Find Academic Supervisors

• Distributed Machine Learning
• Federated Learning
• Decentralized Optimization
• +3 more

Hao Chen is a researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS), specifically within the Computer Science department focusing on Communication Systems and the Optical Network Laboratory (ON Lab). He also contributes to research in Intelligent systems and Information Science and Engineering. Chen completed his doctoral dissertation titled 'Reliable and Efficient Distributed Machine Learning' in 2022, establishing himself as an emerging expert in distributed machine learning systems. Chen's research interests center around distributed and federated machine learning architectures, with particular emphasis on optimizing communication efficiency in decentralized systems. His work addresses critical challenges in distributed learning including communication bottlenecks, straggler nodes (devices with slow responses), and privacy preservation. His research spans applications in wireless IoT networks, satellite communications, and edge computing environments. His publication record shows a clear trajectory of increasingly sophisticated approaches to distributed machine learning. Starting with foundational work on coded stochastic ADMM methods in 2021, he progressed to developing asynchronous parallel algorithms (2023) and exploring applications in specialized domains like speech fatigue recognition (2024). A consistent theme across his work is the optimization of communication resources while maintaining learning performance, with particular attention to real-world constraints in wireless and satellite networks. Chen has collaborated extensively with researchers including Ming Xiao, Mikael Skoglund, Yu Ye, and others across multiple publications. His research has been supported by funding sources including the EU Horizon 2020 program (grant 825272) and the Swedish Foundation for Strategic Research (APR20-0023). His work appears in high-impact journals including IEEE Transactions on Big Data, IEEE Internet of Things Journal, and IEEE Wireless Communications. As a researcher at KTH, Chen contributes to cutting-edge work at the intersection of machine learning and communication systems, developing techniques that enable efficient distributed intelligence across networked devices while addressing practical constraints of real-world deployment.

• Quantum Computing
• Theoretical Physics
• Quantum Control
• +3 more

Jorge Fernandez Pendas is a Researcher at the Applied Quantum Physics Division , Chalmers University of Technology , Sweden. He is affiliated with the Quantum Computer Theory Group at the Wallenberg Centre for Quantum Technology and participates in the European consortium OpenSuperQ for open superconducting quantum computers. His research focuses on modeling and optimal control of superconducting qubits , with expertise in quantum anomalies , quantum transport phenomena , and topological matter . He received his PhD in Theoretical Physics from the Universidad Autónoma de Madrid in 2019. Quantum Computing Theoretical Physics Quantum Control Topological Materials Quantum Transport Phenomena Computational Physics His recent work includes optimizing superconducting qubit gates , mitigating frequency collisions , and analyzing quantum dissipation effects . He collaborates with institutions like Volvo Group , Lund University , and RISE Research Institutes of Sweden .

• Control Systems
• Signal Processing
• Sensor Fusion
• +7 more

Fredrik Gunnarsson is an Adjunct Professor at Linköping University's Department of Electrical Engineering (ISY), specializing in Automatic Control (RT). His research focuses on control systems, signal processing, and sensor fusion, with applications in positioning, navigation, and optimization for autonomous systems. University: Linköping University Department: Department of Electrical Engineering (ISY) Academic Rank: Adjunct Professor Research Interests Fredrik's work spans advanced signal processing, sensor fusion, and control algorithms. He contributes to 5G positioning, Gaussian process state-space models, and indoor localization. His research intersects with optimization-based control for autonomous systems and machine learning techniques in dynamic environments. Article Trends His recent publications emphasize wireless positioning, statistical filtering, and sensor fusion methodologies. Topics include Kalman filtering, Gaussian process modeling, particle methods for indoor navigation, and optimization algorithms for control systems. Keywords span Control Systems , Signal Processing , Machine Learning , and Wireless Communication . Labs and Collaborations LINK-SIC - Linköping Center for Sensor Informatics and Control Automotive Systems research SEDDIT competence center

Marian CodreanuView profile

Associate Professor

• Wireless Networking
• Signal Processing
• Machine Learning
• +3 more

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Theoretical Physics
• Surface Science
• Heterogeneous Catalysis
• +3 more

Anders Hellman is a Professor at the Department of Physics, Chalmers University of Technology, Sweden. He is also affiliated with the Competence Centre for Catalysis and serves as a senior strategic advisor to the Area of Advance (AoA) Energy at Chalmers. His research focuses on theoretical physics, surface science, heterogeneous catalysis, and materials for energy applications. He holds a PhD in theoretical physics from the University of Gothenburg (2003), followed by postdoctoral studies at Haldor Topsoe A/S, DTU Denmark, and Chalmers. His research interests include computational modeling of catalytic processes, energy materials design, and first-principles analysis of reaction mechanisms. Key areas of exploration involve chemical-looping combustion, catalyst screening for ethylene epoxidation, and the development of novel oxygen carriers for energy systems. He has contributed to advancements in understanding surface phenomena, nanoparticle behavior, and the interplay between material properties and catalytic performance. Dr. Hellman’s work bridges theory and experiment, with notable contributions to methane-to-methanol conversion, hydrogen evolution reactions, and photoelectrochemical systems for water splitting. His interdisciplinary approach integrates quantum chemistry, kinetic modeling, and materials discovery to address challenges in sustainable energy and catalytic innovation.

Daniel JönssonView profile

Associate Professor

• Visualization
• Machine Learning
• Medical Imaging
• +3 more

Associate Professor Daniel Jönsson works at the intersection of visualization and machine learning at Linköping University's Department of Science and Technology (ITN) and Media and Information Technology (MIT) division. He leads research in transforming complex datasets into intuitive visual representations, with applications in medical imaging and neuroscience. As a core developer of the Inviwo visualization software, he emphasizes interdisciplinary collaboration across fields like AI and radiology. His work includes developing tools like VisualNeuro for brain cohort studies and exploring neural network interpretability. Research focuses on visualization techniques for high-dimensional data, including fMRI analysis and neural network exploration. Notable achievements include an honorable mention at the IEEE VIS conference (2016) for brain imaging visualization work. Collaborations with CMIV (Center for Medical Image Science and Visualization) highlight his commitment to clinical and medical research applications. Publications emphasize combining visualization with machine learning, addressing challenges in data readiness for AI projects and improving neural network analysis. His contributions span software development, algorithm design, and cross-disciplinary methodological advancements in data-driven decision-making.

Graham HillView profile

Researcher

• Geophysics
• Volcanology
• Magmatic Systems
• +4 more

Dr. Graham Hill is a Senior Research Scientist and leads the Electromagnetics Research Group at the Institute of Geophysics, Czech Academy of Sciences. His expertise lies in electromagnetic induction theory, inverse theory, and the application of magnetotelluric (MT) methods to study magmatic systems, volcanism, and geothermal systems. He has pioneered MT applications in Antarctic tectonics and volcanic monitoring. Current research focuses on temporal MT for volcano and geothermal monitoring, and the mechanisms of andesite melt generation. Education: Ph.D. in Geophysics (not specified further in text). Awards: New Zealand Geophysics Prize, Antarctic Science Medal, Lumina Quaeruntur Fellow. Research Interests: Hill's work bridges geophysics and volcanology, emphasizing: Electromagnetic imaging of magmatic systems Volcanic hazard assessment via MT Crustal-scale tectonic processes in Antarctica and New Zealand Geothermal reservoir characterization Publications: Over 10 significant contributions since 2009, including groundbreaking work on Tongariro eruption mechanics and Antarctic crustal uplift. His 2020 review in Surveys in Geophysics highlights polar geophysical imaging advancements. Advising & Grants: Leads large-scale MT projects (e.g., Antarctic studies with University of Utah). Collaborates internationally on volcano monitoring and subduction zone dynamics. Labs/Teams: Electromagnetics Research Group at the Institute of Geophysics, Czech Academy of Sciences.

Per AnderssonView profile

Senior Lecturer

• Embedded Systems
• Code Generation
• Optimization
• +7 more

Per Andersson is a Senior Lecturer at the Department of Computer Science, Lund University. He serves as Director of first and second cycle studies and holds additional roles at ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the Parallel Systems group. His research focuses on embedded systems, code generation, optimization, and reconfigurable hardware design. He contributes to UN Sustainable Development Goals through technology advancements. Professional roles include leadership in academic program administration and participation in major initiatives like the EASE project (Embedded Applications Software Engineering, 2008-2018). His work bridges computer science and education policy, particularly in recognition of prior learning (RPL) and higher education accreditation processes. Research interests span technical domains such as parallel architectures, reconfigurable systems, and medical informatics, alongside educational topics like lifelong learning systems. His 20+ publications reflect interdisciplinary engagement, with notable contributions in IEEE conferences and journals like Haematologica and International Journal of Lifelong Education. He collaborates extensively with industry and academia, evidenced by participation in projects involving software engineering, radio standard integration, and clinical trial analyses. His work emphasizes practical applications of theoretical advancements in both technical and educational fields.

Ming ZhaoView profile

Lecturer

• Industrial Economics
• Statistics
• Reliability Engineering
• +2 more

Ming Zhao is a Lecturer at the University of Gävle's Academy of Technology and Environment, teaching industrial economics, statistics, reliability engineering, and logistics simulation. Research focuses on reliability modeling, statistical methods, and quality management, with applications in software, manufacturing, and sensor networks. Key interests include masked data analysis for reliability estimation, copula-based modeling of dependent failures, and Bayesian methods for system reliability. Publications emphasize practical frameworks for software reliability and manufacturing system performance. Article trends show consistent innovation in reliability estimation techniques, particularly for systems with incomplete data. Recent work integrates statistical methods with industrial applications like reconfigurable manufacturing and wireless sensor networks. No awards, supervised students, or lab details are documented.

• Protein Structure Prediction
• Protein Evolution
• Computational Biology
• +5 more

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.

Sindri MagnússonView profile

Associate Professor

• Distributed Optimization
• Machine Learning
• Artificial Intelligence
• +4 more

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.

Stefan EngblomView profile

Professor

• Scientific Computing
• Numerical Methods
• Fast Multipole Methods
• +3 more

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.

Erik ElmrothView profile

Professor

• Distributed Systems
• Cloud Computing
• Edge Computing
• +3 more

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.

• Computer Science
• Parallel Processing
• Heuristics
• +2 more

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

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +4 more

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.