Master’s degree – INSA Toulouse
Visualization and live segmentation from medical CT scans and MRIs: a new approach mixing probabilistic learning and Virtual reality.
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.
Inria
lydia.nishimwe [at] inria.fr
Bachelor of Science in Mathematics and Computer Science, Université Grenoble Alpes
Master of Engineering in Mathematics and Computer Science, École Centrale de Nantes
Robust Neural Machine Translation.
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.
Université Paris Cité and Kap Code
emmalepriol [at] gmail.com
Master’s degree : mathematics (Université Paris – Dauphine) and social sciences (Sciences Po Paris)
Using NLP to leverage social media data in the study of rare diseases.
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.
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.
Bachelor of Engineering in Telecommunication Technologies and Services, Technical University of Madrid (UPM)
Master of Science in Telecommunication Engineering, Technical University of Madrid (UPM)
Learning visuomotor policies for robotic manipulation and navigation.
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.
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
Physics and Learning for Visually-Guided Robotics.
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.
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.
Masters of Engineering, Ecole des Ponts
Cheap and expressive neural contextual representations for textual data.
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.
PhD student
Institut Jean Nicod, ENS-PSL & Chargée d’études CNRS
aidaelamrani [at] outlook.fr
Master in Theoretical Computer Science / Aix-Marseille Université
Information in the Interplay Between Mind and Matter.
