Projects

Numerical evaluation of novel methods, a.k.a. benchmarking, is a pillar of the scientific method in machine learning. However, due to practical and statistical obstacles, the reproducibility of published results is currently insufficient: many details can invalidate numerical comparisons, from insufficient uncertainty quantification to improper methodology. In 2022, the benchopt initiative provided an open source Python package together with a framework to seamlessly run, reuse, share and publish benchmarks in numerical optimization. In this project, we aim at bringing benchopt to the whole machine learning community, making it a new standard in benchmarking by empowering researchers and practitioners with efficient and valid benchmarking methods. Our goal is to ensure reproducibility and consistency in model evaluation. We will federate the machine learning community to develop informative and statistically valid benchmarks, while providing methods to reduce identified hurdles in implementing such practices. The results of the project will be integrated in the open source benchopt library.

The collection and statistical analysis of physiological signals is ubiquitous in modern life, from the continuous monitoring of patients in hospitals, to data obtained using cheap wearable devices. While statistical methods based on handcrafted quantities are efficient to capture well identified effects, they require clear hypotheses on the underlying physiological processes. Alternatively, data-driven unsupervised approaches offer an opportunity to explore and leverage such signals for population well-being. Yet, off-the-shelve generic unsupervised algorithms remain limited. Physiological signals are recorded together with surrounding events, that are typically not exploited by unsupervised methods. The main objective of EBUL is to develop a new generation of unsupervised learning methods that jointly model physiological signals and events. EBUL will develop dedicated machine learning and statistical signal processing methods and favor the emergence of new challenges for these fields focusing on five open problems: 1) end-to-end unsupervised methods to jointly model physiological signals and events, 2) physiological events' models with multivariate Point Processes embedded in space, 3) machine learning and statistical tools for actionable feedbacks from the learned representations, 4) fast algorithms that can scale for experimental data, and 5) physiological signals processing tools to impact general anesthesia and neuroscience. These challenges will be tackled through contributions to self-supervised learning methods and point processes. The methods developed in EBUL will have broad applications for fields where physical signals enriched with events are processed. Yet, the primary purpose of EBUL will be to process physiological signals, in particular in the neuroscience and anesthesiology fields. The open source software produced in EBUL will empower practitioners with the necessary tools to uncover new findings about the signals' dynamic.

I am the co-leader of a work-package focused on AI methods to process results from large scale physical simulations to reduce the I/O pressure on the machine.

In the context of bio-signal modeling, unsupervised techniques that highlight recurring patterns permit to assess the local structure of the signal but fail to capture the global one. With POSP, I propose to develop efficient tool that couple such techniques with Point Processes to account for the global temporal dependencies of non-stationary signals.