Brieuc Lehmann

Assistant Professor
Statistical Science
University College London

b dot lehmann at ucl dot ac dot uk
@BrieucLehmann
brieuc-lehmann
Brieuc Lehmann

Bio

I’m a Lecturer (Assistant Professor) in Statistical Science at UCL. My primary research area is health data science, with a particular focus on statistical methods for health equity. I’m also a co-founder of the Data Science for Health Equity community.

From 2019 to 2021, I was a postdoctoral research associate in statistical machine learning at the Big Data Institute and the Department of Statistics at the University of Oxford, working with Prof Chris Holmes and Prof Gil McVean. I also held a Junior Research Fellowship in Statistics at Jesus College, Oxford from September 2019, and was part of the Turing-RSS Health Data Lab from November 2020. Before this, I did my PhD at the MRC Biostatistics Unit, University of Cambridge under the supervision of Dr Simon White, Prof Rik Henson and Dr Linda Geerligs.

Research interests

Health equity, Bayesian computation, Causal inference, Statistical genomics, Statistical network analysis

Group members

Evan Dimitriou (PhD student, started January 2023)
Vanessa Rodriguez (PhD student, started January 2023)
Leandra Bräuninger (PhD student, started September 2023)
Ioanna Thoma (Postdoc, started September 2023)

Prospective students

I am currently taking on PhD students and am particularly interested in hearing from potential applicants with a strong background in {statistics, mathematics, machine learning, programming} and/or an interest in health applications. If this sounds like you, please contact me via email with your CV and a brief description of your research interests.

Selected publications

See my Google scholar page for a full and up-to-date list of publications.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits (Nature Communications, 2023)
B. Lehmann, M. Mackintosh, G. McVean, C.C. Holmes

A predictive approach to Bayesian nonparametric survival analysis (AISTATS, 2022)
E. Fong, B. Lehmann
Code

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework (Nature Microbiology, 2021)
G. Nicholson*, B. Lehmann*, T. Padellini, K.B. Pouwels, R. Jersakova, J. Lomax, R.E. King, A. Mallon, P.J. Diggle, S. Richardson, M. Blangiardo, C.C. Holmes (*Equal contribution)
Code
Blog post

Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models (NeuroImage, 2021)
B. Lehmann, R.N. Henson, L. Geerligs, Cam-CAN, S.R. White
Code

Teaching (2023/24)