I have the pleasure of working with talented researchers at the MRC Biostatistics Unit:

• Xiaoyue Xi, Research Associate;
• Daniel Temko, Research Associate;
• Camilla Lingjærde, Research Associate (co-advised with Sylvia Richardson);
• Marion Kerioui, Research Associate;
• Yiran Li, PhD Student (co-advised with John Whittaker).

I also regularly supervise Bachelor and Master theses of students from Cambridge University and EPFL (Lausanne), and I am a co-organiser of the MRC Biostatistics Unit Internship Programme.

Mar 1, 2024: Our collaborative work, led by Dr Aimee Hanson, on how COVID-19 causes iron dysregulation early on in infection has now been published in Nature Immunology, with a preview here.

Jan 20, 2024: Our paper on the Joint Graphical Horseshoe with scalable expectation conditional maximisation estimation has now been accepted in the Annals of Applied Statistics – with Camilla Lingjærde, Benjamin Fairfax, Sylvia Richardson.

Nov 10, 2023: Invited seminar on “Bayesian joint functional principal component analysis (FPCA) for complex sampling designs”, at Imperial College London.

Nov 9, 2023: New preprint on Bayesian variational message passing for multivariate FPCA – with Tui Nolan and Sylvia Richardson.

Sept 6, 2023: New preprint on encoding auxiliary information to guide Bayesian network inference – with Xiaoyue Xi.

The landscape of biomedical research is changing rapidly, driven by technological advances for quantifying clinical and molecular data at scale. This evolution not only offers a more granular view of disease mechanisms, but also parallels a growing acknowledgment that pathogenic responses are tightly coordinated at the organismal level. Consequently, there is a need for modelling approaches capable of providing a holistic understanding of complex interplays across biological systems.

My team aims to provide principled statistical methodology for tackling this challenge, guided by collaborations with clinicians and researchers in areas such as immunology, infectiology and cancer. Specifically, we develop Bayesian hierarchical modelling approaches for sparse regression, graphical modelling, latent factor modelling and functional data analysis to leverage complicated dependences within and between heterogeneous biological data sources (e.g., genetics, genomics, proteomics, metabolomics), while conveying uncertainty coherently.

We are particularly interested in addressing the tension between flexible joint estimation and practical feasibility for analysis at the scale of current biomedical studies, through dedicated efforts to enhance accuracy, robustness and computational tractability. Common threads for our methods include (i) uncovering and leveraging shared biological structures across multiple contexts (molecular entities, tissues, cell types or disease subtypes), and (ii) using approximate inference procedures, such as expectation-maximisation (EM) and model-specific variational schemes, tailored to the exploration of multimodal parameter spaces.

Our methods are designed with specific contexts in mind, yet are adaptable for various scientific applications, which is facilitated by accompanying statistical implementations (see Software).

Our research receives generous support from the Lopez–Loreta Foundation.

• 2021– : Principal Investigator / Team Leader at the MRC Biostatistics Unit, University of Cambridge (United Kingdom);
• 2019–2021: Postdoctoral Research Associate at the MRC Biostatistics Unit, in Sylvia Richardson’s group;
• 2019: Associate Specialist Statistician, Clinical & Omics Data Science Group, Nestlé Research, Lausanne;
• 2014–2018: PhD candidate in Mathematics, spec. Statistics, Swiss Federal Institute of Technology Lausanne (EPFL) and Nestlé Research, co-advised by Anthony Davison & Jörg Hager;
• 2014: Data Analyst, Bioinformatics Core Facility, Swiss Institute of Bioinformatics (SIB);
• 2013: Master’s thesis, CERMICS, Research Center in Applied Mathematics, Numerical Analysis and Scientific Computing, Ecole des Ponts ParisTech, Paris;
• 2008–2013: Bachelor in Mathematics and Master in Applied Mathematics/Mathematical Engineering, EPFL.

• Co-recipient of the 2021 Young Biometrician Award of the British & Irish Region of the International Biometric Society (BIR-IBS) and the Fisher Memorial Trust for the paper “A global-local approach for detecting hotspots in multiple-response regression” (The Annals of Applied Statistics, 2020);
• Best Postdoc Contributed Paper Award, sponsored by the US National Science Foundation Foundation (NSF), for the presentation “Large-scale inference for detecting QTL hotspots in hierarchically-related sparse regression models” at the 2021 edition of the International Society for Bayesian Analysis (ISBA) Conference;
• Recipient of the 2020 Lopez-Loreta Prize: 1M EUR for the 5-year research project “An atlas of hotspots to disentangle the molecular networks underlying complex diseases”;
• Recipient of the 2019 EPFL Mathematics Doctoral Thesis Award for the best PhD thesis;
• 2019 Congressi Stefano Franscini (CSF) Award for the best oral presentation at the Ascona Workshop: “Statistical Challenges in Medical Data Science”, Switzerland.