Molecular evolution
• +7 more

Gemma Atkinson is an Associate Professor at Lund University's Faculty of Medicine, leading the Atkinson Lab. She specializes in protein evolution and bioinformatics, focusing on antibiotic resistance, bacteriophages, and toxin-antitoxin systems. She manages LU-Fold, an infrastructure for high-throughput protein structure prediction using AlphaFold. Her research integrates computational tools with experimental methods to uncover microbial defense mechanisms. Key projects include developing bioinformatics tools (e.g., LoVis4u, webFlaGs) and investigating phage defense systems. She leads grants from the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Her lab collaborates internationally, contributing to UN Sustainable Development Goals on health and innovation. Publications highlight advancements in toxin-antitoxin systems, antimicrobial peptides, and ribosome function. The lab's tools are widely used globally, aiding researchers in structural and functional genomics analysis.

• Network Performance
• Cybersecurity
• Congestion Control
• +4 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.

Isak SamstenView profile

Senior Lecturer

• Data Science
• Machine Learning
• Time Series Analysis
• +3 more

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +4 more

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.