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

• Xiaoyue Xi, Research Associate;
• Daniel Temko, Research Associate;
• Marion Kerioui, Research Associate;
• Yiran Li, PhD Candidate (co-advised with John Whittaker and Sylvia Richardson);
• Joseph Feest, Research Assistant.

Former team members:

• Camilla Lingjærde, PhD Candidate & Research Associate until May 2024, co-advised with Sylvia Richardson, now Research Fellow at the University of Oslo.

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.

Nov 5, 2024: Our manuscript on a variational Bayesian approach for multivariate functional principal component analysis has now been accepted for publication the Journal of Computational Statistics & Data Analysis ! Joint with Tui Nolan and Sylvia Richardson.

Oct 15, 2024: The research of the team will be highlighted at the London CFE-CMStatistics Conference in December, with talks by Xiaoyue Xi and myself (programme).

Aug 7, 2024: Our Joint Graphical Horseshoe approach has just been published in the Annals of Applied Statistics! Joint with Camilla Lingjaerde, Benjamin Fairfax and Sylvia Richardson.

Jun 25, 2024: Our work on leveraging node-level information for Bayesian network inference has now been published in Biostatistics ! Lead Author: Xiaoyue Xi.

May 31, 2024: Joseph Feest, third year student at Cambridge University (and former summer intern in the team) has successfully completed a year-long project part of his degree, supervised by Xiaoyue Xi and Camilla Lingjaede. Congratulations to him !

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.