BRIANCEAU Camille

ICM Institute

camille.brianceau [at] icm-institute.org

Short bio

Master degree (Diplôme d’ingénieur) at Institut d’Informatique d’Auvergne (ISIMA)

Master degree in imaging and technology for medicine (Université Clermont Auvergne)

Research project

ClinicaDL

Short abstract

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.

MAIER Jakob

PhD student

Inria

jakob.maier {at} inria.fr

Short bio

Bachelor of Science in Mathematics: Technical University of Munich

Master of Science in Statistics and Probability: Ecole Polytechnique (M1) and Université Paris Saclay (M2)

Thesis topic

Efficient algorithms for information extraction on graphs.

Short abstract

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.

BLANKE Matthieu

PhD student

Inria

matthieu.blanke {at} inria.fr

Short bio

Master M2 Mathématiques, Vision, Apprentissage of ENS Paris-Saclay in 2021 and ingénieur de l’École polytechnique.

Thesis topic

Deep implicit layers with applications to physical systems and reinforcement learning.

Short abstract

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.

BEUGNOT Gaspard

PhD student

Inria

gaspard.beugnot [at] inria.fr

Short bio

Ecole Polytechnique – MVA

Thesis topic

Non-convex optimization and learning theory with kernel methods.

Short abstract

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. 

NOUIRA Asma

PhD student

Mines ParisTech

asma.nouira [at] mines-paristech.fr

Short bio

Master degree, National Engineering School of Sousse, Tunisia

Engineer degree, National Engineering School of Sousse, Tunisia

Thesis topic

Stable feature selection in multi-locus Genome Wide Association Studies.

Short abstract

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.

GOYENS Florentin

Postdoctoral Researcher

Dauphine-PSL

florentin.goyens [at] dauphine.psl.eu

Short bio

PhD in mathematics at the University of Oxford

Research topic

Continuous optimization.

Short abstract

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.

VINCENT Louis

PhD Student

Université de Paris / Inria / Inserm / Implicity (CIFRE thesis)

louis.vincent [at] implicity.fr

Short bio

Master 2 – Mathématiques, Vision & Apprentissage (ENS Paris-Saclay),
Master 2 – Statistiques (Sorbonne Université – Campus Pierre et Marie Curie)

Thesis title

Longitudinal data encoding applied to medical decision support in telecardiology.

Short abstract

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.

MISCHENKO Konstantin

Postdoc

Inria

konsta.mish [at] gmail.com

Short bio

PhD from KAUST, supervised by Peter Richtarik

Research topic

Optimization for machine learning.

Short abstract

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.

TAMBY Satya

Postdoc

Université Paris Dauphine-PSL

tambysatya [at] gmail.com

Short bio

PhD, Paris Dauphine-PSL

Research topic

Discrete optimization using machine learning.

Short abstract

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.

COHEN-SOLAL Quentin

Artificial Intelligence

Université Paris Dauphine-PSL

quentin.cohen-solal [at] dauphine.psl.eu

Short bio

PhD at the University of Caen

Research topic

Reinforcement learning in games.

Short abstract

This postdoc focuses on the study and improvement of learning and planning algorithms in games.