SCHAIPP Fabian

Postdoctoral researcher

Inria

fabian.schaipp [at] inria.fr

Short bio

PhD, Technical University of Munich

Research project

Robust and adaptive training algorithms.

Short abstract

Training machine learning models amounts to solving stochastic optimization problems at scale. My research focuses on understanding and improving optimization algorithms, particularly with the aim to design robust and easily tunable methods.

FAVIEZ Carole

Postdoctoral researcher

Inserm

carole.faviez [at] inserm.fr

Short bio

PhD, Université Paris Cité

Research project

Diagnosis support of rare genetic diseases: design of diagnosis support algorithms based on hybrid methods combining symbolic artificial intelligence and machine learning.

Short abstract

Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. In this context, the objective is to integrate expert knowledge about the disease within machine learning models reusing patient data from electronic health records (EHR) to design a diagnosis support system. The model, that is meant to be used in a clinical context, must be reliable, explicable, and interoperable with EHRs.

ANDREW Judith Jeyafreeda

Postdoctoral researcher

Institut Imagine

judithjeyafreeda [at] gmail.com

Short bio

PhD, Université de Caen, Normandie

Research project

Extracting Temporal relations within clinical text documents.

Short abstract

Clinical documents have a mentions of phenotypes with the time they have been identified in a patient. These time frames are not always explicitly stated, thus automatic identification  is a challenging task. However, identifying and constructing a time frame from the identification of a phenotype and its evolution can be very useful to further clinical research. I will be developing AI models to automatically identify temporal relationships between phenotypes and time.

ZAHARIAS Paul

Postdoctoral researcher

Muséum national d'Histoire naturelle

paul.zaharias [at] mnhn.fr

Short bio

PhD at the Muséum national d’Histoire naturelle (MNHN)

Research project

Evaluation and design of fast statistical branch support methods for phylogenetic gene/species tree reconstruction.

Short abstract

Statistical support in phylogenetic tree reconstruction is essential to interpret evolutionary relationships. My goal is to design scalable statistical supports for large phylogenetic and phylogenomic datasets to overcome the limitations of current methods.

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BOUDABOUS Safa

Postdoctoral researcher

Université Paris-Dauphine-PSL

safa.boudabous [at] dauphine.psl.eu

Short bio

PhD, Computing, Data and AI, Institut Polytechnique de Paris

Research project

Detection of micro-arousal and desaturation events from heart rate in apneic patients.

Short abstract

We will work on defining a deep learning model for sleep micro-arousal and desaturation events detection from heart rate. As heart rate increase is a characteristic cardiac autonomic response to micro-arousal and desaturation, we will be using heart rate signal as a surrogate to full Polysomnography signals.

PEREZ Manolo

Postdoctoral researcher

Muséum national d'Histoire naturelle

manolo.fernandezperez [at] mnhn.fr

Short bio

PhD in on Evolutive Genetics and Molecular Biology – Federal University of Sao Carlos (UFSCar)-Brazil

Research project

Computational and machine learning-based methods in phylogenetics.

Short abstract

Deep Learning frameworks have increasingly been applied to phylogenetics, phylodynamics, and macroevolution due to their flexible and data hungry nature. Recent DL implementations have shown encouraging performance, with higher speed and accuracy than similar methods, when used with phylogenetic information to compare Birth-Death diversification models and estimate parameters for epidemiological data. Here, we propose a new DL framework that allows the incorporation of distinct strategies for simulating and representing phylogenetic information.

GILMARTIN Emer

Postdoctoral researcher

Inria

emer.gilmartin [at] inria.fr

Short bio

  • Ph.D, Trinity College Dublin, Ireland. M.Phil, Trinity College Dublin, Ireland
  • B.E (Mech), NUIG, Ireland

Short abstract of the research project

We are working with groups in Korea to understand and model the effects of interlocutor personality in dialogue. We are building a new model of ‘interpersonality’, how personality related behaviours of each participant in a conversation affect the conversation as a whole.

SUSMANN Herbert

Postdoctoral researcher

Université Paris Dauphine-PSL

Herbert.susmann [at] dauphine.psl.eu

Short bio

PhD, Biostatistics, University of Massachusetts Amherst

Research project

Predicting emergency room arrivals in the Île-de-France region.

Short abstract

We are studying the use of modern machine learning techniques to predict arrivals to emergency rooms and subsequent hospitalizations in Île-de-France. We are particularly interested in predictions methods that can provide statistically accurate characterizations of uncertainty.

TRIGG Scott

Postdoctoral researcher

Observatoire de Paris-PSL

scott.trigg [at] obspm.fr

Short bio

MA (Mathematics), MA (History of Science), PhD (History of Science), University of Wisconsin-Madison

Research project

EIDA – Editing and analysing historical astronomical diagrams with artificial intelligence.

Short abstract

The EIDA project is applying deep learning for computer vision to develop new algorithms and tools for analysis of the material and epistemological aspects of diagrams in premodern astronomy on a global scale, and inventing new standards for natively-digital critical editions of diagrams.

EL JURDI Rosana

Postdoctoral researcher

Institut du Cerveau – Paris Brain Institute

rosana.eljurdi [at] icm-institute.org

Short bio

PhD from the University of Rouen Normandie

Research project

Deep Learning for Brain Imaging:

  • Validation of Deep Learning Segmentation Models
  • Segmentation and Survival analysis of Lymphoma in Brain MRI

Short abstract

Reporting standard errors and confidence intervals is crucial in medical image segmentation research, as accurately measuring the level of improvement achieved is challenging. Current methods often report empirical standard deviation, which is biased due to data inter-dependency between folds. In this project,  we focus on estimating confidence intervals in order to determine the scientific contribution made by improved performance over the baseline using experiments on 3D image segmentation, providing a more accurate and reliable measure of performance.

BACHTIS Dimitrios

Postdoctoral researcher

École normale supérieure - PSL

dimitrios.bachtis [at] phys.ens.fr

Short bio

PhD, Swansea University

Research project

Machine learning and the renormalization group.

Short abstract

Guided by insights from the theory of disordered systems, and statistical field-theoretic techniques, we aim to further solidify connections between machine learning and the renormalization group. The project will enable us to further our understanding of neural networks and provide machine-learning enhanced
computational advances to problems of physics.

BARBIER-CHEBBAH Alex

Postdoctoral researcher

École Normale Supérieure - PSL

alex.barbier-chebbah [at] pasteur.fr

Short bio

PHD at Sorbonne University

Research project

Multi-Armed Bandit model.

Short abstract

My main research interests are random walks theory and sequential learning, focusing on their connections to decision-making task in complex environment. We combine statistical physics, Bayesian inference, information theory and numerical simulation to both probe learning procedure in insect behavior, but also to design lightweight algorithms able to mimic such procedures. In particular, relying on infotaxis methods, we develop a new class of multi-armed bandit (MAB) algorithms to achieve optimal performance at all timescales of the sequential learning procedure.

BONNAIRE Tony

Machine learning and statistical physics

École Normale Supérieure - PSL

tony.bonnaire [at] ens.fr

Short bio

PhD in Astrophysics at Université Paris-Saclay

Research project

Machine learning and statistical physics.

Short abstract

My current research focuses on understanding the dynamics of simple neural networks and particularly how gradient descent can achieve good generalization, especially when initialized randomly, in high-dimensional, rough and non-convex landscapes. For this purpose, I use methods coming from theoretical physics, and more precisely the statistical physics of disordered systems, to obtain asymptotic success conditions for these methods but also to study the topological properties of the random landscapes.

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

Postdoctoral researcher

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

Postdoctoral researcher

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.

COHEN-SOLAL Quentin

Postdoctoral researcher

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.

BARRÉ Chloé

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.