YAMANE Ikko
ikko.yamane [at] dauphine.psl.eu
Short bio
Ph.D. from The University of Tokyo
Research topic
Counter factual inference with weakly supervised learning
Short abstract
In counter factual inference, one tries to predict what would happen if attributes of data were some values different from that actually observed. Existing counter factual inference methods often require expensive, controlled experiments to be conducted to collect necessary data. My research interest focuses on developing methods that only need cheaper and efficient experiments possibly with missing observations or milder conditions.
PELLEGRINI Franco
L’Ecole normale supérieure - PSL
franco.pellegr [at] gmail.com
Short bio
PhD in Condensed Matter physics from SISSA, Trieste, Italy
Research Project
Theory of neural network learning dynamics.
Short abstract
We want to describe the general mechanism that makes neural networks so effective in solving real life problems. We aim to use methods from statistical physics to build a model describing the dynamics of neural network parameters during training. We hope this insight will allow us to develop new training algorithms leading to improved networks.
OZAWA Misaki
L’Ecole normale supérieure - PSL
misaki.ozawa2045 [at] gmail.com
Short bio
PhD University of Tsukuba
Research project
Multiscale physics and wavelet transform.
Short abstract
In physics, multiscale phenomena are captured by the renormalization group. Wavelet transform is useful in analyzing multiscale behaviors. We investigate the relation between the renormalization group and the wavelet transform. Then we wish to obtain insight into how features are extracted hierarchically in neural networks.
LOUKATOU Georgia
L'Ecole normale supérieure - PSL
georgialoukatou [at] gmail.com
Short bio
PhD, École Normale Supérieure
Research project
Diversity and learnability in early language acquisition.
Short abstract
My research addresses issues of language learnability in cross-linguistic and cross-cultural settings. I follow an interdisciplinary approach, implementing computational modelling, corpus analysis and experimental methods.
CARMELI Nofar
L’Ecole normale supérieure - PSL
Nofar.Carmeli [at] ens.fr
Short bio
- B.Sc. + Master (Technion)
- PhD at Technion
Research project
The fine-grained complexity of database queries.
Short abstract
As data
analytics becomes more widespread and data becomes bigger, so grows the
importance of identifying how fast we can evaluate any given query.
My research
focuses on characterizing the database queries that allow a highly efficient
evaluation in terms of fine-grained complexity.
BARRA Sérgio
sergioncbarra [at] gmail.com
Short bio
MD PhD (Faculty of Medicine, University of Porto)
Research project
Sudden cardiac death. Research Program on Artificial Intelligence Applied to Cardiac Rhythm Disorders.
Short abstract
The identification of individuals at
long-term risk of sudden cardiac death (SCD) is a difficult task. Our research
group has been focusing on pre-emptive risk stratification and prevention of
SCD by identifying sentinel events which frequently precede SCD, a task
facilitated by recent advances in communication and remote transmission
technologies. We hypothesise that it is possible to identify a high-risk group
of subjects at risk of SCD and thus apply immediate preventive therapies.
Application of artificial intelligence to continuous device monitoring may help
identify specific patterns prior to the occurrence of a fatal event. The DAI-PP
program (centralised at HEGP) has been initiated with a 2-year enrolment and
5-year follow-up of more than 5000 patients with implantable
cardioverter-defibrillators and remote continuous monitoring. Furthermore,
systematic collection of digital electrocardiograms is part of the prospective
Paris-Sudden Death Expertise Center registry, which collects all SCD since May
2011. An improved understanding of electrical signals and their dynamics prior
to the occurrence of a fatal arrhythmic event may allow a better triage and
identification of patients at high-risk of events.
My portfolio includes a number of high-impact publications in the field of SCD prediction in the heart failure setting, most of which focusing on patients with clinical indication for implantable cardioverter-defibrillators or cardiac resynchronization therapy devices. Most of this work was developed while I was working at the Royal Papworth Hospital NHS Trust, in Cambridge, UK, and done in collaboration with Professors Eloi Marijon and Serge Boveda.
ARJOVSKY Martin
martinarjovsky [at] gmail.com
Short bio
PhD, New York University
Research project
Out of Distribution Generalization in Machine Learning.
Short abstract
Machine Learning suffers from a fundamental problem: when systems are deployed in situations different than they were trained on, they can fail catastrophically. I aim to improve a large family of these problems by leveraging ideas from causality, deep learning, and the recognition of invariant statistical patterns. I am particularly focused in high-dimensional nonlinear problems (such as vision, language, and speech).
BAKER Antoine
L’Ecole normale supérieure - PSL
antoine.baker59 [at] gmail.com
Short bio
PhD Ecole Normale Supérieure de Lyon (2011)
Research project
Compositional inference.
Short abstract
Approximate message passing algorithms, first applied to compressed sensing, are a promising approach to solve high-dimensional inference problems: their performance can be theoretically predicted and they often reach the optimal reconstruction error. We develop a modular package to ease the implementation of such algorithms: the user only has to declare the model then the inference, entropy estimation and prediction of performance are fully automated.
