I am a Senior Research Fellow funded by the Lopez-Loreta Foundation (as of June 2021) and joined the MRC Biostatistics Unit in August 2019. I am leading research on Bayesian modelling for high-dimensional and longitudinal data, which involves designing scalable hierarchical approaches for sparse regression, functional data analysis and graphical modelling. This research is applied to biomedical studies with diverse clinical and biological data – including genome-wide association, genomic, proteomic and metabolomic studies – to help advance our understanding of the molecular processes driving disease risk and progression in humans.

ORCID: 0000-0002-7113-2540

• H. Ruffieux, A. L. Hanson, S. Lodge, N. G. Lawler, L. Whiley, N. Gray, N., ... & C. Hess. (2023) A patient-centric modeling framework captures recovery from SARS-CoV-2 infection. Nature Immunology, 24:349-358.
• H. Ruffieux, B. Fairfax, I. Nassiri, E. Vigorito, C. Wallace, S. Richardson, L. Bottolo. (2021) EPISPOT : An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies, The American Journal of Human Genetics, 108:1-18.
• H. Ruffieux, A. C. Davison, J. Hager, J. Inshaw, B. Fairfax, S. Richardson, and L. Bottolo. (2020) A global-local approach for detecting hotspots in multiple response regression, The Annals of Applied Statistics, 14:905-928.
• H. Ruffieux, J. Carayol, R. Popescu, M. E. Harper, R. Dent, W. H. M. Saris, A. Astrup, A. C. Davison, J. Hager, and A. Valsesia. (2020) A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma, PLOS Computational Biology, 16:e1007882.
• H. Ruffieux, A. C. Davison, J. Hager, and I. Irincheeva. (2017) Efficient inference for genetic association studies with multiple outcomes, Biostatistics 18: 618–636

• ATLASQTL (fast global-local hotspot QTL detection)
• ECHOSEQ (synthetic-data generator to emulate genotyping and molecular datasets)
• EPISPOT (annotation-driven approach for large-scale joint regression with multiple responses)
• LOCUS (large-scale variational inference for variable selection in sparse multiple-response regression)