Dr Anelia Kurteva is a senior postdoctoral researcher at Department of Informatics, King’s College London. Previously, Anelia was a postdoc at TU Delft, in the Netherlands. Anelia’s research lies at the intersection of the Semantic Web, Privacy and Human-Computer Interaction domains and explores how ontologies and knowledge graphs can be utilised to support FAIR, responsible, privacy-preserving and legally (e.g. GDPR, AI Act) compliant data sharing as a backbone for trustworthy AI.

Anelia completed her PhD at the Semantic Technology Institute (STI) Innsbruck, Department of Computer Science, University of Innsbruck, Austria. Her doctoral research focused on supporting machines and humans in making sense of informed consent, for sensor data sharing in smart cities, with knowledge graphs. In collaboration with colleagues, Anelia’s research on automated GDPR compliance verification based on semantically modelled informed consent was recognised by the OECD Policy Division and the German Federal Ministry of Labour and Social Affairs as a significant contribution to the field of Accountable AI.

In 2023, Anelia joined the European Data Protection Board’s pool of experts in new technologies, privacy and data protection and in 2024 was awarded a postdoc fellowship in Safety and Security in AI by the German Academic Exchange Service (DAAD). Prior to her PhD, Anelia received an MSc in Advanced Computer Science from Cardiff University, The UK, where she also completed her BSc in Computer Science and Visual Computing.

Projects that Anelia has been involved in: smashHit H2020, CampaNeo FFG, RePlanIT, CIRPASS-2, FAIR-PV, MLCommons, Trustworthy Knowledge Graphs and Wikidata Gaps.

Research interests

• Knowledge graphs
• Ontologies
• Privacy and data protection
• FAIR data
• Semantic-based mechanisms for implementing legal compliance (e.g. with GDPR, AI Act)
• Decentralisation
• Human-computer interaction

Teaching

• Co-supervising PhD students working on LLMs, knowledge graphs and HCI.
• Co-supervising BSc final year projects on the making data more discoverable for ML.

More information

• Personal website