PhD Students

• Urska Matjasec: Interpretable methodology with high performance for tabular data.
• Mateo Espinosa Zarlenga: Concept based learning for explainable AI.
• Konstantin Hemker: Multi-Modal and Explainable Machine Learning on Biomedical Data.
• Julia Komorowska: Dynamics-informed protein generative modelling. (First supevisor with Pietro Lio)
• Xiangjian Jiang: Interpretable machine learning models for low-sample-size regimes in healthcare.
• Oscar Hill: Concept Discovery for Explainable AI.
• Yael Konforti: Explainable AI and bias.
• Marco Dos Santos: LLMs for mathematical reasoning.
• Haoyan Luo: Explainability of large language models.

Past PhD Students

• Albert Qiaochu Jiang (2024): Language models for verifiable mathematical automation: Interaction, integration, and autoformalization.
• Andrei Margeloiu (2025): Breaking the curse of dimensionality in low-data tasks.
• Dimitrios Deslis (2025): Developing an Interactive Computer Support System to Facilitate Teacher Learning of Proof-related Instruction in Mathematics. (First supervisor with Andreas Stylianides in Education Faculty)
• Mei Yang (2024): Designing a Computer-assisted System For Prospective Mathematical Teachers’ Training of Proof-Related Instruction . (First supervisor with Andreas Stylianides in Education Faculty)
• Dmitry Kazhdan (2024): Enhancing Interpretability: The Role of Concept-based Explanations Across Data Types. (First supervisor with Pietro Lio)
• Paul Scherer (2023): Distributional and relational inductive biases for graph representation learning in biomedicine. (First supervisor with Pietro Lio)
• Agnieszka Slowik (2023): Out-of-distribution generalisation in machine learning. (First supervisor with Sean Holden)
• Aaron Stockdill (2021): Automating representation change across domains for reasoning.
• Edward Ayers (2021): A Tool for Producing Verified, Explainable Proofs. (First supervisor with Tim Gowers in Maths Department)
• Botty Dimanov (2021): Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations.
• Duo Wang (2021): Neural Diagrammatic Reasoning. (First supervisor with Pietro Lio)
• Advait Sarkar (2017): Interactive Analytical Modelling. (First supervisor with Alan Blackwell and Martin Spott)
• Matej Urbas (2013): Mechanising Heterogeneous Reasoning in Theorem Provers.
• Daniel Winterstein (2004): Automating Diagrammatic Reasoning in Continuous Domains. (Second supervisor with Alan Bundy at University of Edinburgh)

Past Post Doctoral Researchers

• Dr Nikola Simidjievski. Mark Foundation for Cancer Research: AI for Integrative Cancer Medicine.
• Dr Daniel Raggi. EPSRC Project: Automating Representation Choice for AI Tools.
• Dr Lianlong Wu. EPSRC Project: Automating Representation Choice for AI Tools.
• Dr Zohreh Shams. Mark Foundation for Cancer Research: AI for Integrative Cancer Medicine. Leverhulme Trust Project: ARD: Accessible Reasoning with Diagrams.
  Now a visiting fellow with my group.
• Dr Gem Stapleton. EPSRC Project: Automating Representation Choice for AI Tools.
  Now a visiting fellow with my group.
• Dr Helena Andres-Terre. Mark Foundation for Cancer Research: AI for Integrative Cancer Medicine.