CREMESE Robin

Institut Pasteur

robin.cremese [at] pasteur.fr

Short bio

Master’s degree – INSA Toulouse

Thesis topic

Visualization and live segmentation from medical CT scans and MRIs: a new approach mixing probabilistic learning and Virtual reality.

Short abstract

Development of learning methods for optimal path planning in a constrained environment. Reinforcement Learning methods as well as physical simulations will be studied. Possible applications to the medical domain for the preventive planning of operations.

Nishimwe Lydia

Inria

lydia.nishimwe [at] inria.fr

Short bio

Bachelor of Science in Mathematics and Computer Science, Université Grenoble Alpes

Master of Engineering in Mathematics and Computer Science, École Centrale de Nantes

Thesis topic

Robust Neural Machine Translation.

Short abstract

Neural machine translation models struggle to translate texts that differ from the “standard” data commonly used to train them. In particular, social media texts pose many challenges because they tend to be “noisy”: non-standard use of spelling, grammar and vocabulary; typographical errors; use of emojis, hashtags and at-mentions; etc. I aim to develop new methods to better translate these texts.

LE PRIOL Emma

Université Paris Cité and Kap Code

emmalepriol [at] gmail.com

Short bio

Master’s degree : mathematics (Université Paris – Dauphine) and social sciences (Sciences Po Paris)

Thesis topic

Using NLP to leverage social media data in the study of rare diseases.

Short abstract

My PhD thesis aims at exploring NLP techniques to study the online contents from rare diseases’ patients or their caregivers.  The first goal is to better understand natural histories of the studied diseases, and compare the spontaneously reported symptoms to symptoms collected during medical interviews. The other goal is to study how patients’ associations become invested in public policy governance, in particular by acquiring a vast knowledge collectively.

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.

GARCIA PINEL Ricardo

PhD student

Inria

ricardo-jose.garcia-pinel {at} inria.fr

Short bio

Bachelor of Engineering in Telecommunication Technologies and Services, Technical University of Madrid (UPM)
Master of Science in Telecommunication Engineering, Technical University of Madrid (UPM)

Thesis topic

Learning visuomotor policies for robotic manipulation and navigation.

Short abstract

Current industrial robots are mostly restricted to predefined tasks in controlled environments and are lacking the ability to adapt to new settings. Hence, the use of robots in cluttered and changing environments where the robot is required to adapt behaviors during task execution
presents a major challenge. Existing methods rely on hand-engineered methods which typically overfit to a particular setting and need to be re-designed for every new task and environment. To deal with those challenges, future robots should be equipped with advanced perception coupled with control enabling navigation and manipulation in previously unseen and dynamically changing environments. The scientific objective of this thesis is to design, develop and evaluate new models and algorithms for learning visuomotor policies for goalconditioned robotic manipulation and navigation.

CHABAL Thomas

PhD student

Inria

thomas.chabal [at] inria.fr

Short bio

Master’s degree in Applied Mathematics and Computer Science – Ecole des Ponts ParisTech (diplôme d’ingénieur)

Master’s degree in Mathematics, Vision and Learning (MVA) – Ecole Normale Supérieure Paris-Saclay

Thesis topic

Physics and Learning for Visually-Guided Robotics.

Short abstract

The purpose of this PhD is to enhance robots autonomy in unknown environments by learning visually-guided behaviors from sensory data. We plan to automate action plans by developing methods combining modern computer vision techniques with geometric reasoning which will lead to fast search methods, able to adapt on-the-fly to previously unseen environments.

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. 

GODEY Nathan

PhD student

Inria

nathan.godey [at] inria.fr

Short bio

Masters of Engineering, Ecole des Ponts

Thesis topic

Cheap and expressive neural contextual representations for textual data.

Short abstract

Neural language models are pre-trained using self-supervised learning to produce contextual representations of text data like words or sentences. These representations are shaped by the pre-training procedure: its data, its task, its optimization scheme, among others. I aim at identifying ways to improve the quality of the text representations by leveraging new pre-training approaches, in order to reduce data and/or compute requirements without quality loss.

Elamrani Aïda

PhD student

Institut Jean Nicod, ENS-PSL & Chargée d’études CNRS

aidaelamrani [at] outlook.fr

Short bio

Master in Theoretical Computer Science / Aix-Marseille Université

Thesis topic

Information in the Interplay Between Mind and Matter.

Short abstract