Interview for the position:
Research Associate in Machine Learning for Longitudinal Population Studies

Arthur Leroy

University of Sheffield - 10/05/2021

Background

  • 2009-2012: BSc in pure mathematics at the University of La Rochelle,





Background

  • 2009-2012: BSc in pure mathematics at the University of La Rochelle,
  • 2012-2014: MSc in mathematics, major in statistics, at the University of Rennes I,




Background

  • 2009-2012: BSc in pure mathematics at the University of La Rochelle,
  • 2012-2014: MSc in mathematics, major in statistics, at the University of Rennes I,
  • 2014-2014: Research internship at National Institute for Research in Computer Science and Automation (Inria), in Paris,
  • 2016-2017: Research engineer at the National Institute of Sports, Expertise and Performance (INSEP), in Paris.

2017-2020: PhD at the University of Paris

  • Proposed a research project focusing on developing machine learning algorithms for helping with talent identification using longitudinal data of performance,
  • Won 2 grants (a PhD grant and a research funding for the lab) from research and sports ministries for this project,
  • Collaborations with the French Swimming Federation and French Athletics Federation.

Multi-Task GP with Common Mean


A. Leroy, P. Latouche, B. Guedj, S. Gey - MAGMA: Inference and Prediction using Multi-Task Gaussian Processes with Common Mean - Under submission in Machine Learning

Clustering and Prediction with a mixture of Multi-Task GPs


A. Leroy, P. Latouche, B. Guedj, S. Gey - Cluster-Specific Predictions with Multi-Task Gaussian Processes - Under submission in JMLR

Current work and perspectives

Current projects:

  • 2020- : Teaching and Research Fellow at the University of Paris,
  • Implementation of a comprehensive R package (with the associate paper) and a Python library for our Multi-Task GPs framework (supervision of an MSc intern),
  • Extensions of the framework: sparse approximations and multi-dimensional inputs,
  • Theoretical work across PAC-Bayes theory and Gaussian Processes.

Interests for the future:

  • Widen my comprehension of the GP literature, in particular on sparse approximations, online learning and kernel choice,
  • Explore and develop my skills on the specificities of high-dimensional data,
  • Explore theoretical properties of GPs, like generalisation bounds or the links with NNs,
  • Developing accessible tools for practitionners using probabilistic modelling and discover new areas of applications.

In a nutshell

Applicative contributions:

  • A. Leroy et al. - Cluster-Specific Predictions with Multi-Task Gaussian Processes - 2018
  • I. Moussa, A. Leroy et al. - REDI: adaptive and robust method for computing […] - 2019
  • R. Pla, A. Leroy et al. - Bayesian approach to quantify morphological impact […] - 2019

Methodological contributions:

  • A. Leroy et al. - MAGMA: Inference and Prediction using Multi-Task Gaussian […] - 2020
  • A. Leroy et al. - Cluster-Specific Predictions with Multi-Task Gaussian Processes - 2020

Algorithmic contributions:

Scientific community:

  • Involvement in the French Statistical Society (Young statisticians, Statistics & Sports),
  • Reviewer for Journal of the Royal Statistical Society (SC), PeerJ, Journal of Sport Science.