جست‌وجوی استاد و پژوهشگر

Peter Bangمشاهده پروفایل

استاد

• Pediatric Endocrinology
• Growth Disorders
• Insulin-like Growth Factor (IGF-I) Research
• +۴ مورد دیگر

Peter Bang is a Professor of Pediatrics and Head of Division at the Division of Children's and Women's Health (BKH) within the Department of Biomedical and Clinical Sciences (BKV) at Linköping University. His academic career includes a PhD from the Karolinska Institute, postdoctoral training at Stanford University, and clinical expertise in pediatric endocrinology. Education: Graduated from the Engineering School, Technical University of Copenhagen; Medical School, University of Copenhagen; PhD under Prof. Kerstin Hall at Karolinska Institute. Completed pediatric endocrine training at Karolinska University Hospital. Research focuses on GH-IGF axis regulation, growth disorders, diabetes, and neonatal health, with 50+ peer-reviewed publications. Key projects include the Global Increlex Registry analyzing rhIGF-1 therapy and the EU-IGFD registry for SPIGFD patients. Labs/Teams: Leads the Division of Children's and Women's Health (BKH), collaborating on multidisciplinary pediatric and妇幼 health research. Active in clinical trials and registry-based studies for rare endocrine disorders.

• Federated Learning
• Distributed Machine Learning
• Wireless Networks
• +۲ مورد دیگر

Seyedmohammad Azimi Abarghouei is a Senior Researcher at the Division of Network and Systems Engineering, KTH Royal Institute of Technology, collaborating with Prof. Carlo Fischione. Previously, he served as a Postdoctoral Fellow in the same division from 2022 to 2024 under Prof. Viktoria Fodor. He completed his Ph.D. at Sharif University of Technology in Iran in November 2020, where his research on wireless networks earned the national Best PhD Thesis Award in electrical engineering and information technology. His educational background includes: Ph.D. in Electrical Engineering and Information Technology, Sharif University of Technology, Iran (2020) Dr. Azimi Abarghouei's research centers on distributed machine learning systems, particularly federated learning architectures for wireless networks. His work develops hierarchical aggregation methods, over-the-air computation techniques, and lattice coding approaches to address communication efficiency and data heterogeneity. He also contributes to molecular communication and stochastic geometry for nanonetwork modeling, bridging theoretical foundations with practical wireless applications. Analysis of his recent publications reveals a dominant focus on scalable federated learning systems, with significant innovation in hierarchical structures, quantization techniques, and interference-aware aggregation. His work consistently targets real-world challenges in edge intelligence, including bandwidth constraints, non-IID data distributions, and massive device coordination in IoT environments. His scientific recognition includes: Best PhD Thesis Award in Electrical Engineering and Information Technology (National Level, Iran) Jubilee Appropriation Scholarship from Knut and Alice Wallenberg Foundation (2023) Exemplary Reviewer of IEEE Wireless Communications Letters (2022, 2023, 2025) Exemplary Reviewer of IEEE Communications Letters (2023) Dr. Azimi Abarghouei has secured competitive research funding including the Knut and Alice Wallenberg Foundation's Jubilee Appropriation Scholarship. His active peer review contributions and conference tutorial leadership (e.g., IEEE ICMLCN 2025) demonstrate significant community engagement. While specific grant details beyond the scholarship aren't fully documented, his publication record indicates sustained support for federated learning research through institutional and foundation channels. He operates within Prof. Carlo Fischione's research group at KTH's Division of Network and Systems Engineering, focusing on networked machine learning systems. His collaborative work spans international conferences and journals, with recent emphasis on federated learning standardization through tutorials and preprints that shape emerging research directions in distributed AI.

Carolina Bergelingمشاهده پروفایل

مدرس ارشد

• Brain-Computer Interfaces
• Control Engineering
• Marine Engineering Systems
• +۳ مورد دیگر

Carolina Bergeling is a Senior Lecturer in control engineering at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH), a position she has held since January 2022. Her research spans the interdisciplinary fields of Brain-Computer Interfaces, control engineering, and marine engineering systems. Dr. Bergeling's research interests focus on: Brain-Computer Interfaces and neural signal processing Control of complex systems, particularly in marine environments H-infinity optimal control theory and applications Systems engineering approaches to multi-agent and distributed systems Her recent publications demonstrate a strong focus on applying advanced control theory to Brain-Computer Interface technologies, with particular emphasis on tensor decomposition methods, domain adaptation techniques, and multi-armed bandit approaches for improving EEG signal processing and neural decoding. She also maintains an active research program in H-infinity optimal control, with contributions to theoretical foundations and practical applications in marine engineering contexts. Dr. Bergeling's scientific contributions include: Advancements in tensor decomposition techniques for transfer learning applications in BCI Novel methods for improving EEG-based decoding of auditory attention through domain adaptation Applications of multi-armed bandit theory to brain-computer interface calibration Significant contributions to H-infinity optimal control theory for various system classes She teaches courses in control engineering and robotics, and serves as a supervisor for projects in the Marine Engineering program at BTH. Her ongoing research project focuses on "Swarming technology for cooperative surface and underwater vehicles (drones)," demonstrating her commitment to applying theoretical control concepts to practical marine engineering challenges.

Magnus Janssonمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +۳ مورد دیگر

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

• Machine Learning
• Stochastic Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Alexandre Proutiere is a Professor of Communication Networks at KTH Royal Institute of Technology, specializing in stochastic optimization and machine learning. His research focuses on developing mathematical tools to optimize machine learning algorithms and manage dynamic systems, particularly in the context of autonomous systems like robots and self-driving vehicles. He leads a research team advancing techniques for data analysis, network performance verification, and reinforcement learning. His academic contributions span theoretical advancements in reinforcement learning, multi-agent systems, and statistical methods for change point detection. Proutiere's work bridges practical applications in telecommunications and robotics with foundational mathematical rigor. He collaborates on projects involving adaptive control algorithms, distributed optimization, and the design of efficient learning frameworks for complex environments. Key areas of investigation include optimal policy identification in Markov decision processes, low-rank matrix estimation in reinforcement learning, and conformal prediction under Markovian data. His research has implications for improving network efficiency, autonomous system decision-making, and real-time data-driven control mechanisms.

Olga Mulaمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +۳ مورد دیگر

Olga Mula is an Associate Professor of Mathematics at TU Eindhoven and will transition to a Full Professor position at the University of Vienna starting September 2025. Her research focuses on numerical analysis of Partial Differential Equations (PDEs) combined with data-driven methodologies, including Scientific Machine Learning, High-Dimensional Approximation, and Optimal Transport. She leads the Computational PDEs group and has held roles at institutions like INRIA Paris and Paris Dauphine University. Mula's work addresses challenges in computational science, such as pollution modeling, epidemiology, and nuclear engineering. She currently seeks PhD and Postdoc candidates for projects on Wasserstein Gradient Flows and singularly perturbed PDEs. Education: PhD in Applied Mathematics (Sorbonne University, 2014), French Habilitation (Paris Dauphine University, 2021) Research Interests: Machine Learning for PDEs, Data Assimilation, Numerical Optimal Transport Her contributions include advancements in PINN frameworks, model order reduction, and sensor placement strategies. She teaches courses on Numerical Analysis, Optimization, and Mathematics of Neural Networks at TU Eindhoven.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +۳ مورد دیگر

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .