VERDIER Hippolyte

PhD Student

Institut Pasteur

hverdier [at] pasteur.fr

Short bio

Ingénieur polytechnicien, École polytechnique (Palaiseau)

MPhil in Computational biology, University of Cambridge (UK)

Thesis title

Combine artificial intelligence with high resolution microscopy to better dissect the mechanism of binding and mechanism of action of multi-specific biologics.

Short abstract

Photo-activated localization microscopy (PALM) enables high-resolution recording of single proteins trajectories in live cells, thus providing precious probes of small-scale properties of the protein environment. I use graph neural networks to characterize relevant physical properties of these dynamics, and developed a flexible analysis scheme able to deal both with the diversity of motion types encountered in nature and with the fact that observed trajectories inevitably differ, to some extent, from archetypal theoretic models.

ANDRAL Charly

PhD Student

Dauphine - PSL

andral [at] ceremade.dauphine.fr

Short bio

Diplome d’ingénieur – ENSAE Paris

Master Statistics And Machine Learning, Paris Saclay University

Thesis title

Improvement of MCMC methods and adaptation to the Big Data.

Short abstract

MCMC methods can have some difficulties exploring space, especially in high dimensional settings that can occur in a context of Big Data. The goal of my PhD thesis is to find enhancements to MCMC about this exploring issue.

HAIRAULT Adrien

PhD Student

PSL

hairault [at] ceremade.dauphine.fr

Short bio

MSc in Statistical Science, Oxford University

Double licence M.I.A.S.H.S, Université Paris 1 Panthéon-Sorbonne & SciencesPo Paris

Thesis title

Foundations and applications in Bayesian Mixture Modelling.

Short abstract

Mixtures are a popular class of models bridging parametric and non-parametric statistics and, as part of the standard data analysis toolkit, have ubiquitous applications in regression, clustering, machine learning, etc. One of the main goals of this thesis is to ease model selection within such a class of models, in particular by finding efficient ways of computing the marginal likelihood (aka evidence) of semi-parametric models (such as Dirichlet Process Mixtures). We also study the convergence properties of the Bayes Factor when comparing such parametric and semi-parametric models.

MOREL Rudy

PhD Student

L’Ecole normale supérieure - PSL

rudy.morel [at] ens.fr

Short bio

MS in Probabilities and Finance (ex DEA El Karoui) from UPMC

BSc in Mathematics from École Normale Supérieure of Rennes

Thesis title

Modelling of multiple time series with learning of the structure across series.

Short abstract

Many phenomena observed in nature can be described as a collection of time series (component of an audio recording, pixels of a video over time, economic agents of a complex system). The goal of this PhD is to model multiple time series and to learn the structure across series.

FOUCHE Aziz

PhD Student

Institut Curie

aziz.fouche [at] curie.fr

Short bio

M2 Bioinformatics & Modelling, Sorbonne Université

Thesis title

Integration of multi-level single cell molecular data to unravel the mechanisms of oncogene activation effect on cellular phenotypes.

Short abstract

We investigate oncogene effects on cancer cells driving the shift from wild-type to malignant phenotype using single-cell data. Getting rid of patient-specific information among several cancer datasets is a crucial consideration to answer this question, as it blurs tumoral signal of interest. Furthermore, developing methods allowing for information integration between several data acquisition techniques (RNA-seq, ATAC-seq…) may yield very insightful and relevant results for investigating systems biology of cancers.

DI FOLCO Cécile

Research Engineer

ICM Institute

cecile.difolco [at] icm-institute.org

Short bio

Engineer diploma (AGROPARISTECH)

Master of Data science («INFORMATIQUE: SYSTEME INTELLIGENTS»-UNIVERSITE PARIS-DAUPHINE)

Master of Cognitive Sciences (ENS, UNIVERSITE DE PARIS, EHESS)

Research project

Modelling neurodegenerative diseases.

Short abstract

I study the modeling of neurodegenerative diseases’ progression using imaging and clinical data. In particular, I investigate the influence of various cofactors, including genetics, on Parkison’s Disease progression.

CARMELI Nofar

Postdoctoral Researcher

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.

WALDSPURGER Irène

Mathematics

waldspurger [at] ceremade.dauphine.fr

Irène Waldspurger

Short bio

I prepared a PhD at École Normale Supérieure de Paris, on phase retrieval and scattering transforms, under the supervision of Stéphane Mallat. I defended in 2015, then spent one year as a post-doc at MIT, mentored by Philippe Rigollet. Since then, I am a CNRS researcher.

Topics of interest

Non-convex optimization, inverse problems, scattering transform

Project in Prairie

It is known that simple non-convex algorithms can sometimes efficiently solve a priori difficult problems, like phase retrieval. This phenomenon has been rigorously explained under strong statistical assumptions only. I would like to understand better under which circumstances it happens. Another direction of research I would like to pursue is to study the links between the low layers of learned convolutional networks and the scattering transform.

ROBERT Christian

Computational statistics

xian [at] ceremade.dauphine.fr / Blog: xianblog.wordpress.com

Christian Robert

Short bio

Professor at Université Paris Dauphine since 2000, part-time professor at University of Warwick (Fall 2013- ), fellow of the ASA (2012) and the IMS (1996), former editor of the Journal of the Royal Statistical Society (2006-2010) and deputy editor of Biometrika (2018-), senior member of Institut Universitaire de France (2010-2021)

Topics of interest

Foundations of Bayesian analysis, Bayesian decision theory, Markov chain simulation methods, approximate Bayesian inference

Project in Prairie

To assess and improve approximate inference methods that handle complex and big data models, in particular developing novel ABC and MCMC technology. Contribution to the PSL maths graduate school by teaching and administrating the MASH program. Animation of international conferences and summer schools in Bayesian computational statistics.

Quote

Christian Robert travaille depuis une quinzaine d’années sur les méthodes d’inférence bayésienne approximatives, induites par la complexité ou la taille des données. Ses résultats valident des méthodes de Monte Carlo sur des modèles génératifs et aident à la construction de techniques de réduction de dimension efficaces.

PEYRÉ Gabriel

Applied mathematics

gabriel.peyre [at] ens.fr

Gabriel Peyre

Short bio

CNRS research director and professor at Ecole Normale Supérieure. Director of the data sciences center of the ENS. Blaise Pascal Prize 2017 of Académie des sciences, Magenes prize 2019 from the UMI. ERC starting grant 2012, ERC consolidator grant 2017.

Topics of interest

Optimal transport, imaging sciences, machine learning

Project in Prairie

The goal of my research project is to scale Optimal Transport methods both computationally and statistically to handle high dimensional machine learning problems. As deputy scientific director of PRAIRIE, I help to coordinate the research and teaching effort of the project. I am also be involved through my chair in fundamental and collaborative researches, as well as in teaching and dissemination of research.

Quote

Optimal transport (OT) is a fundamental mathematical theory at the interface between optimization, partial differential equations and probability. It has recently emerged as an important tool to tackle a surprisingly large range of problems in data sciences, such as shape registration in medical imaging, structured prediction problems in supervised learning and training deep generative networks.