Master degree (Diplôme d’ingénieur) at Institut d’Informatique d’Auvergne (ISIMA)
Master degree in imaging and technology for medicine (Université Clermont Auvergne)
ClinicaDL
ClinicaDL is an open-source software for deep learning processing on neuro-imaging data. My works consists in extending this software with new features and standard deep learning tools of the community, and providing PhD students and researchers with support.
Bachelor of Science in Mathematics: Technical University of Munich
Master of Science in Statistics and Probability: Ecole Polytechnique (M1) and Université Paris Saclay (M2)
Efficient algorithms for information extraction on graphs.
We examine algorithms that extract information from a given graph which can be issued from an application or from random sampling. The information is typically obtained in the form of statistical, algebraic, or combinatorial invariants and serves in several applications: detection of a latent geometric structure, alignment of two graphs, or community identification. The main objective is to obtain theoretical guarantees for the functioning of the algorithms while assuring that they can be efficiently executed.
Master M2 Mathématiques, Vision, Apprentissage of ENS Paris-Saclay in 2021 and ingénieur de l’École polytechnique.
Deep implicit layers with applications to physical systems and reinforcement learning.
My research focuses on the use of machine learning tools to understand and control physical systems. I am interested in developing efficient methods to learn physical systems, with the concern of incorporating and taking advantage of available prior information.
Ecole Polytechnique – MVA
Non-convex optimization and learning theory with kernel methods.
Kernel methods are a versatile tool to study the statistical properties of a vast category of learning algorithm. On one hand, we aim at understanding the generalisation properties of neural network. This enable to design new and more efficient learning routines. On the other, we tackle non-convex optimisation problems through kernel sum-of-squares.
Master degree, National Engineering School of Sousse, Tunisia
Engineer degree, National Engineering School of Sousse, Tunisia
Stable feature selection in multi-locus Genome Wide Association Studies.
Our main goal is to provide a stable framework in Genome Wide Association Studies using Machine Learning, essentially feature selection models to deal with high-dimensional data. Many challenges lay ahead such as: genetic population stratification, linkage disequilibrium patterns clustering, the stability of the selection and the computational complexity. We aim to solve these issues by developing efficient algorithms applied to real data in case control studies such as breast cancer disease.
PhD in mathematics at the University of Oxford
Continuous optimization.
Most of my research is related to continuous nonconvex optimization. I am particularly interested in optimization problems with constraints, such as smooth manifolds; and second-order methods. I consider applications in numerical analysis and machine learning.
PhD Student
Université de Paris / Inria / Inserm / Implicity (CIFRE thesis)
louis.vincent [at] implicity.fr
Master 2 – Mathématiques, Vision & Apprentissage (ENS Paris-Saclay),
Master 2 – Statistiques (Sorbonne Université – Campus Pierre et Marie Curie)
Longitudinal data encoding applied to medical decision support in telecardiology.
In telecardiology as in many other fields of modern medicine, we have at our disposal large amounts of data explaining the evolution of a patient. These data can often be missing or corrupted, and data from several sources can sometimes be of different nature, which makes their exploitation difficult.
My goal is to develop a model capable of synthesizing different types of temporal data via auto-encoders to infer the state of a patient. In the context of tele-cardiology, this could for instance allow us to predict deteriorations of a patient’s health status, and thus anticipate and prevent more serious complications.
PhD from KAUST, supervised by Peter Richtarik
Optimization for machine learning.
I design new optimization algorithms for machine learning and study their convergence. I am particularly interested in stochastic methods, adaptivity, distributed training, and federated learning.
PhD, Paris Dauphine-PSL
Discrete optimization using machine learning.
Discrete optimization is a very efficient approach to solve decision problems but is extreamely costly in general. We are trying to use machine learning techniques as a heuristic to guide the exploration of the search space.
Artificial Intelligence
Université Paris Dauphine-PSL
quentin.cohen-solal [at] dauphine.psl.eu
PhD at the University of Caen
Reinforcement learning in games.
This postdoc focuses on the study and improvement of learning and planning algorithms in games.
