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I am an Associate Professor in Statistics in the Department of Mathematics at Imperial College London.
My research focuses on statistical and machine learning methodology motivated by complex real-world problems, applied to areas such as cyber-security and biology. I work primarily on probabilistic models for dynamic and multiplex networks, temporal point processes and time series, and textual data. More recently, I have also become interested in modern mathematical and statistical challenges arising in the analysis and understanding of large language models and related generative AI systems.
I also have a personal webpage on the Imperial College website.
StatML-Meta studentship – We are pleased to announce a fully funded PhD studentship in collaboration with Meta, as part of the StatML Centre for Doctoral Training at Imperial College London. This exciting project sits at the intersection of statistics, machine learning, AI and cybersecurity, and will develop new statistically principled methods for evaluating and improving modern AI systems. The research will explore topics including:
- Prompt injection detection and statistical significance in LLM evaluations;
- Role mining in access graphs with node attributes for large distributed systems.
I am unable to provide opportunities to discuss individual applications at this stage.
STING27 – I am organising STING, a one-day workshop on Statistical Inference on Networks and Graphs, which will take place on 14 January 2027 at Imperial College London, in the South Kensington campus.
PhD enquiries – I am always interested in hearing from potential PhD students. If you think that your interests and background are a good match with my research, please contact me.