florentin.goyens [at] dauphine.psl.eu
PhD in mathematics at the University of Oxford
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.
anantha.5491 [at] gmail.com
PhD, Insitute of Mathematical Sciences, Chennai, India
Query evaluation over inconsistent databases.
We look at the dichotomy conjecture of evaluating boolean conjunctive queries over inconsistent databases with self joins.
adeline.fermanian [at] mines-paristech.fr
PhD in Statistics, Sorbonne Université
High-dimensional inference in genomic data.
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.
konsta.mish [at] gmail.com
PhD from KAUST, supervised by Peter Richtarik
Optimization for machine learning.
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.
Université Paris Dauphine-PSL
tambysatya [at] gmail.com
PhD, Paris Dauphine-PSL
Discrete optimization using machine learning.
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.
Université Paris Dauphine-PSL
quentin.cohen-solal [at] dauphine.psl.eu
PhD at the University of Caen
Reinforcement learning in games.
This postdoc focuses on the study and improvement of learning and planning algorithms in games.
manel.ayadi [at] dauphine.eu
PhD in Computer Science at LAMSADE – Paris-Dauphine
How does changing the voting system and the electoral district boundaries impact the outcome of the French legislative elections?
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.
chloe.barre [at] pasteur.fr
PhD, LPTMC (Laboratoire de Physique Théorique de la Matière Condensée), Sorbonne University, Paris
Bayesian induction of the behavior of the larva.
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.
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.
pierreablin [at] gmail.com
Understanding neural networks with differential equations.
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.
ikko.yamane [at] dauphine.psl.eu
Ph.D. from The University of Tokyo
Counter factual inference with weakly supervised learning
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.