Find Academic Supervisors

Jonatan LangletView profile

Research Fellow

• Computer Networking
• Network Telemetry
• Programmable Hardware
• +4 more

Jonatan Langlet is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Software and Computer Systems (SCS) and the Network Systems Lab (NSLab). Holding a Digital Futures fellowship, his work bridges programmable hardware, network telemetry, and machine learning, with a focus on high-speed monitoring and security technologies embedded in network switches and cards. Education: PhD in Computer Science (2024) from Queen Mary University of London, supervised by Prof. Gianni Antichi Teaching: Distributed Systems (QMUL 2021-2022), Data Structures and Algorithms (Karlstad 2019) Research spans three main areas: In-Network Intelligence : Developing machine learning inference capabilities within programmable switches (P4), optimizing neural networks for hardware constraints Telemetry Systems : Creating RDMA-based telemetry pipelines for zero-CPU, line-rate data collection (Direct Telemetry Access, ACM SIGCOMM 2023) 5G Network Innovation : Designing programmable pipelines for 5G user plane functions (IEEE TMC 2022) His publications in top venues like ACM SIGCOMM, SIGMETRICS, and HotNets demonstrate technical depth in programmable hardware, while community roles (TPC member, reviewer) highlight academic engagement. The Digital Futures fellowship supports his ongoing work on large-scale monitoring technologies.

• Electromagnetics
• Computational Electromagnetics
• Microwave Engineering
• +4 more

Thomas Rylander is an Assistant Professor in the Signal Processing research group at Chalmers University of Technology. His research focuses on electromagnetics, computational methods, and microwave engineering, with applications in antenna modeling, wireless power transfer, and electromagnetic compatibility. He has led projects such as Modeling of RF emissions from e-axis (MORFex) (2024–2028) and Säker induktiv energiöverföring för elfordon (2014–2017). His work integrates advanced numerical techniques like the Method of Moments (MoM) and Finite Element Method (FEM) to solve complex electromagnetic problems. Education: PhD in Electromagnetics (2001, Chalmers University) Research Keywords: Electromagnetics, Computational Electromagnetics, Microwave Engineering, Signal Processing, Wireless Power Transfer, Finite Element Method, Method of Moments Projects: MORFex (2024–2028, funded by Energimyndigheten), Virtual Electric Driveline (2018–2022, Vinnova), FFI SAWE (2014–2017, Energimyndigheten), Model-Based Reconstruction (2011–2014, VR) His recent publications emphasize efficient electromagnetic modeling techniques, including macro basis functions for wire antennas and compressed sensing for microwave imaging. Despite extensive collaboration with researchers like Matthys M. Botha and Johan Winges, no specific scientific awards or advisees are mentioned in the provided data.

Chao RenView profile

Researcher

• Quantum Federated Learning
• Federated Learning
• Quantum Computing
• +3 more

Chao Ren is a Wallenberg-NTU Presidential Principal Researcher (Forskare) at KTH Royal Institute of Technology's Department of Intelligent Systems. He holds a Ph.D. from Nanyang Technological University (NTU), Singapore, where he received the prestigious Graduate College Research Excellence Award. His research focuses on Quantum Federated Learning, Power Engineering, and Big Data Analytics for smart grid stability assessment. Ren has held postdoctoral fellowships at NTU and KTH, collaborating with leading experts like Prof. Mikael Skoglund (IEEE Fellow). Education: Ph.D. in Interdisciplinary Graduate Programme, NTU (2017–2022) B.E. in Computer Science, Nanjing University of Aeronautics and Astronautics (2013–2017) Research Interests: Combining quantum computing with federated learning to enhance cybersecurity and efficiency in power systems. Key areas include dynamic security assessment, adversarial robustness, and battery degradation prediction. His work bridges theoretical advancements and practical applications in smart grids and distributed systems. Awards & Grants: Winner of Graduate College Research Excellence Award (NTU, 2022) Wallenberg AI, Autonomous Systems & Software Program (WASP) grant (SEK 300,000) NTU grant for 'Modern Smart Grid Stability Assessment' (S$100,000) Professional Activities: Guest Lecturer at KTH, Editorial Board member for IET AI for Engineering, and reviewer for top conferences like NeurIPS and AAAI. Active in organizing workshops on federated learning and quantum computing. Labs & Teams: Leads the Trustworthy Federated Ubiquitous Learning Research Lab (AI Singapore-TrustFUL) and collaborates with the Singapore Power Group-NTU Joint Lab. Focuses on interdisciplinary projects involving energy systems, AI, and quantum technologies.

Giuseppe BelgioiosoView profile

Assistant Professor

• Control Systems
• Game Theory
• Optimization Algorithms
• +4 more

Giuseppe Belgioioso is an Assistant Professor at the Division of Decision and Control Systems within the Digital Futures Faculty at KTH Royal Institute of Technology. His research focuses on control systems, game theory, and optimization algorithms applied to complex systems such as smart grids, data centers, and multi-agent networks. He collaborates with Stockholm University and RISE Research Institutes of Sweden through the Digital Futures initiative. His work bridges theoretical foundations with practical applications in energy markets, networked systems, and algorithmic fairness. Belgioioso’s research interests include distributed optimization, equilibrium seeking in games, and data-driven control methodologies. He explores carbon-aware computing strategies for sustainable data centers and designs algorithms to mitigate polarization in recommendation systems. His contributions often integrate game-theoretic models with feedback control principles to address real-world challenges in energy systems, traffic routing, and peer-to-peer energy trading. His recent work emphasizes online learning and model-free approaches for dynamic systems, with applications to multi-area power grids and autonomous decision-making. He has published extensively on topics such as hypergradient-based optimization, decentralized equilibrium seeking, and stability analysis of distributed algorithms. His research is characterized by a strong interdisciplinary approach, combining systems theory with practical engineering solutions.

• Machine Learning
• Electronic Health Records (EHR)
• Precision Medicine
• +2 more

Emmanuella Ama Wieba is a doctoral student at the Center for Applied Intelligent Systems Research (CAISR) within Halmstad University's School of Information Technology. She holds a Bachelor's and Master's degree in Computer Science from the University of Botswana. PhD in Intelligent Systems (ongoing) Computer Science (BSc/MSc) Research Focus: Specializes in synthetic Electronic Health Records (EHRs) and machine learning applications in healthcare. Key areas include: Precision medicine through AI-enhanced EHR analysis Temporal fidelity evaluation in synthetic time-series medical data Cervical cancer risk prediction algorithms Bantu language identification using probabilistic models Development of EHR evaluation frameworks Publication Trends: Her work demonstrates expertise at the intersection of healthcare data science and machine learning, with recent publications focusing on synthetic data validation, medical risk modeling, and NLP applications for African languages. Key themes include temporal pattern analysis in EHRs, cervical cancer prediction methods, and data imputation techniques.

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +3 more

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

• Secure Control Systems
• Scalable Control
• Positive Systems
• +3 more

Sribalaji C. Anand is a postdoctoral researcher at KTH Royal Institute of Technology, Sweden, affiliated with the Division of Decision and Control Systems and the Department of Intelligent Systems. Hosted by Prof. Karl Henrik Johansson and Prof. Henrik Sandberg, his academic journey includes an M.Sc. in System and Control from Delft University of Technology (2019) and a Ph.D. in Automatic Control from Uppsala University (2024). Research Focus: Secure control systems, scalable control, positive systems, convex optimization applications, dissipative systems, and adaptive control. Grants: VR International Postdoctoral Grant (2024), STINT International Postdoctoral Scholarship (2024), and multiple travel scholarships from IEEE and Stenholm Wilgott. Publications: 15 recent articles covering security metrics, attack mitigation, and control system optimization.

• Variational inference
• Bayesian inference
• Deep learning
• +4 more

Najmeh Abiri is a researcher with multiple affiliations at Lund University, holding positions as a Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), a Visiting Research Fellow at Computational Science for Health and Environment, a Postdoc at the Department of Statistics, and a Researcher at eSSENCE: The e-Science Collaboration. Her research focuses on the intersection of statistics and machine learning with practical applications. Key areas include: Variational inference Bayesian inference Deep learning architectures Missing data imputation techniques Computational biology applications Abiri's publication record shows a clear trajectory toward increasingly sophisticated applications of machine learning in scientific domains. Her recent work demonstrates expertise in diffusion models for time series analysis, AI-driven disease outbreak prediction, and quantum mechanical applications of deep learning. Her 2019 paper on denoising autoencoders for missing data problems has been particularly influential with 62 citation indexes. She has received notable attention for her interdisciplinary work, with publications featured in high-impact journals like The Lancet Regional Health - Europe and covered by multiple news outlets. Her research on West Nile virus prediction has garnered policy citations, indicating real-world impact. Abiri actively participates in the academic community through workshops and seminars, particularly those focused on AI technologies and their applications in scientific research. Her collaborative work spans multiple institutions and research domains, reflecting the interdisciplinary nature of her expertise.

• Bioinformatics and Computational Biology
• Computer Graphics and Computer Vision
• Medical Imaging

Kimmo Kartasalo is a Senior Research Specialist and Assistant Professor at the Department of Medical Epidemiology and Biostatistics , Karolinska Institutet , Sweden. His research focuses on the intersection of Artificial Intelligence , Bioinformatics , and Medical Imaging , particularly in cancer diagnostics. Current roles: Senior Research Specialist (2024-), Assistant Professor (2024-2030) Supervision: Sol Erika Boman (2023), Kelvin Szolnoky (2022) His work emphasizes deep learning for histopathology analysis, including prostate cancer grading, breast cancer spatial gene expression prediction, and multi-stain whole-slide imaging. He leads projects in 3D tissue reconstruction , virtual reality histology visualization , and AI-assisted diagnostic uncertainty estimation . Collaborations span international institutions and include algorithm benchmarking challenges like PANDA and ACROBAT. Key methodological contributions include physical color calibration for digital pathology scanners, conformal prediction frameworks for diagnostic reliability, and open-source tools like OpenPhi for whole-slide image processing. His publications appear in top journals including Nature Medicine , Cancer Research , and European Urology .

Zheng ZhaoView profile

Assistant Professor

• Computational Statistics
• Machine Learning
• Generative Diffusion Models
• +4 more

Zheng Zhao is an Assistant Professor in computational statistics and machine learning at Linköping University, leading a research team within the Division of Statistics and Machine Learning (STIMA) under the Department of Computer and Information Science (IDA). His work focuses on developing computationally and statistically efficient models for machine learning applications, particularly generative diffusion models and their use in signal/image processing. He also applies these methods to fields like astronomy (e.g., stellar magnetic field analysis) and climate science. His research team emphasizes practical applications of diffusion models and Bayesian methods, contributing to advancements in image restoration, sensor array analysis, and high-dimensional data modeling. Zhao collaborates with STIMA colleagues on interdisciplinary projects, leveraging statistical theory and computational techniques. Publications highlight his exploration of diffusion models, Bayesian neural networks, and probabilistic methods across domains like astrophysics and environmental science. His work bridges theory and application, addressing challenges in uncertainty quantification and overfitting mitigation. Zhao is actively involved in academic initiatives such as the international master's program in Statistics and Machine Learning (hosted by STIMA). His research has been applied in competitions like the NTIRE challenge, demonstrating real-world impact in image restoration.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +4 more

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

• Secure Control Systems
• Scalable Control
• Positive Systems
• +3 more

Sribalaji Coimbatore Anand is a Researcher at the Department of Intelligent Systems, KTH Royal Institute of Technology, Sweden. Hosted by Professors Karl Henrik Johansson and Henrik Sandberg, his work focuses on secure control systems, scalable control, and applications of convex optimization. He holds an M.Sc. in System and Control from Delft University of Technology (2019) and a Ph.D. in Automatic Control from Uppsala University (2024). His research emphasizes cyber-physical system security, including stealthy attacks, resilient control algorithms, and privacy-preserving mechanisms. Notable grants include the VR International Postdoc grant (2024) for analyzing secure large-scale networked control systems and the STINT grant (2024) exploring total positivity frameworks in secure network control. Education: M.Sc., System and Control, Delft University of Technology (2019) Ph.D., Automatic Control, Uppsala University (2024) He has received awards such as the IEEE Travel Scholarship (2022) and Dean’s List scholarships (2013–2016). His work spans theoretical frameworks for detector placement, optimal security allocation, and game-theoretic approaches to sensor design in cyber-physical systems. Current projects involve collaborations with Prof. George Pappas (University of Pennsylvania) and Christian Grussler (Technion, Israel), focusing on scalable security metrics and resilient control architectures.

• Autonomous Systems
• Robotics
• Computer Vision
• +3 more

Daniel Duberg is a Researcher and PhD candidate at the Division of Robotics, Perception and Learning (RPL) within the School of Electrical Engineering and Computer Science at Kungliga Tekniska Högskolan (KTH). His research focuses on autonomous exploration and real-time 3D mapping using unmanned aerial vehicles (UAVs), emphasizing onboard processing for indoor environments. He has contributed to frameworks like UFOMap, which address dynamic environments and efficient data structures. His work integrates probabilistic modeling, sensor fusion, and real-time algorithms to enhance UAV navigation and decision-making. Notable projects include dynamic-aware mapping (DUFOMap) and exploration strategies guided by formal methods like Signal Temporal Logic. Duberg teaches engineering courses in robotics and collaborates on open-source tools for autonomous systems. His research trends emphasize scalability, robustness in uncertain conditions, and adaptive planning for long-term autonomy. He has published widely on exploration algorithms, volumetric mapping, and UAV navigation challenges, with applications in both static and dynamic environments. Duberg’s academic contributions span over a decade, with early work on tele-operation safety and recent advancements in semantic mapping and multi-sensor fusion. His research bridges theoretical foundations with practical implementations for deployable robotic systems.

Arunava NahaView profile

Assistant Professor

• Control Systems
• Cyber-Physical Systems Security
• Reinforcement Learning
• +6 more

Arunava Naha is an Assistant Professor in the Department of Electrical Engineering at Linköping University, Sweden, affiliated with the Information Coding (ICG) research group. His work bridges control theory, cybersecurity, and machine learning, focusing on secure and reliable operation of networked and cyber-physical systems. His research interests include: Optimal and risk-constrained control under uncertainty Reinforcement learning for control systems Detection of deception and replay attacks in cyber-physical systems Physical watermarking for active intrusion detection Sequential change detection and statistical monitoring The recent publications show a strong trend in applying machine learning and stochastic control techniques to enhance the security and resilience of networked control systems. His work often involves probabilistic constraints, model-free methods, and quickest detection frameworks, contributing to both theoretical advances and practical solutions in critical infrastructure protection. Scientific awards: No awards mentioned in the provided text. Advising and grants: No information available on students, advising, or funded grants. Labs, teams, and research groups: Arunava Naha is part of the Information Coding (ICG) group within the Department of Electrical Engineering at Linköping University, which conducts research in communication, signal processing, and control systems with applications in secure and reliable information transmission and system operation.