GOYENS Florentin

Postdoctoral Researcher

Dauphine-PSL

florentin.goyens [at] dauphine.psl.eu

Short bio

PhD in mathematics at the University of Oxford

Research topic

Continuous optimization.

Short abstract

Most of my research is related to continuous nonconvex optimization. I am particularly interested in optimization problems with constraints, such as smooth manifolds; and second-order methods. I consider applications in numerical analysis and machine learning.

Padmanabha Anantha

Postdoctoral Researcher

ENS & PSL

anantha.5491 [at] gmail.com

Short bio

PhD, Insitute of Mathematical Sciences, Chennai, India

Research project

Query evaluation over inconsistent databases.

Short abstract

We look at the dichotomy conjecture of evaluating boolean conjunctive queries  over inconsistent databases with self joins.

FERMANIAN Adeline

Postdoc

Mines ParisTech

adeline.fermanian [at] mines-paristech.fr

Short bio

PhD in Statistics, Sorbonne Université

Research project

High-dimensional inference in genomic data.

Short abstract

Our goal is to propose new efficient procedures for high-dimensional inference, motivated by applications to high-dimensional genomic data. More specifically, we are interested in identifying regions of the genome associated with a phenotype, through procedures that provide p-values, typically via post-selection inference procedures.

MISCHENKO Konstantin

Postdoc

Inria

konsta.mish [at] gmail.com

Short bio

PhD from KAUST, supervised by Peter Richtarik

Research topic

Optimization for machine learning.

Short abstract

I design new optimization algorithms for machine learning and study their convergence. I am particularly interested in stochastic methods, adaptivity, distributed training, and federated learning.

TAMBY Satya

Postdoc

Université Paris Dauphine-PSL

tambysatya [at] gmail.com

Short bio

PhD, Paris Dauphine-PSL

Research topic

Discrete optimization using machine learning.

Short abstract

Discrete optimization is a very efficient approach to solve decision problems but is extreamely costly in general. We are trying to use machine learning techniques as a heuristic to guide the exploration of the search space.

COHEN-SOLAL Quentin

Artificial Intelligence

Université Paris Dauphine-PSL

quentin.cohen-solal [at] dauphine.psl.eu

Short bio

PhD at the University of Caen

Research topic

Reinforcement learning in games.

Short abstract

This postdoc focuses on the study and improvement of learning and planning algorithms in games.

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.