Short bio

MSc in Applied Mathematics / Diplôme d’Ingénieur – Ecole Polytechnique

MSc in Social Data Science – University of Oxford

Thesis title

Fairness in machine learning: insights from social choice.

Short abstract

Designing fair algorithms has recently appeared as a major issue in machine learning, and more generally in AI, while it has been studied for long in economics, especially in social choice theory.  My goal is to bring together the notions of fairness of the two communities, and leverage the concepts and mathematical tools of social choice to address the new challenges of fairness in machine learning.

Short bio

• Manipal University (India, BTech)
• Cesi school of Engineering (Software majors, Ecole de engineer)
• Munster Technological University (MSc Artificial Intelligence)

Thesis title

Extracting information related to the Scientific Articles published and making a knowledge base out of it, with the application of various AI / Machine learning based techniques.

Short abstract

Every years thousand’s of scientific papers are published in the academia covering various scientific proofs theorems and relations in a form of a Pdf document. I am enrolled in a TheoremKb (A project led by Pierre Senellart) to extract information from the scientific articles while training Machine learning models to identify / relate various documents together based upon the information expressed in the article (including the mathematical proof’s).

Short bio

• BSc. Applied Mathematics, BA. Neuroscience
• MSc. Biotechnology, Brown University, USA

Thesis title

Rapidly identifying therapeutics of Alzheimer’s Disease using millions of Drosophila larvae and amortized inference.

Short abstract

Alzheimer’s Disease (AD) affects millions of people worldwide, yet the limited treatments address only the physiological symptoms instead of the cause of pathogenesis. The goal of this PhD project is to establish a new pipeline for measuring AD phenotypes that leverages the advantages of Drosophila as a model system for circuit studies and links probabilistic behavior to disease progression. The pipeline builds on automated machine learning to rapidly analyze data from millions of larvae.

Short bio

Master in Theoretical Physics, ENS Paris

Thesis title

Deep learning: from toy models to modern architectures.

Short abstract

My research focuses on understanding how deep neural networks are able to generalize despite being heavily overparametrized. On one hand, I use tools from statistical mechanics to study simple models, and try to understand when and why they overfit. On the other hand, I investigate how different types of inductive biases affect learning, from fully-connected networks to convolutional networks to transformers.

Short bio

• Diplôme de l’Ecole polytechnique
• MSc of Computational and Mathematical Engineering – Stanford University

Thesis title

Study of causal networks.

Short abstract

Causal inference is very important to a wide range of use from clinical trials to econometrics: we learned that “correlation is not causation” but how can we learn true causal relationships? We will using learning of causal diagrams using the latest advances in optimization. We will also study experimental design, given your current knowledge, how to best use your limited resources to gain insightful causal information (e.g., by doing biological experimentations)?

Short bio

Master 2 MVA , ENS Paris-Saclay

Thesis title

Apprentissage non-supervisé de représentations et applications en santé (Unsupervised representation learning and applications in healthcare).

Short abstract

Unsupervised representation learning has recently caught up with the performance of supervised approaches thanks to the introduction of contrastive methods. The mathematical study of these new methods is essential to better understand and exploit them as well as provide guarantees on their performance. Their application is especially relevant in healthcare where supervision is commonly lacking.