Welcome to the High-Dimensional Statistical Modelling Team

We are focusing on developing machine learning algorithm for science including feature selection, topological data analysis, graph neural network, optimal transport and transfer learning.

We have two goals:

  • Machine learning for science: Develop machine learning methods that help scientists finding new scientific discoveries.
  • Machine learning: Develop fundamental machine learning algorithms.

News

19, June 2022

Our GraphLime paper has been accepted to IEEE TKDE! Congrats!

15, June 2022

Our high-dimensional optimal transport paper has been accepted to ECML 2022. Congrats!

19. January 2022

Three papers has been accepted to AISTATS 2022! Congrats Yuki, Ryoma, Benjamin, Peter, Hector, and Tam!

29. September 2021

Two papers has been accepted to NeurIPS 2021! Congrats Tam and Hiroaki!

25. July 2021

One paper about optimal transport has been accepted to ICCV 2021! Congrats Tam!

19. June 2021

One paper about optimal transport has been accepted to ECML 2021! Congrats Yanbin, Hubert, and Tam!

9. May 2021

Three papers have been accepted to ICML 2021! Congrats Yuki, Ryoma, Tobias, Hector, Benjamin, Tam!

1. May 2021

Peter Naylor has joined our lab! Welcome!

19. March 2021

Our paper about AML treatment has been published at Nature Cancer!

... see all News