People
Current PhDs and Post doc
Subject: Benchopt
Subject: Rare-event detection with local pattern modeling
Co-advisor:
M. Kowalski
Subject: Decoding communication from non-invasive brain signal
Co-advisor:
S. d'Ascoli
Subject: Improving benchmarking efficiency and best practices in machine learning
Co-advisor:
G. Varoquaux
Subject: Event detection for Large-Scale Physical Simulations
Co-advisor:
Virginie Grangirard
Subject: Point process models for physiological events
Co-advisor:
J. Cartailler
Subject: Perioperative monitoring enhanced by artificial intelligence techniques based on deep neural network learning in a big data environment in anesthesia
Co-advisor:
F. Vallée
Former PhDs and Post doc
Subject: An empirical study of distributed optimization - benchmarking PCA
Co-advisor:
H. Hendrickx
Subject: Bilevel optimization for LLM learning
Co-advisor:
P. Ablin
Subject: Improving benchopt visualiation with embeded HTML
Subject: Novel solvers for Inverse Problem
Subject: Hawkes processes for physiological signals modeling
Subject: Contributions to stochastic bilevel optimization
Co-advisor:
S. Vaiter
P. Ablin
Subject: Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience FR | EN
Co-advisor:
A. Gramfort
Subject: Automatic Data Augmentation for EEG signals and invariances
Co-advisor:
A. Gramfort
Subject: A study of unrolled algorithms for dictionary learning and inverse problems, and contributions to M/EEG signal processing
Co-advisor:
M. Kowalski
Subject: Efficient whole brain estimation of the haemodynamic response function for TV-regularized semi-blind deconvolution of neural activity in fMRI
Co-advisor:
C. Leroy
P. Ciuciu