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Tom M. Ragonneau
Actively developing software for derivative-free optimization.

Self introduction

I am a Ph.D. candidate in the Department of Applied Mathematics at The Hong Kong Polytechnic University, advised by Dr. Zaikun Zhang and Prof. Xiaojun Chen, and supported by the University Grants Committee (UGC) of Hong Kong, under the Hong Kong Ph.D. Fellowship Scheme (HKPFS, ref. PF18-24698).

Research overview

My research interests include mathematical optimization and its applications, especially methods based on inaccurate information and methods dedicated to derivative-free optimization.

Selected publications

[1] R. Benshila, G. Thoumyre, M. Al Najar, G. Abessolo Ondoa, R. Almar, E. Bergsma, G. Hugonnard, L. Labracherie, B. Lavie, T. M. Ragonneau, S. Ehouarn, B. Vieublé, and D. Wilson (2020). A deep learning approach for estimation of the nearshore bathymetry. J. Coast. Res., 95(sp1), 1011–1015.

Education

Ph.D. student in computational mathematics
2019–Present
M.Sc. in scientific computing
Toulouse INP, ENSEEIHT · Toulouse, France
2018–2019
M.Eng. in computer science and applied mathematics
Toulouse INP, ENSEEIHT · Toulouse, France
2016–2019
  • Graduated in High Performance Computing and Big Data.

Experiences

Teaching assistantship
2020–2021

Revision Tutorial Sessions (RTS) for

  • AMA1110 Basic Mathematics I – Calculus and Probability & Statistics.
  • AMA1120 Basic Mathematics II – Calculus and Linear Algebra.

Recent posts

Proofs of the Kantorovich inequality.
2022-01-04
4 min read