I am a researcher in the parietal team @ INRIA Saclay, since autumn 2019, working on unsupervised learning for time series and on deep learning methods applied to solving inverse problems.
My research interests touch several areas of Machine Learning, Signal Processing and High-Dimensional Statistics. In particular, I have been working on Convolutional Dictionary Learning, studying both their computational aspects and their possible application to pattern analysis. I am also interested in theoretical properties of learned optimization algorithms such as LISTA, in particular for the resolution of inverse problems.
I did my PhD under the supervision of Nicolas Vayatis and Laurent Oudre, in the CMLA @ ENS Paris-Saclay. My PhD focuses on convolutional representations and their applications to physiological signals. I continued working on unsupervised learning for time series with application to electrophysiological recordings during a 1,5 year in the Parietal Team. I am also involved in open-source projects such as joblib or loky, for parallel scientific computing, and benchopt, for reproducible benchmarks in optimization.
Latest publication and projects
Numerically Efficient Parametric Inference for Learning Space-Time Hawkes Processes 2025
Emilia Siviero, Guillaume Staerman, Stephan Clémençon, Thomas MoreauIn DSAA
Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can accurately capture. This paper aims to develop a fast and flexible parametric inference technique to recover the parameters of the kernel functions involved in the intensity function of a space-time Hawkes process based on such data. Our statistical ...
Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can accurately capture. This paper aims to develop a fast and flexible parametric inference technique to recover the parameters of the kernel functions involved in the intensity function of a space-time Hawkes process based on such data. Our statistical approach combines three key ingredients: 1) kernels with finite support are considered, 2) the space-time domain is appropriately discretized, and 3) (approximate) precomputations are used. The inference technique we propose then consists of a l2 gradient-based solver that is fast and statistically accurate. In addition to describing the algorithmic aspects, numerical experiments have been carried out on synthetic and real spatio-temporal data, providing solid empirical evidence of the relevance of the proposed methodology.
Reproducibility and frugality in AI benchmarking: lessons from Benchopt 27 Nov 2025,
At OSDays
Research and development in modern AI are primarily driven by empirical work, benchmarking new methods to evaluate relative performance. However, the statistical variability inherent in evaluation processes is often poorly accounted for, leading to a validation crisis in which genuine advances are difficult to discern. This talk will explore the concept of reproducibility for AI benchmarking, and and discuss recent work on the “validation crisis” — showing how cross-validation can ...
Research and development in modern AI are primarily driven by empirical work, benchmarking new methods to evaluate relative performance.
However, the statistical variability inherent in evaluation processes is often poorly accounted for, leading to a validation crisis in which genuine advances are difficult to discern.
This talk will explore the concept of reproducibility for AI benchmarking, and and discuss recent work on the “validation crisis” — showing how cross-validation can drastically improve the robustness of algorithm rankings.
I will also present Benchopt, a framework designed to facilitate reproducible, reusable and extendable benchmarking of optimization and AI algorithms, which is a key component in addressing these crisis while also accounting for the need for frugality in modern AI research.
Loky Apr 2023
The aim of this project is to provide a robust, cross-platform and cross-version implementation of the ProcessPoolExecutor class of concurrent.futures.
The aim of this project is to provide a robust, cross-platform and cross-version implementation of the ProcessPoolExecutor class of concurrent.futures. It features:
Deadlock free implementation: one of the major concern in standard multiprocessing and concurrent.futures libraries is the ability of the Pool/Executor to handle crashes of worker processes. This library intends to fix those possible deadlocks and send back meaningful errors.
Consistent spawn behavior: All processes are started using fork/exec on POSIX systems. This ensures safer interactions with third party libraries.
Reusable executor: strategy to avoid respawning a complete executor every time. A singleton pool can be reused (and dynamically resized if necessary) across consecutive calls to limit spawning and shutdown overhead. The worker processes can be shutdown automatically after a configurable idling timeout to free system resources.
27 Nov 2025,
