Assistant Professor
Statistical Science
University College London
b dot lehmann at ucl dot ac dot uk
@BrieucLehmann
brieuc-lehmann
Brieuc Lehmann
I’m a Lecturer (Assistant Professor) in Statistical Science at UCL, having joined the department in 2021. My primary research area is health data science, with a focus on statistical methods for health equity. I’m particularly interested in developing methods to assess the representativeness of biomedical datasets, with the goal of informing experimental design to then enhance that representativeness.
I’m also a co-founder of Data Science for Health Equity (DSxHE), an interdisciplinary and cross-sector community-of-practice bringing together academics, healthcare professionals, policymakers, and others working at the intersection of data science and health equity.
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 under the supervision of Dr Simon White, Prof Rik Henson and Dr Linda Geerligs.
Health equity, Causal inference, Statistical genomics, Algorithmic fairness
Evan Dimitriou (PhD student, started January 2023)
Vanessa Rodriguez (PhD student, started January 2023)
Leandra Bräuninger (PhD student, started September 2023)
Ioanna Thoma (Postdoc, September 2023 - May 2025, moved to LSHTM)
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.
See my Google scholar page for a full and up-to-date list of publications.
Methodological opportunities in genomic data analysis to advance health equity (Nature Reviews Genetics, 2025)
B. Lehmann, L. Bräuninger, Y. Cho, F. Falck, S. Jayadeva, M. Katell, T. Nguyen, A. Perini, S. Tallman, M. Mackintosh, M. Silver, K. Kuchenbäcker, D. Leslie, N. Chatterjee, C.C Holmes
A Bayesian multilevel model for populations of networks using exponential-family random graphs (Statistics and Computing, 2024)
B. Lehmann, S.R. White
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