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About Leeds Annual Statistical Research (LASR) Workshops
Leeds Annual Statistical Research (LASR) Workshops started about 50 years ago by Professor Kanti Mardia as a biennial event to foster Interdisciplinary Research in Statistics in an emerging area of science. LASR today is a well-established international conference and has been instrumental in bringing together different scientific communities over the years. The workshop format is a mixture of invited and contributed talks. It has a tradition of being informal, and relaxed and encouraging interaction and discussion between the participants.
This year, the workshop focus is on three key areas: (1) deep learning approaches to protein structure prediction, (2) statistical learning for medical imaging analysis, and (3) developments in graphical models and Networks Stuctures.
The scientific program features three keynote lectures by distinguished researchers alongside contributed talks spanning all career stages, from early-career researchers to established mid-career scientists.
Celebrate our milestones: 50th & 25th Anniversaries!
This year’s event marks both the 50th anniversary of LASR, reflecting its continued evolution as a biennial international meeting, and the 25th Anniversary of its focus on Protein Structure Prediction.
These milestones celebrate not only the longevity of the series, but also its enduring mission, started by Professor Mardia, to break disciplinary boundaries and promote impactful collaboration between statistics and real-world scientific challenges.
Keynote Speakers
Prof Jane Richardson
They created key tools such as MolProbity and introduced foundational concepts like protein ribbon diagrams and common fold motifs.
Jane’s contributions have earned major scientific honors, and together they continue to address challenges in cryo‑EM, SARS‑CoV‑2 structures, and limits of AI predictions like AlphaFold.
Dr Emma Robinson
Her work underpins key HCP neuroimaging paradigms and optimises pipelines for major open datasets such as HCP and UK Biobank.
She now focuses on geometric deep learning for cortical and cardiac surfaces and on building learning‑based models of neurological processes (“digital twins”).
Dr Daniela De Canditiis
Her work focuses on mathematical modelling and statistical inference for complex low‑ and high‑dimensional data, including methods in nonparametric regression, graphical models, survival analysis, and clustering.
She has contributed to many interdisciplinary projects across fields such as atmospheric physics, genetics, medicine, neuroscience, cultural heritage, and environmental sciences.