Teaching

My teaching in this course focuses on knowledge graphs, graph analytics, and learning over graph-structured data. I cover RDF and RDF syntaxes, SPARQL querying, RDF Schema and OWL-based inference, graph analytics methods, and machine learning tasks for knowledge graphs, including graph representations and embeddings such as TransE and DistMult.

My teaching in this course focuses on data privacy. I cover anonymisation techniques such as k-anonymity, l-diversity, and t-closeness; statistical data release and reconstruction attacks; pure and approximate differential privacy; and differentially-private machine learning methods such as DP-SGD, PATE, dp-GAN, and PATE-GAN.

My teaching in this course focuses on foundational database development, including SQL data definition and querying, relational schemas, entity-relationship modelling, keys and constraints, updates, referential integrity, and database normalisation.

My teaching in this course focused on knowledge graphs and Web intelligence, covering RDF-based graph data, SPARQL querying, graph analytics, and learning methods for knowledge graphs, including graph representations and embeddings.

My teaching in this course focused on Web data and Semantic Web technologies. I covered RDF for representing Web data, SPARQL for querying RDF graphs, vocabularies and OWL ontologies for modelling domain knowledge, and rule-based reasoning.

My teaching in this course focused on Semantic Web foundations and engineering methods, covering RDF, RDFS, OWL, SPARQL, linked data, reasoning, and practical application development.

My teaching in this continuing education programme focused on smart contracts. I covered conceptual and technical foundations of smart contracts, their role in blockchain-based systems, and practical aspects of designing and reasoning about decentralised applications.

My teaching in this PhD winter school focused on data privacy, introducing privacy risks in data analysis and methods for privacy-preserving data processing.

My teaching in this PhD summer school focused on smart contracts, covering their role in blockchain systems and their use in decentralised applications.

The course addressed the construction of robust knowledge graphs from diverse data sources, covering core knowledge graph concepts, data models and query languages, information extraction from text, entity matching, knowledge graph ingestion, and emerging LLM-based approaches for extracting structured data from text, with applications in the medical domain.

The course focused on dataflow systems as a programming paradigm and execution model for distributed data processing. It covered programming models and implementation aspects, including constructs for static and streaming data analysis, iterations, time-based computations, user-defined functions, and design trade-offs in modern dataflow systems, complemented by hands-on sessions.

The course provided a practical introduction to knowledge graphs and Semantic Web technologies, covering ontology-based knowledge graphs, W3C standards, inference and reasoning engines, triplestores, ontology and entity alignment, applications to data science, and the interaction between machine learning, ontologies, and knowledge graphs.