Find Academic Supervisors

• Information Security
• Cybersecurity
• Software Architecture
• +5 more

Mathias Ekstedt is a Professor of Industrial Information and Control Systems at KTH Royal Institute of Technology in Stockholm, Sweden. Affiliated with the Software Systems Architecture and Security (SSAS) research group under the Division of Network and Systems Engineering in the Department of Computer Science, School of Electrical Engineering and Computer Science (EECS). He holds a Ph.D. (2004) and M.Sc. (1999) from KTH. Co-founder of foreseeti (acquired by Google in 2022) Director of KTH's Master Programme in Cybersecurity Member of the Digital Futures research center's Trust Working Group Affiliated with Center for Cyber Defense and Information Security (CDIS) Ekstedt's research focuses on information security and cybersecurity integrated with software/systems architecture modeling and analysis . His work emphasizes probabilistic attack/defense graphs for system-of-systems security evaluation, particularly in SCADA and Industrial Control Systems within power infrastructure. He has developed the Meta Attack Language (MAL) and collaborated extensively with the power industry. Recent research trends (2022-2025) include: Threat modeling languages for ICT and power systems Probabilistic attack graph applications in energy flexibility markets and smart cities Software Bill of Materials (SBOM) challenges in Java ecosystems Dynamic attack graph generation and lazy evaluation techniques Cyber-physical security for low-voltage grids and substation automation Ekstedt supervises 17 Ph.D. students (11 as main supervisor, 6 as co-supervisor) and has mentored numerous postdocs. He leads major projects like the SSF project CHAINS (software supply chain security) and coordinates initiatives within SweGRIDS , HONOR , and SOCCRATES .

• Business Process Management
• Process Mining
• Object-Centric Process Mining
• +6 more

Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.

Markus H. FlierlView profile

Associate Professor

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +3 more

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

• Control Engineering
• Process Control
• Optimal Control
• +11 more

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +6 more

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

Alexandros SopasakisView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +9 more

Alexandros Sopasakis is a Senior Lecturer at the Department of Mathematics, Faculty of Engineering (LTH), Lund University. He is affiliated with multiple research initiatives including eSSENCE (the e-Science Collaboration), ELLIIT (Linköping-Lund IT and mobile communication initiative), and LU Profile Areas in Natural and Artificial Cognition. Senior Lecturer in Mathematics Principal Investigator for eSSENCE Active in LU Profile Areas: Climate, Health, and AI Research Interests: His work bridges machine learning, dynamical systems, and applied mathematics, focusing on: Stochastic modeling of complex systems Attention-based forecasting (Transformers) Diffusion models for synthetic data generation Graph neural networks in transportation and agriculture Anomaly detection in signal processing Climate modeling under non-Gaussian noise Scientific Awards: Recipient of three patents in: 5G/6G mobility devices Beam management measurement optimization Traffic network forecasting

• Machine Learning
• Network Engineering
• Graph Neural Networks

Rémi Bourgerie is a doctoral student and researcher at KTH Royal Institute of Technology , affiliated with the Division of Network and Systems Engineering . His work focuses on integrating machine learning techniques with network systems. Educational background: Engineering degree from École Centrale de Lyon (2023), MSc in Machine Learning from KTH (2023). Rémi’s research specifically explores Graph Neural Networks for telecom network fault detection. His publications highlight the application of bi-level federated graph neural networks in this domain. As a teaching assistant, he contributed to courses such as Data Mining (ID2211) and Introduction to Computing Systems Engineering (EP1200) . Contact: remibo@kth.se .

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Robotics
• Autonomous Systems
• Spatial Cognition
• +6 more

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

• Machine Learning
• Health Informatics
• Disease Surveillance
• +3 more

Atiye Sadat Hashemi is a Research Fellow at the Academy of Information Technology, Halmstad University. Her work bridges machine learning and healthcare innovation, with a focus on developing secure and interpretable AI models for medical applications. She maintains an active research profile through publications and collaborative projects in data-driven healthcare solutions. Research Focus: Her expertise spans machine learning applications in disease surveillance, precision medicine, and adversarial robustness. Key areas include: Optimizing ML for real-time disease outbreak detection using anomaly detection Advancing personalized treatment strategies through data-driven models Enhancing model security against adversarial attacks in critical domains Developing privacy-preserving synthetic health data generation techniques Publication Trends (2022-2024): Her recent works demonstrate a consistent focus on healthcare-AI integration, with emphasis on: Anomaly detection systems for epidemiological monitoring Privacy-enhancing technologies for medical data Explainable AI methods for clinical decision support Robustness improvements for safety-critical ML applications Methodologies frequently involve generative adversarial networks, graph neural networks, and time-series analysis.

Christiane SchmidtView profile

Academic

• Algorithm Design and Analysis
• Computational Geometry
• Air Traffic Management
• +3 more

Christiane Schmidt is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Communications and Transport Systems (KTS) research group. Her work focuses on algorithm design, air traffic management, railway optimization, and computational geometry. She contributes to both academic research and practical solutions for transportation systems. Education and Research: While specific educational details are not provided, her research emphasizes algorithmic solutions for complex transportation challenges. Her team develops models for air traffic controller workload prediction using machine learning and explores late train-crew rescheduling via heuristic approaches. Research Interests: Key areas include railway traffic optimization, air traffic management systems, and computational geometry applications. Recent projects involve guard route optimization for polygons, multi-agent path planning, and automated aircraft arrival scheduling. Publications: Her 2025 work highlights advancements in tabu-search-based scheduling, machine learning for air traffic analysis, and train path insertion methods. Earlier papers address workforce scheduling and disaster recovery in transportation networks. Labs and Teams: Active within the Communications and Transport Systems group, she collaborates on projects like the Flight Logistics Unit (FL) and contributes to initiatives like B-PREPARED for disaster preparedness.

• Theoretical Particle Physics
• Neutrino Physics
• Dark Matter Physics
• +3 more

Sandhya Choubey is a Professor at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the School of Particle and Astrophysics and Medical Imaging. Her research focuses on theoretical particle physics, particularly neutrino physics and dark matter, with specialization in neutrino oscillations, dark matter signatures, and experimental collaborations like Hyper-Kamiokande, ESSnuSB, and DUNE. She contributes to major international projects such as the India-based Neutrino Observatory (INO) and the Deep Underground Neutrino Experiment (DUNE). Choubey teaches advanced physics courses including Quantum Field Theory, Theoretical Particle Physics, and Symmetries in Physics. Her work explores neutrino properties, including mass hierarchy, CP violation, and non-standard interactions, alongside implications for dark matter and cosmology. She has been instrumental in designing detector technologies and analyzing experimental data from leading neutrino observatories. Her research also addresses fundamental questions in physics, such as the connection between neutrino masses, baryogenesis, and dark matter candidates. She frequently collaborates with global teams to advance neutrino physics and astroparticle studies, contributing to both theoretical frameworks and experimental implementations.

• Community Detection
• Graph Clustering
• Graph Representation Learning
• +4 more

Susanna Pozzoli is a doctoral student at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. She also serves as the vice chairperson of the EECS PhD Council, representing doctoral students at the school level. Education: Bachelor of Science in Engineering of Computing Systems from Politecnico di Milano (2017) EIT Digital Master's Programme in Data Science (2017–2019), completed first year at Politecnico di Milano and second year at KTH in Stockholm, Sweden Research Interests: Susanna’s work focuses on advancing graph-based machine learning techniques, including community detection, graph clustering, role discovery, and unsupervised learning. She explores how graph representation learning can enhance performance evaluation in complex networks, with applications ranging from social network analysis to biomedical data science. Publications: Her recent work highlights innovations in graph embedding techniques and diffusion-driven role recognition, alongside interdisciplinary research in domain-agnostic feature selection for breast cancer analysis. These studies reflect her expertise in bridging theoretical computer science with practical data-driven solutions. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Teams: While no specific lab affiliations are listed, her research is embedded within the collaborative environment of KTH’s SCS division.

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Engineering
• Data Analytics
• +4 more

Dr. Enayat Rajabi is an Associate Professor of Data Analytics at the Shannon School of Business , Cape Breton University , Canada. He also serves as an Adjunct Professor at Dalhousie University and is affiliated with Nova Scotia Health as a scientist. His academic journey included a Ph.D. in Information and Knowledge Engineering from the University of Alcalá, Spain, and he has contributed extensively to machine learning and semantic web domains. Education: Ph.D. in Information and Knowledge Engineering, University of Alcalá, Spain (2015) Master of Software Engineering, Ferdowsi University of Mashhad, Iran (2004) Bachelor of Software Engineering, Razi University, Iran (2001) Dr. Rajabi's research focuses on machine learning , knowledge engineering , and semantic web applications in healthcare and smart cities. His work explores explainable AI frameworks, knowledge graph construction, and data-driven solutions for sustainable transportation and clinical decision support systems. His recent publications highlight trends in knowledge graph integration with large language models for healthcare, graph neural networks , and predictive analytics in urban environments. He has secured significant grants, including the NSERC Discovery Grant and Mitacs Research Training Award , to advance these domains. Scientific Contributions: NSERC Discovery Grant (2020-2025) - Semantic Web Analysis over Nova Scotia Open Data ($156,000) New Health Investigator Grant (2022-2024) - Machine Learning for ALC Patients ($97,418) Mitacs Globalink ($4,250) - Graph Neural Networks CBU RISE grants for Explainable Clinical Decision Support Systems and Multi-Label Text Classification Dr. Rajabi has mentored numerous research assistants across projects and maintains active collaborations with institutions in Canada, Spain, and Iran. His technical expertise spans Python, Tableau, Databricks, and PySpark, with teaching responsibilities in Predictive Analytics , Data Visualization , and Quantitative Methods .