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

• Joseph Feest, PhD Candidate (co-advised with Paul Kirk);
• Yiran Li, PhD Candidate (co-advised with John Whittaker and Sylvia Richardson);
• Xinzhi Zhang, PhD Candidate (co-advised with Chris Wallace).

Former team members (all current MRC BSU visiting researchers):

• Camilla Lingjærde, PhD Candidate & Research Associate (Oct 2019-Apr 2024), co-advised with Sylvia Richardson, now Research Fellow at the University of Oslo;
• Daniel Temko, Research Associate (Oct 2022-Dec 2024), now Research Associate at the Early Cancer Institute of Cambridge University;
• Joseph Feest, Intern & Research Assistant (Jul 2023-Dec 2024);
• Marion Kerioui, Research Associate (Jan 2024-Oct 2025), now INSERM Research Scientist in Paris;
• Tudor Benea, Intern (Apr 2024-Aug 2024), Lecturer at the University of Sibiu;
• Xiaoyue Xi, Research Associate (Apr 2022-March 2025), now Senior Research Associate at the Big Data Institute of Oxford University.

I also regularly supervise Bachelor and Master theses of students from Cambridge University and EPFL (Lausanne). I am also a co-organiser of the MRC BSU Internship Programme; applications for the 2026 summer internships are now open until April 15, get in touch if interested!

The Unit also provides a dynamic environment to begin a career in biostatistics, with a PhD Programme offering multiple positions annually.

Aug 2026: Co-launched the MRC BSU AI Discussion Group, a forum for peer learning and discussion around statistical and AI methods in biomedical research.

July 13, 2026: New preprint on a Partition FPCA model for time-course gene expression data – lead author Marion Kerioui, joint with Daniel Temko and Shahin Tavakoli.

July 1, 2026: I look forward to speaking at the Statistical and AI methods for multi-modal multi-scale modeling of biological systems Conference in Ascona about latent trajectory modelling for longitudinal molecular data – joint work with Salima Jaoua and Daniel Temko.

May 19, 2026: I look forward to presenting our work at the IBS-CEN2026 Conference in Warsaw, on Bayesian network inference with node-level information – joint work with Xiaoyue Xi.

Mar 21, 2026: New preprint on a Bayesian latent factor modelling framework to uncover coordinated biological dynamics from high-dimensional longitudinal data – joint with Salima Jaoua and Daniel Temko.

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. We have a particular interest in longitudinal and multimodal biomedical data, including patient trajectories, molecular dynamics and multimodal integration across omics, clinical, wearable and patient-reported data.

Our methodological work spans Bayesian hierarchical modelling, sparse regression, latent representation models, network models and functional data analysis. We develop scalable statistical and machine learning approaches to uncover latent and shared structure across biological systems, characterise within- and between-individual heterogeneity across biological contexts, and quantify uncertainty in the resulting inferences. We are particularly interested in addressing the tension between flexible joint estimation and practical feasibility at the scale of current biomedical studies, with a focus on accuracy, robustness and computational tractability. Common threads include (i) learning and leveraging shared biological structures across modalities, scales and contexts (molecular entities, tissues, cell types or disease subtypes), and (ii) developing approximate inference procedures, such as expectation-maximisation (EM) and model-specific variational schemes, for high-dimensional parameter spaces.

Our methods are tailored to specific scientific questions, yet remain adaptable to a broader range of applications. We support their use through accompanying statistical software and implementations (see Software).

I am strongly committed to open science and reproducible research. I contribute to these efforts through the BSU Open Science Committee, which promotes transparency, collaboration and the sharing of tools, data and methods, both within the Unit and across the wider scientific community.

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

• Oct 2025–present: Assistant Research Professor at the MRC Biostatistics Unit, University of Cambridge (United Kingdom);
  
• Fall 2025: Lecturer (‘chargée de cours externe’) at EPFL, Lausanne, Switzerland;
• 2023–Sep 2025: Senior Research Fellow at the MRC Biostatistics Unit, University of Cambridge (United Kingdom);
  
• 2021–2023: Research Fellow at the MRC Biostatistics Unit;
• 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: 5-year fellowship for the project “An atlas of hotspots to disentangle the molecular networks underlying complex diseases” (1M EUR);
• Recipient of the 2019 EPFL Doctoral Program Thesis Distinction and of the 2019 EPFL Mathematics Doctoral Thesis Award;
• 2019 Congressi Stefano Franscini (CSF) Award for the best oral presentation at the Ascona Workshop: “Statistical Challenges in Medical Data Science”, Switzerland.