Find Academic Supervisors

• Multi-Agent Systems
• Control Systems
• Robotics
• +4 more

Zhiqi Tang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology's Division of Decision and Control Systems (EECS Department), supervised by Professors Jonas Mårtensson and Karl Henrik Johansson. She previously held positions as a postdoctoral researcher at Instituto Superior Técnico (IST) in Portugal and as an Invited Assistant Professor in their Department of Electrical and Computer Engineering. She holds a double Ph.D. in Automatic Control and Robotics from IST and I3S-CNRS (Université Côte d'Azur), receiving the EDSTIC Ph.D. thesis prize, and a B.S. in Electrical and Computer Engineering from the University of Macau (2015). Her research focuses on estimation, control, and decision-making in multi-agent systems, with applications in Robotics and Transportation Systems. Key areas include cooperative estimation, networked systems, nonlinear dynamics, and formation control. She teaches the Nonlinear Control (EL2620) course at KTH. Her recent work emphasizes safe platooning for autonomous vehicles, collision avoidance in UAV formations, and distributed localization under dynamic conditions. Notable contributions include barrier function-based control for CAVs and bearing-rigidity theory for multi-agent coordination. Her research bridges theoretical control frameworks with practical implementations in robotics and transportation. Awards: EDSTIC Ph.D. thesis prize (2021) Grants/Advising: Supervises postdoctoral and doctoral research in multi-agent systems Labs/Teams: Involved with KTH's Decision and Control Systems group and IST's Institute for Systems and Robotics

Jonas Hultin RosenbergView profile

Associate Professor

• Political Philosophy
• Political Sociology
• Democratic Inclusion
• +4 more

Jonas Hultin Rosenberg is an Associate Professor of Political Science at Mälardalen University's School of Business, Society and Engineering (Division of Economy and Political Science). He holds a PhD from Uppsala University (2017) and was appointed docent (associate professor) there in 2023. His research focuses on political philosophy, political sociology, and the intersection of AI with democratic systems. Current roles include Guest Researcher at Uppsala University's Centre for Multidisciplinary Research on Religion and Society (CRS) and associate editor of Philosophy & Technology . Research interests emphasize democratic inclusion criteria, political equality, AI's societal impact, and citizenship. Key themes include justification of democracy, voter turnout among marginalized groups (notably people with intellectual disabilities), and the ethical implications of algorithmic governance. His work bridges normative theory with empirical analysis, addressing questions like 'Who should be included in democratic processes?' and 'How does technology reshape political legitimacy?' Publications since 2012 reflect a trajectory from foundational democratic theory to contemporary challenges posed by AI and disability inclusion. Recent work explores AI's potential as both a disruptor and enabler of democratic values, alongside reinterpreting classical principles like the 'all-affected' criterion in modern contexts. He employs mixed methods, including conjoint experiments and longitudinal data analysis. Professional service includes editorial work for peer-reviewed journals and advisory roles in multidisciplinary research centers. Current projects likely continue investigating AI's socio-political dimensions and expanding inclusion frameworks to non-human actors (e.g., nature, AI systems).

• Machine Learning
• Federated Learning
• Data Mining
• +4 more

Addi Ait-Mlouk is a Senior Lecturer in Informatics at the School of Informatics, University of Skövde. He holds a prominent role as a Course Coordinator for multiple Master's level courses, contributing to advanced education in informatics and technology. His research focuses on machine learning applications in federated learning, privacy-preserving technologies, and industrial automation. Current projects include ProcTwin , a digital tool development initiative for optimizing steel production processes through machine learning and sensor data integration (2025–2028). Key research interests span federated learning for healthcare diagnostics, edge computing optimization, and privacy-enhanced AI systems. His 2024 publications highlight advancements in federated learning for leukemia diagnosis and brain tumor classification, emphasizing ethical AI integration. Earlier work includes contributions to data mining methodologies for road safety and smart grid security. Collaborations involve industry partners like ABB, RISE, and ShiftLabs, focusing on additive manufacturing and industrial digital transformation. Addi actively participates in academic events such as the Sweden Game Conference and INGENIUM European University initiatives, promoting interdisciplinary innovation.

• Biophysics
• Molecular Dynamics Simulations
• Native Mass Spectrometry
• +7 more

Erik Marklund is a researcher at Uppsala University, affiliated with the Department of Chemistry - BMC, Biochemistry, under the Disciplinary Domain of Science and Technology. His work bridges computational biophysics and experimental structural biology, with a focus on protein dynamics, mass spectrometry, and molecular simulations. His research interests include: Biophysics and molecular dynamics simulations Native mass spectrometry and ion mobility Protein-lipid and protein-DNA interactions Structural stability of viral capsids and membrane proteins X-ray single particle imaging and coherent diffractive imaging Liquid-liquid phase separation and functional amyloids His recent publications (2023–2025) reveal a strong trend in integrating computational modeling with experimental structural techniques, particularly in understanding protein conformational changes, stability, and assembly. His work spans from fundamental biophysical principles to applications in enzyme evolution and biomaterial design. Notable scientific contributions include: Development of high-performance molecular dynamics methods using the fast multipole method for large protein complexes Insights into fractal self-assembly in metabolic enzymes Engineering lipid-binding in artificial membrane proteins Advancing x-ray single particle imaging through orientation bias modeling He is involved in multiple funded research projects supported by the Swedish Research Council, EU Horizon 2020, Knut and Alice Wallenberg Foundation, and others, indicating active grant acquisition and collaborative research. While no direct students are listed, his frequent co-authorship with early-career researchers suggests mentoring roles. He is part of interdisciplinary teams including SciLifeLab and international collaborations in structural biology.

• Computer Science
• Software Engineering
• Distributed Systems
• +2 more

Johan Montelius is a Lecturer in the Department of Software Engineering and Computer Systems at the Royal Institute of Technology (KTH). His contact details include phone +46 8 790 42 83 and email johanmon@kth.se. He holds responsibilities as an examiner, course coordinator, and teacher for multiple courses, primarily focused on algorithms, data structures, and degree projects across computer science, computer engineering, and information technology domains. His research interests span Computer Science , Software Engineering , and Distributed Systems , with specific expertise in Algorithms and Data Structures as evidenced by his teaching portfolio. No scientific awards or prizes are documented in the available information. Montelius supervises numerous degree projects at bachelor and master levels across computer science and engineering disciplines. Specific student names are not provided. Labs, teams, or grants are not mentioned in the source material.

Björn MorénView profile

Assistant Professor

• Mathematical Optimization
• Brachytherapy Treatment Planning
• Radiotherapy
• +7 more

Björn Morén is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where his research focuses on mathematical optimization applied to brachytherapy treatment planning in radiation oncology. His work aims to improve cancer treatment outcomes through advanced computational models that enhance precision and robustness in radiotherapy. Research Interests: His primary research lies at the intersection of applied mathematics and medical physics. He specializes in developing and analyzing mathematical optimization models for high-dose-rate (HDR) brachytherapy, particularly for prostate and cervical cancers. His work addresses critical challenges such as rotational and positional uncertainties, dwell time optimization, and automated plan validation. He seeks to bridge the gap between manual clinical planning and algorithm-driven optimization to improve dosimetric outcomes and reduce complications. Publication Trends: His recent publications (2021–2025) demonstrate a consistent focus on robust optimization techniques in brachytherapy. Themes include uncertainty mitigation, dosimetric evaluation, inverse planning, and automation in treatment workflows. His interdisciplinary work combines operations research, medical imaging, and clinical oncology, reflecting strong collaboration across physics and medicine. Scientific Awards: No awards or honors are mentioned in the provided text. Advising and Grants: There is no information available about students advised or research grants received. However, his active publication record suggests ongoing funded research and potential mentorship roles within his research group. Labs and Teams: Björn Morén is part of a multidisciplinary research group at Linköping University focused on brachytherapy treatment planning. This group collaborates on developing optimization models for clinical use, working closely with medical physicists and oncologists to translate mathematical innovations into practical healthcare solutions.

György DánView profile

Professor

• Applied Game Theory
• Critical Infrastructure Security
• Edge Computing
• +5 more

György Dán is a Professor in Teletraffic Systems at KTH Royal Institute of Technology, Stockholm, Sweden. He holds a Ph.D. in Telecommunications from KTH (2006) and has extensive academic leadership roles, including Director of Third Cycle Education. His research focuses on networked systems design, with expertise in critical infrastructure security, edge computing, and cyber-physical systems. Dán has supervised over 20 PhD students and led major projects like VR DICE6G and REACT. He has authored/co-authored over 150 publications, receiving awards such as the Ericsson Research Foundation Award (2010-2014) and the 2020 KTH PhD Supervisor of the Year. Education Ph.D. in Telecommunications, KTH Royal Institute of Technology (2006) M.Sc. in Business Administration, Corvinus University of Budapest (2003) M.Sc. in Computer Engineering, Budapest University of Technology and Economics (1999) Research Interests His work spans applied game theory, smart grid security, mobile edge computing, and adversarial machine learning. Current projects include resilient edge computing systems and secure AI for cyber-physical infrastructures. Awards & Recognition Best Paper Awards at SNCNW 2025, IEEE SmartGridComm 2014, and IEEE Infocom 2008 Fulbright Visiting Scholar (University of Illinois, 2012-2013) Invited Professor at EPFL (2014-2015) Labs & Collaborations He leads the Network and Systems Engineering Division at KTH, collaborating with industries like Ericsson and ABB. His group focuses on experimental platforms like the GreenEyes testbed and stateful serverless edge computing.

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.

Paul TownendView profile

Associate Professor

• Energy-efficient distributed systems
• Sustainable computing
• Cloud computing
• +7 more

Paul Townend is an Associate Professor at Umeå University , Sweden, with a Docent qualification. He leads the Green Distributed Computing group and serves as Research Leader for the Autonomous Distributed Systems (ADSLab) lab. Scientific Coordinator for Horizon Europe COGNIT Co-Manager & Scientific Advisor for WASP WARA-Ops Member of WASP Graduate School Management Team His research focuses on energy-efficient and sustainable distributed systems , with a current emphasis on Edge-Cloud integration . Additional areas include fault tolerance, provenance, data center optimization, and simulation . He has supervised 5 PhD students and 2 postdocs , with expertise in IoT, machine learning for cloud management, and microservices . Best Paper Awards at SOSE 2013 and ISORC 2012 Principal Investigator for grants totaling over $5.8M , including projects like Adaptive Monitoring of Streaming Data and Energy-Aware Cloud-Edge Management

Ida ArvidssonView profile

Research Fellow

• Machine Learning
• Deep Learning
• Medical Imaging
• +7 more

Ida Arvidsson is a postdoctoral research fellow at the Centre for Mathematical Sciences , Faculty of Engineering, Lund University, Sweden. She holds a Ph.D. in Mathematics (2021) and an M.Sc. in Engineering Physics (2016) from Lund University. Active in eSSENCE: The e-Science Collaboration Involved in ELLIIT (Linköping-Lund initiative on IT and mobile communication) Member of Lund University's AI and Digitalization and Natural and Artificial Cognition profile areas Her research focuses on machine learning and deep learning applied to medical imaging, including prostate cancer grading , coronary artery disease detection , breast cancer prediction , and Alzheimer's disease diagnosis . This work contributes to the UN Sustainable Development Goals Good Health and Well-Being . Recent publications emphasize AI-driven diagnostics in ultrasound imaging, out-of-distribution detection, and trustworthiness in low-resource settings. Her collaborative projects span Sweden, Africa, and the US , with over 22 research outputs and 8 active/finished projects. Scientific Awards Oscar II stipendium for best dissertation at LTH (2023) IVAs 100-lista (2023) LU Innovation Award (2022) She supervises Nätterdahl, C. on skin cancer diagnosis in teledermoscopy and co-leads projects like Breast Point-of-Care Examination Trial . Her work is supported by grants including eSSENCE@LU 10:7 .

• Machine Learning
• Data Mining
• Graph Theory
• +3 more

Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.

• Computer Networks
• Internet of Things (IoT)
• Network Security
• +4 more

Markus Hidell is an Associate Professor at the Royal Institute of Technology (KTH) , specializing in computer networks, IoT systems, and network security. His teaching includes courses like Advanced Internet Technology and Security and Data Privacy on the Internet , where he serves as examiner or teacher. Research Focus : IoT architectures, energy-efficient networking, and security in distributed systems. Recent Publications : Investigate IoT scalability, buffer management algorithms, SDN security, and LTE vulnerabilities. Key Technical Areas : Network virtualization, delay-tolerant systems, and smart city infrastructure.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +3 more

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 .

Jonas SpengerView profile

Researcher

• Distributed Systems
• Programming Languages
• Cryptography
• +3 more

Jonas Spenger is a Researcher at the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . Education : BSc and MSc in Computer Science from Humboldt-Universität zu Berlin . Experience : Previously worked at RISE Research Institutes of Sweden and as a research assistant at HU Berlin , Charité Berlin , and Zuse Institute Berlin . His research interests span distributed systems , programming languages , cryptography , and serverless computing . He leads the Portals project, focusing on stateful serverless systems and secure computation. His recent work includes studies on Byzantine Fault Tolerant (BFT) consensus, verifiable BFT, and the application of blockchain in tamper-proof protocols. Jonas’s publications (2020–2024) address topics such as stateful serverless computing , dataflow systems , and secure multi-party computation (MPC) . He also organizes a Zoom MPC Study Group , documenting discussions on protocols like BGW, GMW Compiler, and Beaver Triples. Contact him via jspenger@kth.se or explore his work on GitHub and LinkedIn .

• Volcanology
• Remote Sensing
• Atmospheric Physics
• +5 more

Santiago Arellano is a Researcher at Chalmers University of Technology within the Geoscience and Remote Sensing unit. His work focuses on the physics of volcanic and atmospheric processes via spectroscopic remote sensing , including instrument development, fieldwork, and data analysis for volcanic gas emissions (SO2, CO2, mercury) to understand geophysical and environmental risks. Research Themes: Volcanic gas emissions and atmospheric interactions Remote sensing techniques (DOAS, FTIR, UAVs) Environmental risk assessment Global volcanic monitoring networks (NOVAC) Recent Publications (2025-2016): Volcanic plume dynamics and atmospheric scattering SO2 emission trends and dispersion modeling Mercury depletion in plumes Drone-based gas plume measurements Multi-sensor integration for activity analysis Global volcanic volatile budgets Projects: Global Volcanic and Atmospheric Observation Programme NOVAC (Network for Observation of Volcanic and Atmospheric Change) ECMWF CAMS2_61 Emissions DECADE initiative for volcanic outgassing