Dr Leonardo Bottolo

I am a Reader in Statistics for Biomedicine at the University of Cambridge and a former Alan Turing Institute fellow. I received a PhD in Methodological Statistics from the University of Trento, Italy. Before joining the University of Cambridge, I was appointed Senior Lecturer in Statistics in the Department of Mathematics, Imperial College. I also worked as a postdoc in the Mathematical Genetics group at the University of Oxford and the Institute of Mathematical Sciences, Imperial College. 

My goal is to develop scalable Bayesian models and design their efficient software implementations to tackle the problem of statistical and machine learning in large, diverse and complex data sets that are standard in modern molecular biology, for example, single-cell RNA-sequencing – scRNA-seq – (https://doi.org/10.1038/s41588-024-02050-9) and single-cell Assay for Transposase-Accessible Chromatin with high-throughput sequencing – ATAC-seq – (https://doi.org/10.1038/s41467-025-55900-3), and biomedicine, for instance, data collected in patients with mental illness (https://doi.org/10.1016/j.ajhg.2025.03.005), multilocus imprinting disturbances (https://doi.org/10.1073/pnas.2505884122) and mitochondrial and cardiovascular diseases (https://doi.org/10.1016/j.ajhg.2023.06.005). 

I am a member of the editorial board of Genome Biology (https://genomebiology.biomedcentral.com/). 

Project/study information 

Currently, I’m interested in the Variational Bayes implementation of: 

• Linear and non-linear graph learning models. 
• Overlapping clustering of instrumental variables for causal inference models. 
• Sparse biclustering factor models. 

I’m applying these methods to scRAN-seq, ATAC-seq and CHiC data in healthy individuals and patients with Parkinson's disease, Alzheimer's disease and Lewy body dementia.

Recruitment of PhD / Post doctoral students 

Enquiries and applications from potential PhD students are welcome. 

Potential projects include: 

• Linear and non-linear graph learning models for mixed data, multi-study bi-clustering factor models and multiview clustering of instrumental variables and exposures/outcomes for causal inference models. 
• A bachelor’s degree in mathematics, statistics, computational biology, or computer science (or a closely aligned discipline), or an equivalent level of professional qualifications and experience, is essential. Applicants should also have a good background in computational statistics and be able to implement algorithms in high- (R, Python) or low-level language (C, C++).