Jannice Käll is a Senior Lecturer and Associate Professor in Sociology of Law at Lund University. Her work focuses on how digital technologies reshape legal frameworks, particularly through new materialist and posthumanist theories. She holds an LL.D. in Legal Philosophy (Gothenburg University, 2017), LL.M., and M.Sc. in Intellectual Capital Management (Gothenburg University, 2009). Research interests include law's interaction with digitalization, posthumanist legal theory, and intellectual property law. Key themes involve AI-driven governance, blockchain's impact on property rights, and data-driven regulatory mechanisms. Recent publications (2023–2025) explore algorithmic justice, blockchain ecosystems, and posthuman rights frameworks. She leads projects like 'Vulnerability in the Automated State' (2023–2028) and participates in biomaterials research through Lund's Pufendorf Institute. Education: LL.D. (2017), LL.M. (2009), M.Sc. (2009) Grants/Projects: Active in EU-funded legal tech initiatives Labs/Teams: Collaborates with multidisciplinary groups on AI ethics and digital law
Tobias Pulls is a Senior Lecturer (Docent ≈ Associate Professor) in Computer Science at Karlstad University, Sweden. He is actively involved in the Tor Project as a core member and serves on the board of DFRI (Digital Rights and Freedom Institute). His research focuses on the intersection of computer security, privacy, and human rights, with a strong emphasis on traffic analysis, anonymity, and cybersecurity. Research Interests: His work spans privacy-preserving technologies, anonymity in Tor networks, website fingerprinting defenses, and ethical implications of cybersecurity. He has collaborated on projects funded by EU initiatives (e.g., H2020, FP6/FP7), Google, Mullvad VPN, and the Swedish Internet Foundation, among others. Key Contributions: His research includes advancements in Tor’s DNS privacy, traffic splitting defenses against website fingerprinting, and cryptographic tools like ScrambleSuit for censorship circumvention. He is also an advocate for privacy-by-design principles, co-developing educational tools like a MOOC on GDPR compliance. Grants & Collaborations: Funded by diverse entities including the Swedish Knowledge Foundation and NLnet Foundation, Tobias’s work bridges academic research with practical applications. His projects often address real-world challenges in maintaining privacy and democratic rights in digital spaces. Labs & Teams: Collaborates closely with the Tor Project, DFRI, and interdisciplinary teams at Karlstad University’s Department of Mathematics and Computer Science, fostering innovation in secure systems and privacy-enhancing technologies.
Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.
Hongyu Jin is a Researcher at the KTH Royal Institute of Technology in the Networked Systems Security (NSS) Group , led by Prof. Panos Papadimitratos. His work focuses on security and privacy in wireless and mobile networks , with emphasis on vehicular communication systems and decentralized location-based services. Primary Affiliation : Division of Software and Computer Systems, KTH Royal Institute of Technology Research Focus : Vehicular network security, DoS-resilience, privacy protection, and decentralized authentication mechanisms Research Trends : Hongyu Jin's publications highlight advancements in secure vehicular communication , including mitigation of Distributed Denial-of-Service (DoS) attacks , beacon verification , and Wi-Fi localization privacy . His work integrates cooperative security models and Bloom Filter-based certificate validation to enhance scalability and robustness in automotive networks. Additional Contributions : He has developed runtime MAC address re-randomization for Wi-Fi privacy, decentralized LBS frameworks , and scalable identity management infrastructures for vehicular systems.
Zahra Kharaghani is a Postdoctoral Fellow at the Department of Computing Science, Umeå University, specializing in incremental federated machine learning . Currently based in MIT House, Room MIT.E.251, Umeå, Sweden, she contributes to advancements in distributed learning systems. Research Focus Her work bridges: Federated learning architectures Incremental model updating Biomedical data analysis Privacy-preserving AI Decentralized healthcare systems Recent publications highlight cross-institutional collaboration and handling non-IID data distributions.
Lihui Wang is a Professor and Chair of Sustainable Manufacturing at KTH Royal Institute of Technology in Sweden. He leads the Centre of Excellence in Production Research (XPRES) and holds leadership roles in organizations like CIRP and NAMRI/SME. His research focuses on human-robot collaboration, digital twin technology, and Industry 5.0 . He has authored over 750 publications and received numerous awards, including the SME Gold Medal (2024). Education: PhD and MSc from Kobe University (Japan), 1993 and 1990 BSc from China, 1982 Research Interests: Prof. Wang’s work spans real-time monitoring, brain robotics, sustainable production systems , and cyber-physical systems . His recent efforts emphasize AI-driven smart manufacturing , including predictive maintenance and human-centric collaboration frameworks. His labs explore applications like mixed-reality assembly guidance and adaptive robotic systems . Awards & Recognition: 2024 SME Gold Medal 2020 '20 Most Influential Professors in Smart Manufacturing' Eight NRC Institute Awards (2002–2005) Fellowships: CAE, CIRP, ASME, SME, AET Grants & Labs: Leads projects like SMART (Predictive Maintenance for Pharma) and co-leads the Swedish Production Academy . His labs include the Human-Robot Collaboration Group and AI for Manufacturing Initiative .
Eric-Oluf Svee is an Associate Professor at Stockholm University 's Department of Computer and Systems Sciences . His research bridges Values Sensitive Design (VSD) , Consumer Values , and Information Systems (IS) development through Natural Language Processing (NLP) , Enterprise Architecture , and Requirements Engineering . He co-founded the NLP Research Group and the PRECIS group, focusing on privacy, explainability, and domain adaptation. Education: M.Sc. in Interactive Systems Engineering (2007, Royal Institute of Technology/KTH ), B.A. in Political Philosophy, B.Mus. in Music Performance (Saxophone). Professional Background: Prior roles at Adobe Systems , AT&T Wireless , Swedish Defence Research Agency , and RISE (formerly SICS) . His work integrates Phenomenological Theories and Technological Mediation into AI alignment with human values, emphasizing Privacy , Explainability , and Domain Adaptation . He pioneered the Consumer Preference-aware Meta-Model (CPMM) to link consumer values to system requirements, with applications in Online Education , Healthcare , and Personalized Services . The Consider8 Project (patent filing) focused on privacy-sensitive mobile personalization. Eric-Oluf is active in Teaching and Industry Collaboration , with a focus on Time Geography , Data Visualization , and Business-IT Alignment . He has no listed scientific awards but contributes extensively to Conference Proceedings and Theses .
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.
Dr. Mairton Barros is an Assistant Professor in the Division of Computer Systems at Uppsala University, Sweden. Previously, he held a Marie Skłodowska-Curie Postdoctoral Fellowship at Princeton University and KTH Royal Institute of Technology. His research focuses on federated learning, wireless communications, and optimization algorithms. He received his PhD from KTH in 2019, with earlier degrees from the Federal University of Ceará, Brazil. Education: PhD in Electrical Engineering, KTH Royal Institute of Technology (2019) MSc in Telecommunications Engineering, Federal University of Ceará (2014) BSc in Telecommunications Engineering, Federal University of Ceará (2012) Affiliations: Postdoctoral Researcher, KTH (2019-2022) Visiting Researcher, Princeton University (2022) Research Interests: His work bridges machine learning and wireless communications, emphasizing federated learning, full-duplex systems, and mmWave technologies. Key contributions include optimizing communication efficiency in distributed learning frameworks and advancing smart antenna techniques for full-duplex networks. Professional Activities: He has organized conferences (e.g., IEEE SECON 2022, IEEE GLOBECOM workshops) and delivered tutorials on wireless machine learning at major IEEE events. Recent grants include funding from Ericsson Research (2022). Lab/Team: Leads research initiatives in distributed intelligence and edge computing within Uppsala’s computer systems division.
Carlo Fischione is a Full Professor at KTH Royal Institute of Technology in the School of Electrical Engineering and Computer Science, Division of Network and Systems Engineering, Stockholm, Sweden. He is a Fellow of IEEE, KTH Digital Futures, and the Italian Academy DASP, and a Distinguished Lecturer of the IEEE Communications Society. He holds a PhD and Laurea (Summa cum Laude) in Engineering from the University of L’Aquila, Italy, and has held research positions at MIT, Harvard, and UC Berkeley. PhD in Electrical and Information Engineering, University of L’Aquila (2005) Laurea in Electronic Engineering, Summa cum Laude, University of L’Aquila (2001) His research focuses on applied optimization, wireless Internet of Things, and machine learning , with particular emphasis on federated learning, over-the-air computation, and spectrum sharing in 5G/6G networks. He leads a vibrant research group and has supervised numerous PhD and postdoctoral researchers, many of whom now hold faculty or senior research positions globally. His recent publications reflect a strong trend toward machine learning in distributed and resource-constrained networked environments , especially focusing on communication efficiency, privacy, and scalability. Key themes include federated learning over fading channels, over-the-air computation, and AI-aided wireless channel prediction. IEEE Fellow IEEE Distinguished Lecturer, Communications Society IEEE Communication Society S. O. Rice Award (2018) Best Paper Award, IEEE Transactions on Industrial Informatics (2007) Starting Grant, Swedish Research Council (2008) Prof. Fischione has advised numerous students who have gone on to prominent academic and industry roles. He has secured significant research funding from the Swedish Research Council, SSF, KAW Foundation, Vinnova, and EU Horizon programs, leading projects such as MALEN, SAICOM, TAIRCOMP, and WIDCOMP. He is also the founding General Chair of IEEE ICMLCN and a co-founder of ELK.Audio, demonstrating strong industry and innovation engagement. He leads multiple research initiatives and labs focused on networked machine learning, including groups working on federated learning, wireless AI, and edge intelligence. His team actively contributes to advancing the theoretical and practical foundations of machine learning over networks.
Ahmad Ghazawneh is a Senior Lecturer at the School of Information Technology , Halmstad University. His research explores the intersection of digital innovation platforms, blockchain technology, and fintech, emphasizing transformative impacts on financial systems and digital economies. Email: ahmad.ghazawneh@hh.se Research Focus: Dr. Ghazawneh investigates blockchain-based financial ecosystems, knowledge graph integration in healthcare, and platform dynamics across multiple domains including mobility systems and social media affordances. Scientific Trends: Recent publications demonstrate expertise in federated health data systems, token-based blockchain ecosystems, conversational AI for healthcare, and sustainable mobility platforms. His work bridges theoretical platform economics with practical implementations.
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.
Maksymilian Michal Kuzmicz is a Research Fellow at the Department of Law, Stockholm University , and a PhD candidate within the visuAAL Innovative Training Network under the Marie Skłodowska-Curie Fellowship . He joined the RESHUFFLE project at KU Leuven's Institute for European Law in 2020, focusing on legal frameworks for AI-driven Active and Assisted Living (AAL) technologies. BA and MA in Law from KU Lublin (summa cum laude) , with exchange studies at KU Leuven Current PhD research on "AI-driven video-based AAL technologies and balancing of interests" , proposing legal tools for conflict management in European law contexts His research integrates European law , privacy law , and data protection with applications in AI-driven AAL systems . Key projects include RESHUFFLE (ERC-funded) and visuAAL , focusing on ethical, legal, and social implications of surveillance technologies. Recent publications address stakeholder classification in AAL, criminal liability for intimate images under European law, and information obligations under GDPR and the AI Act. He explores methodologies like proportionality and compromise in EU case law to develop conflict resolution frameworks in technology contexts. Scientific awards: Marie Skłodowska-Curie Fellowship Contributions to legal sciences: novel frameworks for balancing interests in AI , stakeholder theories in AAL technologies
Sara Saeidian is a researcher at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), specifically within the Information Science and Engineering department under the Intelligent systems division. She completed her doctoral dissertation titled "Pointwise Maximal Leakage: Robust, Flexible and Explainable Privacy" in 2024, establishing herself as a promising researcher in information-theoretic privacy. Dr. Saeidian's research program centers on developing a comprehensive framework for privacy-preserving systems with three essential criteria: explainability (operationally meaningful privacy guarantees), robustness (resilience against diverse adversaries), and flexibility (applicability across contexts and data types). Her primary contribution is the development and analysis of pointwise maximal leakage (PML) as a privacy measure that quantifies information leakage about a secret variable to a publicly available related variable. Her publication record from 2021-2025 demonstrates a cohesive research trajectory examining PML's theoretical foundations, composition properties, and practical applications. She has established critical relationships between PML and existing privacy notions like differential privacy, while challenging misconceptions about the impossibility of meaningful inferential privacy guarantees. Her work spans theoretical investigations of optimal privacy mechanisms under leakage constraints to practical applications in privacy-preserving machine learning frameworks like PATE. Dr. Saeidian's research has been published in premier venues including IEEE Transactions on Information Theory, IEEE Transactions on Information Forensics and Security, and proceedings of the IEEE International Symposium on Information Theory, reflecting the significance and quality of her contributions to the field of data privacy.
Dr. José Mairton Barros da Silva Júnior is an Assistant Professor at the Department of Information Technology , Uppsala University , Sweden. His work integrates Wireless Communications and Machine Learning to advance future network technologies. Ph.D. in Electrical Engineering and Computer Science, KTH Royal Institute of Technology (2019) M.Sc. and B.Sc. in Teleinformatics Engineering, Federal University of Ceará (2014, 2012) His research bridges distributed optimization and machine learning for wireless systems, focusing on federated learning , vehicular communications , and full-duplex architectures . He investigates communication efficiency via novel modulation techniques and privacy-preserving frameworks. Recent publications address over-the-air computation , resource-constrained federated learning , and differential privacy in multi-base station systems. His work spans 6G network design , IoT applications , and wireless sensor networks . Scientific Awards : Exemplary Reviewer , IEEE Open Journal of the Communications Society (2021) He supervises PhD and Master's students including Metehan Karatas and Saeed Razavikia . He organizes IEEE conferences and develops open-source MATLAB toolboxes for mmWave beamforming.
