Évènements / S3

S3 Seminar
Évènements à venir

Graph signal processing for the study of multivariate physiological signals

Speaker — Laurent Oudre (Centre Borelli, ENS Paris Saclay) Abstract — In many biomedical studies, data take the form of multivariate time series, whose dimensions are highly correlated. In order to take this structure into account in data processing, graphs appear as a valid solution, defining a new analysis domain that can be considered as […]


UQSay #60

The sixtieth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, May 25, 2023. 2–3 PM — Stefano Fortunati (LSS & IPSA) — [slides] Matched, mismatched and semiparametric inference in elliptical distributions Any scientific experiment, which aims to gain some knowledge about a real-word phenomenon, starts with the data […]


Deep learning solutions to estimation and detection

Speaker — Ami Wiesel (Hebrew University of Jerusalem) Abstract — In this talk, we will discuss the use of deep learning in statistical signal processing. We will address settings in which the classical solutions are intractable and will propose modern approaches based on neural networks. We will begin with parameter estimation and focus on learning […]


Towards GNSS High Precision Navigation: Manifolds and Robust Statistics

Speaker — Daniel Medina (Institute of Communications and Navigation, German Aerospace Center) Abstract — Navigation information is an essential element for the operation of robotics platforms and intelligent transportation systems. Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, providing all-weather, all-time positioning and timing at a worldwide scale. The use […]


Probabilistic PCA for heterogeneous-quality data

Speaker — David Hong (Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, US) Abstract — Principal component analysis (PCA) is a workhorse method for identifying low-dimensional (i.e., low-rank) structure in noisy data and is ubiquitous in signal processing. However, it estimates the underlying low-rank structure sub-optimally when the samples have heterogeneous quality, […]