Recent activity and highlights

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 !

Apr 26, 2024: Our work will be highlighted at the International Society for Clinical Biostatistics (ISCB) Conference 2024 in Thessaloniki-Greece, with a talk on high-dimensional functional data analysis given by Marion Kerioui (programme).

Mar 25, 2024: The research of Salima Jaoua, formerly EPFL Master student, on Bayesian FPCA for longitudinal gene expression data, will be presented Daniel Temko (supervisor) at the European Mathematical Genetics Meeting 2024 in Vienna (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.

Feb 8, 2024: Salima Jaoua, EPFL student completing her Master’s thesis in our team, supervised by Daniel Temko, has successfully defended her thesis and graduated. Congratulations to her !

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.

Jan 11, 2024: Our work will be highlighted at the International Society for Bayesian Analysis (ISBA) World Meeting 2024 in Venice, with three of our abstracts accepted for talks and posters, presented by Xiaoyue Xi, Sylvia Richardson and myself (programme).

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.

Apr 6, 2023: Invited seminar on an “FPCA framework to characterise systemic recovery from SARS-CoV-2 infection”, at Inserm Bordeaux Population Health, Bordeaux University.

Mar 31, 2023: Invited seminar on “Fast Bayesian joint inference for uncovering disease mechanisms at scale”, at The Research Center for Statistics, GSEM, University of Geneva.

Feb 17, 2023: Luca Bracone, EPFL student completing his Master’s thesis in our team, supervised by Daniel Temko, has successfully defended her thesis and graduated. Congratulations to him !

Jan 30, 2023: New paper on the characterisation of immunometablic recovery from SARS-CoV-2 infection using FPCA and predictive modelling approaches published in Nature Immunology.

Nov 7, 2022: Course given as part of a Masterclass on variational inference, for the CoSInES and Bayes4Health programme – with Camilla Lingjærde.

Sept 29, 2022: Invited speaker at the 19th edition of the Armitage Workshop, Cambridge.

Sept 16, 2022: Article on our R package “atlasqtl” for multiple-response hierarchical regression published in the Software Corner of the International Biometric Society Bulletin (Volume 39, Issue 3).

Jun 23, 2022: New preprint introducing the Joint Graphical Horseshoe with scalable expectation conditional maximisation estimation – with Camilla Lingjærde, Benjamin Fairfax, Sylvia Richardson.

May 3, 2022: Invited seminar on “Large-scale variational inference for sparse hierarchical regression applied to genome-wide association problems” for the British Irish Region IBS Meeting on Variational Bayes Methods.

Dec 20, 2021: Chapter “Variable selection for hierarchically-related outcomes: models and algorithms” now published in the Handbook of Bayesian Variable Selection, ed. M. Tadesse & M. Vannucci, Chapman and Hall/CRC.