Data Science Seminars - The Institute of Statistical Mathematics, Japan and University of Bristol

Methodologies and applications of geometric data analysis

Seminar series 3 September - 8 October 2020

Modern large-scale datasets often exhibit rich geometric structure that is often overlooked in classical methods of analysis, and yet may provide the answer to a number of scientific questions. This series included theoretical and methodological developments in the area of geometric data analysis, and also practical applications in diverse fields, such as earth sciences, biology, and material science. 

If you missed this series or would like to re-visit slides and recordings, please browse the seminars below.

Seminar slides Recording

Dan Lawson: CLARITY - Comparing heterogeneous data using dissimiLARITY

Tjun Yee Hoh: Fibre analysis for super-resolution microscopy data

Daisuke Murakami: Compositionally-warped additive mixed modeling: application to COVID19 data in Japan

Ayaka Sakata: Active pooling design in group testing based on Bayesian posterior prediction

Seminar Two

Henry Reeve: Optimistic bounds for multi-output prediction

Seminar Three

Hideitsu Hino: Modal Principal Component Analysis

Seminar Four

Patrick Rubin-Delanchy: Manifold structure in graph embeddings

Song Liu: Estimating Density Models with Truncation Boundaries

Seminar Five

Ryo Yoshida: Machine Learning for Materials Discovery

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