QUENNELLE Sophie
Hôpital Necker-Enfants Malades
sophie.quennelle [at] protonmail.com
Short bio
- Master 2 – Informatique biomédicale, Sorbonne Université, Paris
- Docteur en cardiologie – Université Paris Cité
Thesis title
Deep representation of the patient’s electronic health record for clinical event prediction and patient similarity.
Short abstract
Pediatric cardiologist at Necker-Enfants Malades in Paris interested in health data extraction and reuse for clinical research. Her PhD project started in October 2020 supervised by Pr. Anita Burgun and co-supervised by Dr. Antoine Neuraz. Its objective is to propose a deep learning model to provide a reliable representation of the patient electronic health record.
LAZARD Tristan
tristan.lazard [at] mines-paristech.fr
Short bio
Master 2 Mathematics and applications, UPMC
Thesis title
Deep learning for digital pathology: from full to no supervision.
Short abstract
Digital pathology involves studying digital versions of tissue slides to make diagnoses or derive prognostic markers from cells and tissue features. Deep learning can automate some diagnostic steps or help discover associations between phenotype and genotype. However, these slides are large, with full magnification versions weighing up to 16GB uncompressed, which therefore raises specific challenges. The goal of this PhD is to determine the best supervision methods to extract information from these slides.
HEMFORTH Lisa
lisa.hemforth [at] icm-institute.org
Short bio
Master in Biomedical Engineering (BioImaging) from BME Paris (Arts et Métiers, Université de Paris, PSL, Télécom)
Thesis title
Deep learning for rating of atypical anatomical patterns on MRI data.
Short abstract
Incomplete hippocampal inversion (IHI) is an atypical anatomical pattern of the brain found in 15 to 20% of the general population which’s origins and link to different pathologies are still unknown. The aim of this project is to develop automatic rating methods based on anatomical criteria to apply them to big databases which would prove difficult to annotate manually. Using this data, we will perform genome wide association studies as well as heritability studies and study correlations to different pathologies.
BRUNELLI Filippo
filippo.brunelli [at] inria.fr
Short bio
- Bachelor degree in Mathematics – University of Trento
- Master degree in Mathematics – University of Pisa
Thesis title
Graph algorithms for temporal graphs and transport networks.
Short abstract
Temporal graphs arose with the need to better model contexts where the appearance of interactions or connections depends on time, such as epidemic propagation or transport networks. While classical notions such connectivity and shortest paths have been extensively studied in static graphs there is still room for improvement in the temporal case.
Theo Bodrito / Doctorant - équipe WILLOW / Centre Inria de Paris
BODRITO Théo
theo.bodrito [at] inria.fr
Short bio
Engineer’s degree (Mines Paris)
Thesis title
Deep Learning for Exoplanet Detection.
Short abstract
The goal of this thesis is to develop machine learning models and algorithms for detecting exoplanets by using direct imaging techniques.
BONTE Thomas
thomas.bonte [at] mines-paristech.fr
SHORT BIO
Ingénieur Civil des Mines de Paris – Mines ParisTech
THESIS TITLE
Artificial intelligence for the analysis of label-free microscopy.
SHORT ABSTRACT
Deep learning algorithms outperform traditional methods for common image analysis tasks, with powerful architectures that have been developed recently. The goal of this PhD is to leverage these algorithms to provide tools for biologists in order to help them in their studies. Such deep learning methods will help them save time, avoid tedious annotation or simplify their experiments in their daily studies.
LUCIANO Antoine
antoine.luciano [at] dauphine.psl.eu
Short bio
Master MASH – Université Paris-Dauphine
Thesis title
ABC methods for PDE
LOIZILLON Sophie
sophie.loizillon [at] icm-institute.org
Short bio
Engineering Degree Bordeaux INP – Université de Bordeaux
Thesis title
Deep learning for assisting diagnosis of neurological diseases using a very large-scale clinical data warehouse.
Short abstract
The aim of this thesis is to design and validate deep learning methods for computer- assisted diagnosis of neurological disorders using a very large dataset (over 100,000 patients) from the AP-HP data warehouse.
YAZDAN PANAH Arya
arya.yazdan-panah [at] icm-institute.org
Short Bio
Diplome d’ingénieur, Grenoble INP Phelma
Thesis title
Deep learning for the analysis of medical images in multiple sclerosis.
Short abstract
Multiple sclerosis (MS) is a chronic disease of the central nervous system with both an autoimmune and a neurodegenerative component. Conventional MRI techniques poorly predict disability worsening, leading to research exploring molecular imaging with PET and new biomarkers, such as choroid plexuses (CPs), which have been proposed as a neuroinflammatory biomarker along the entire disease course. This project aims to develop deep learning methods for automatic processing of MRI data to study CP segmentation and perfusion changes, as well as to explore the clinical course of patients based on the combination of different information.
DUBOIS-TAINE Benjamin
benjamin.paul-dubois-taine [at] inria.fr
Short Bio
- Bachelor in Computer Science – McGill University
- Master 2 – Université Paris-Saclay
Thesis title
Continuous Optimization and applications.
Short abstract
We study continuous optimization problems under different forms, from smooth first-order methods to relaxation of combinatorial problems, with a strong focus on theoretical guarantees. Applications on satellite imagery are also explored.
ORTHOLAND Juliette
juliette.ortholand [at] icm-institute.org
Short bio
- Master, Sorbonne University
- Engineer, Mines Paristech
Research project
Modelling changes of dynamic over longitudinal data.
Short abstract
The objective is to develop models for longitudinal data and events in the context of neurodegenerative diseases. The models could then be used for prediction and/or for the description of the pathology.
MOHAPATRA Biswesh
biswesh.mohapatra [at] inria.fr
Short bio
Integrated Master of Technology in Computer Science Engineering from IIIT Bangalore
Research project
Improving multimodal dialogue systems through conversational grounding.
Short abstract
This project plans to dive deep into the issues regarding conversational grounding. The thesis intends to do the following – 1) It will investigate why modern neural networks trained on vast amounts of data are unable to solve the phenomenon of conversational grounding in current dialogue systems. 2) It will investigate old approaches to conversational grounding for neural network-based models. 3) It will look into the role of nonverbal behavior such as eye gaze and head nods in conversational grounding, and how insights from cognitive science studies of these phenomena can be integrated into deep learning approaches. 4) The project aims to then build computational models that take conversational grounding into account and help the state-of-the-art conversational models like BlendorBot [5] or dialoGPT [6] to generate more consistent dialogues. 5) It will also develop methods to quantify and test the phenomenon of conversational grounding. 6) Finally, it will evaluate the success of these models in human-chatbot conversation, by looking at whether users are more successful in human-computer collaborative tasks.
CREMESE Robin
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
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
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
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
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
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.
