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

Methodologies and applications of geometric data analysis

A new weekly online seminar series

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 will include 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. 

Programme

Date and time Speakers Title

 

Dan Lawson &

Tjun Yee Hoh

(Bristol)

CLARITY - Comparing heterogeneous data using dissimiLARITY  

Fibre analysis for super-resolution microscopy data 

 

 

Daisuke Murakami &

Ayaka Sakata

(ISM)

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

Active pooling design in group testing based on Bayesian posterior prediction

Thursday 17 Sept

09.00-10.00 (BST)

Henry Reeve

(Bristol)

Optimistic bounds for multi-output prediction

 

Thursday 24 Sept

09.00-10.00 (BST)

Hideitsu Hino &

Daichi Mochihashi

(ISM)

Modal Principal Component Analysis

 

Probabilistic Latent Semantic Scaling

 

Thursday 1 Oct

09.00-10.00 (BST)

Patrick Rubin-Delanchy &

Song Liu

(Bristol)

Manifold structure in graph embeddings

 

Thursday 8 Oct

9.00-10.00 (BST)

Ryo Yoshida

(ISM)

Registration

Please register via Evenbrite for each event via links of titles above

This weekly online seminar series is a follow-up to the conference "High-Dimensional Bayesian inference towards quantifying real-world uncertainty" held at the University of Bristol in May 2019, and will bring together academics from the Institute of Statistical Mathematics in Japan and the University of Bristol to foster collaborations in geometric data analysis.

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