Camille Castera, Researcher in Optimization and Machine Learning

Welcome

I am a researcher in optimization and machine learning. Since February 2022, I am a postdoc at the University of Tübingen, working on the TRINOM-DS project led by Peter Ochs and Jalal Fadili.

Prior to that, I wrote my PhD thesis (readable here) at the IRIT laboratory in Toulouse (supervised by Cédric Févotte, Edouard Pauwels and Jérôme Bolte). I was a member of the FACTORY project.

I graduated from INSA Toulouse where I obtained a master degree in "Applied Mathematics and Modeling". I wrote my master thesis under the supervision of Pierre Weiss and Frédéric de Gournay.

My research focuses on designing and analyzing optimization algorithms for machine learning, with second-order methods and deep learning as main topics.

Research interests

Optimization (convex & non-convex, non-smooth, stochastic)
Deep learning (neural networks training)
Second-order (Newtonian) methods
Automatic hyper-parameter tuning
Scientific programming (mostly Numpy & Pytorch)

Teaching

From 2018 to 2021 I gave tutorials and practicals to master students at INP-ENSEEIHT where I taught the following:

Tutorials & practicals on Probability (random variables, conditional expectation, etc.)
Tutorials & practicals on Statistics (estimation, tests)
Tutorials on Integration theory and Fourier transform