AYADI Manel

Postdoctoral Researcher

Dauphine - PSL

manel.ayadi [at] dauphine.eu

Short bio

PhD in Computer Science at LAMSADE – Paris-Dauphine

Research project

How does changing the voting system and the electoral district boundaries impact the outcome of the French legislative elections?

Short abstract

The aim of the project is to study the impact of changing the voting system (mixed electoral system, proportional representation …) and the electoral district boundaries on the outcome of the French legislative elections of 2017.

Chloé Barré

Postdoctoral Researcher

Institut Pasteur

chloe.barre [at] pasteur.fr

Short bio

PhD, LPTMC (Laboratoire de Physique Théorique de la Matière Condensée), Sorbonne University, Paris

Research project

Bayesian induction of the behavior of the larva.

Short abstract

Making decisions is a fundamental feature of animal behavior. Nevertheless, there remains a large knowledge gap in linking neural architecture and behavioral response. To bridge this gap, targeting individual neurons and having a simple read-out of their activity is crucial, and Drosophila larvae are ideal organisms for such an approach. My work is part of a larger project to explore the relationship between neural network dynamics and decision making in Drosophila larvae. I use Bayesian induction techniques and physical modeling to understand this relationship.

By combining video measurement experiments of larval behavior with advances in modern optogenetics that allow the activation/inactivation of individual neurons, a database of millions of larvae responding to the activation of single neurons has been constructed. Although a machine learning approach that projects larval videos into complex behavioral dictionaries has been developed, some images remain ambiguous and the corresponding behavior is therefore poorly detected. To improve behavior detection we describe the shape of the larva using insights from solids mechanics. Using this physical model, we perform a Bayesian induction to find parameters that describe the behavior of the larvae in a more robust way. 

Once the behaviors are properly detected and quantified, we want to detect all possible responses and modulations induced by the activation or inactivation of a neuron. We have written a simplified model that describes the dynamics and sequences of behaviors. With Bayesian inference I learn the parameters of my model and with a generative model and theses parameters I can recreate virtual larvae. These virtual larvae made it possible to separate neural responses between those provoking simple and immediate actions from those generating complex behaviors. It is thus possible to group neurons in terms of response. 

By combining the techniques of biologists with probabilistic analysis techniques (including Bayesian inference), we can identify behavioral changes due to the activation/inactivation of neurons and thus will allow us to infer causal relationships between neural activity and behavioral patterns, and uncover how behavior emerges from activity in the connectome.

ABLIN Pierre

Postdoctoral Researcher

CNRS

pierreablin [at] gmail.com

Short bio

PhD Inria

Research project

Understanding neural networks with differential equations.

Short abstract

Neural networks have encountered great empirical success, yet the reasons behind this success are still mostly unknown. It has recently been proposed to draw bridges between neural networks and differential equations. I study the nature of this link, and its implications on the theoretical and practical properties of neural networks.

YAMANE Ikko

Postdoctoral Researcher

Dauphine - PSL

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.

SEMINCK Olga

Postdoctoral Researcher

CNRS

olga.seminck [at] cri-paris.org

Short bio

Bachelor Vrije Universiteit (Amsterdam)

Master Université Paris Diderot, Doctorate Sorbonne Paris Cité

Research Topic

Computational Linguistics & Digital Humanities

Short abstract

My research project focuses on using techniques from the domain of Natural Language Processing to answer questions about language and style on big corpora of literary texts. Currently, I am working on a project about the notion of idiolect. I try to answer the questions 1) How does the idiolect of an author evolve over their lifetime ? and 2) Can we distinguish idiosyncratic changes from general diachronic language evolution?

PONTOIZEAU Thomas

Postdoctoral Researcher

Dauphine - PSL

thomas.pontoizeau [at] lamsade.dauphine.fr

Short bio

PhD in Computer Science at LAMSADE – Paris-Dauphine

Research project

Solving graph problems with machine learning and Monte-Carlo methods.

Short abstract

The aim of the project is to study graphs (combinatorial problems solving, structural aspects prediction) with tools from neural networks and Monte-Carlo methods.

PELLEGRINI Franco

Postdoctoral Researcher

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

Postdoctoral Researcher

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.

MARCHAND Tanguy

Postdoctoral Researcher

L’Ecole normale supérieure - PSL

Tanguy.marchand [at] ens.fr

Short bio

Master from Ecole Polytechnique

Master from University of Cambridge

PhD from Sorbonne Université

Research project

Simulating physical stochastic processes using Machine Learning.

Short abstract

Physical stochastic processes such as turbulence, astrophysical maps and so on provide a wide range of non-Gaussian processes. My research is to develop new Machine Learning tools to better analyze and reproduce them.

LOUKATOU Georgia

Postdoctoral Researcher

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.