5.1.1 quantCurves

• Keyword:
  Statistical modeling
• Functional Description:
  Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves.
• News of the Year:
  Software developed in 2022.
• URL:
  https://­cran.­r-project.­org/­web/­packages/­quantCurves/­quantCurves.­pdf
• Contact:
  Sandie Ferrigno

5.1.2 Harissa

• Name:
  Hartree approximation for inference along with a stochastic simulation algorithm
• Keywords:
  Gene regulatory networks, Reverse engineering, Molecular simulation
• Functional Description:
  Harissa is a Python package for both inference and simulation of gene regulatory networks, based on stochastic gene expression with transcriptional bursting. It was implemented in the context of a mechanistic approach to gene regulatory network inference from single-cell data.
• News of the Year:
  New version in 2022 associated with an application paper (hal-03942224).
• URL:
  https://­github.­com/­ulysseherbach/­harissa
• Publications:
  hal-03942224, hal-03370296, hal-03370228, hal-01646910
• Contact:
  Ulysse Herbach

6.1.1 Quasi-stationary distributions

We are continuing our research on quasi-stationary distributions (QSD), that is, distributions of Markov stochastic processes with absorption, which are stationary conditionally on non-absorption. For models of biological populations, absorption usually corresponds to extinction of a (sub-)population. QSDs are fundamental tools to describe the population state before extinction and to quantify the large-time behavior of the probability of extinction.

Thanks to the previous general result of the team in  53, together with B. Cloez (INRAE), we proved in 26 the exponential convergence of a chemostat model, whose dynamics are highly degenerate due to a deterministic part, towards a unique quasi-stationary distributions.

We also finalized an important work 7 that provides general criteria for the exponential convergence of conditional distributions of absorbed Markov processes when the convergence is not uniform with respect to the initial distribution. Our results allow to characterize a large subset of the domain of attraction of the minimal QSD and apply to a large range of stochastic processes, including diffusion processes and perturbed dynamical systems. We completed this work with specific studies of the periodic case in 24 and the case of reducible processes in 25. In this last work, we were in particular able to characterize cases with polynomial speed of convergence to a QSD and to prove the existence of a QSD for general processes in denumerable state spaces, assuming only aperiodicity, the existence of a Lyapunov function and the existence of a point in the state space from which the return time is finite with positive probability.

Motivated by our work with M. Benaïm (Univ. Neuchâtel) on degenerate processes such as hypoelliptic diffusions  48, we studied in 21 the links between Feller properties and quasi-compactness of general semigroups. This work allows to clarify the links between existing results on QSDs for hypoelliptic diffusions. We also provided in 6 a counterexample to the uniqueness of a quasi-stationary distribution for a diffusion process which satisfies the weak Hörmander condition.

In collaboration with A. Watson (Univ. College London, UK), we studied a general fragmentation-growth equation with unbounded rates. The work is decomposed into two parts: in the first part, it is shown that the equation admits a unique solution on a certain definition space; in the second part, spectral properties of the solution are established 36. The proofs are largely based on 7 and on fine properties of piecewise deterministic Markov processes.

In 33, we obtained a central limit theorem and Berry-Esseen estimates for Markov processes conditioned to never be absorbed (so-called $Q$-processes) under the general conditions of 7. We also obtained in 34 quasi-ergodic theorems for particular time-inhomogeneous Markov processes, whose time-inhomogeneity is asymptotically periodic, with applications to processes absorbed at a moving boundary.

6.1.2 Adaptive dynamics in biological populations

We continued our study of parameter scalings of individual-based models of biological populations under mutation and selection, taking into account the influence of negligible but non-extinct populations. In a work within the ERC SINGER, in collaboration with S. Méléard (École Polytechnique), S. Mirrahimi (Univ. Montpellier) and V.C. Tran (Univ. Paris Est Marne-la-Vallée) 23, we were able to give an individual-based justification of the Hamilton-Jacobi equation of adaptive dynamics (see e.g.  70), with a specific parameter scaling that is promising for the study of local (in space) extinction of sub-populations. The analysis of models allowing for such an extinction is the next step of this project.

We also worked on general evolutionary models of adaptive dynamics under an assumption of large population and small mutations. This year, we obtained in 22 existence, uniqueness and ergodicity results for a centered version of the Fleming-Viot process of population genetics, which is a key step to recover variants of the canonical equation of adaptive dynamics, which describes the long time evolution of the dominant phenotype in the population, under less stringent biological assumptions than in previous works. We plan to complete this project next year.

6.1.3 Multi-type bisexual branching process and branching processes with Moran interaction

The asexual multi-type Galton-Watson branching processes as well as the single-type bisexual processes have been studied in the literature. In particular, survival condition of the processes are well known in both cases. However, until now, the multi-type bisexual branching processes have only been studied in very specific situations and no general mathematical description has been established yet.

In 31, we studied general multi-type bisexual branching processes with superadditive mating function. We exhibited a necessary and sufficient condition for almost sure extinction, we proved a law of large numbers for our model and we studied the long-time convergence of the rescaled process.

In collaboration with E. Horton (Inria ASTRAL team) and A. Cox (Univ. of Bath, UK), we proposed a new population size model gathering in the same framework branching processes and processes with Moran type interactions. We studied this process in a setting of large population and long time 27. Its dynamics is related to the evolution of a non-conservative semi-group, whose spectral properties provide information on the long time behavior of the model.

6.1.4 Reconstruction of epigenetic landscapes from single-cell data

Joint work with E. Ventre (ENS Lyon), T. Espinasse (Univ. Lyon 1), G. Benoit (Univ. Rennes 1) and O. Gandrillon (ENS Lyon).

The aim is to better understand how living cells make decisions (e.g., differentiation of a stem cell into a particular specialized type), seeing decision-making as an emergent property of an underlying complex molecular network. Indeed, it is now proven that cells react probabilistically to their environment: cell types do not correspond to fixed states, but rather to “potential wells” of a certain energy landscape (representing the energy of the possible states of the cell) that we are trying to reconstruct. The achievement of this year is to show that the same mathematical model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions 35.

6.1.5 Modeling of chronic obstructive pulmonary disease

Joint work with I. Dupin (Univ. Bordeaux), E. Maurat (Univ. Bordeaux) and J.-M. Sac-Epée (IECL).

Lung exposure to various types of particules, such as those present in cigarette smoke, can lead to chronic obstructive pulmonary disease (COPD). COPD bronchi are an area of intense immunological activity and tissue remodeling, as evidenced by the extensive immune cell infiltration and changes in tissue structures. This allows the persistent contact between resident cells and stimulated immune cells. Our hypothesis is that the contact between cells is a major cause of chronic destructive or fibrotic manifestations. We aim to analyze the potential cell-cell interactions in situ in human tissues, to characterize in vitro the dynamics of the interplay, and to define a computational model with intercellular interactions which fits to experimental measurements and explains the macroscopic properties of cell populations. The effects of potential therapeutic drugs modulating local intercellular interactions will be tested by simulations. Two papers have been submitted this year 29, 30.

6.1.6 Numerical simulation of diffusions

In a collaboration with A. Lejay (Inria PASTA team) and their PhD student A. Anagnostakis, D. Villemonais proposed a method for approximating general, singular diffusions by discrete time and state space processes 1. One of the main interests compared to existing methods is to propose a numerical method whose main computational cost is done upstream and thus represents a fixed cost, independently of the number of simulations performed afterwards.

6.1.7 A toy model of an animal foraging

We considered a toy model of an animal foraging in a one-dimensional space. The position of the animal as time $t$ elapses is described by a standard Brownian motion. To survive, it needs one unit of food per unit of time, and it may stockpile any extra supply for future use, without any upper limit on the size of the stock nor any expiry date for its consumption. As for the provision of food, we assume that only half of the space is initially filled with one unit of food per unit length, and that there is no replenishment. We studied this model in  14, determining in particular the joint distribution of the position and time of death of the animal.

6.2.1 Cramér–von Mises goodness-of-fit tests in regression models

Join work with R. Azaïs (Inria, ENS Lyon) and M.-J. Martinez (Univ. Grenoble Alpes).

Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly heteroscedastic) regression model. Most of them are ‘directional’ in that they detect departures from a given assumption of the model. Other tests are ‘global’ (or ‘omnibus’) in that they assess whether a model fits a dataset on all its assumptions. We focus on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. We consider two nonparametric ‘directional’ tests and one nonparametric ‘global’ test, all based on generalizations of the Cramér-von Mises statistic.

To perform these goodness-of-fit tests, we have developped the R package cvmgof  42, an easy-to-use tool for practitioners, available from the Comprehensive R Archive Network (CRAN). The use of the library is illustrated through a tutorial on real data and simulation studies are carried out in order to show how the package can be exploited to compare the 3 implemented tests. The practitioner can also easily compare the test procedures with different kernel functions, bootstrap distributions, numbers of bootstrap replicates, or bandwidths. The package was updated last year, this is its third version. An article 2 has been published on this work in 2022.

We are now working on nonparametric tests associated with the functional form of the variance of the regression model. For this, we continue to work on the global test of Ducharme and Ferrigno in order to compare its performance with directional tests associated with the variance of the model. Many simulations are in progress and a part of this work has been presented in CMStatistics 2022 conference 37. This will also make it possible to propose a more general package-type tool allowing to validate the regression models used in practice.

To complete this work, we plan to assess the other assumptions of a regression model such as the additivity of the random error term. The implementation of these directional tests would enrich the cvmgof package and offer a complete easy-to-use tool for validating regression models. Moreover, the assessment of the overall validity of the model when using several directional tests will be compared with that done when using only a global test. In particular, we will discuss the well-known problem of multiple testing by comparing the results obtained from multiple test procedures with those obtained when using a global test strategy.

6.2.2 Online data analysis and online learning

Stochastic approximation is an important tool for the analysis of streaming data, introduced by Robbins and Monro in 1951, that can be used for example to estimate online parameters of a regression function  56 or centers of clusters in unsupervised classification  50. Another type of stochastic approximation processes was introduced by Benzécri in 1969 for estimating eigenvectors and eigenvalues of the $M$-symmetric expectation of a random matrix $A$ using independent observations of $A$. In all these processes, it is assumed that we can observe independent observations of the random matrix and that we take into account one or a mini-batch of observations per step. We are interested in the study of cases where we can't have independent observations and cases where at each step, we take into account all the observations up to this step without having to store them. Experiments we have conducted show that this second type of process generally converges faster than the first type with a mini-batch of observations at each step.

On this topic, our works with E. Albuisson (CHRU Nancy) on constrained binary logistic regression with online standardized data 13 and on the construction and update of an online ensemble score involving linear discriminant analysis and logistic regression 12, described in the previous activity report, are now published.

In the article 32, we establish an almost sure convergence theorem of two stochastic approximation processes for estimating eigenvectors of the unknown $Q$-symmetric expectation $B$ of a random matrix: the first one is the classical process proposed by Oja and the second one makes use of past and current observations at each step. This theorem extends previous theorems to the case where the metric $Q$ is unknown and estimated online in parallel. We apply these results to streaming PCA and streaming generalized canonical correlation analysis of a random vector $Z$, both when a mini-batch of observations of $Z$ is used at each step or when all the observations up to the current step are used. Other applications are in progress, such as streaming multiple factor analyses and streaming partial PCA, as well as convergence theorems of Robbins-Monro type processes.

6.3.1 Estimation of reference curves for fetal weight

Our research in the field of epidemiology focuses on fetal development in the last two trimesters of pregnancy. Reference or standard curves are required in this kind of biomedical problems. Values that lie outside the limits of these reference curves may indicate the presence of a disorder. Data are from the French EDEN mother-child cohort (INSERM). This is a mother-child cohort study investigating the prenatal and early postnatal determinants of child health and development. 2002 pregnant women were recruited before 24 weeks of amenorrhoea in two maternity clinics from middle-sized French cities (Nancy and Poitiers). From May 2003 to September 2006, 1899 newborns were then included. The main outcomes of interest are fetal (via ultra-sound) and postnatal growth, adiposity development, respiratory health, atopy, behaviour and bone, cognitive and motor development. We are studying fetal weight and height as a function of the gestional age in the third trimester of pregnancy. Some classical empirical and parametric methods such as polynomial regression are first used to construct these curves. For instance, polynomial regression is one of the most common parametric approaches for modeling growth data, especially during the prenatal period. However, these classical methods build upon restrictive assumptions on estimated curves. We therefore propose to work with semi-parametric LMS methods, by modifying the response variable (fetal weight) with, among others, Box–Cox transformations. An article detailing these methodologies applied to the EDEN data should be submitted next year.

Alternative nonparametric methods as Nadaraya-Watson kernel estimation, local polynomial estimation, B-splines or cubic splines are also developed to construct these curves. The practical implementation of these methods requires working on smoothing parameters or choice of knots for the different types of nonparametric estimation. In particular, optimal choice of these parameters has been proposed. To fit these curves, we have developped the R package quantCurves  39, an easy-to-use tool for practitioners. In addition, a graphical interface (GUI) intended for practitioners is being developed to enable intuitive visualization of the results given by the package and an article is in progress.

6.3.2 Prediction of silencing experiments on gene networks for chronic lymphocytic leukemia

We are working with L. Vallat (CHRU Strasbourg) on the inference of dynamical gene networks from RNAseq and proteome data. The goal is to infer a model of gene expression allowing to predict gene expression in cells where the expression of specific genes is silenced (e.g. using siRNA), in order to select the silencing experiments which are more likely to reduce the cell proliferation. We expect the selected genes to provide new therapeutic targets for the treatment of chronic lymphocytic leukemia. This year, we have developed a package for the statistical analysis of temporal gene expression datasets with several biological conditions (in particular for exploratory analysis and the detection of differentially expressed genes), which will be submitted soon to Bioconductor.

6.3.3 Covariates selection in high-dimensional data of genetic profiles in oncology

Joint work with T. Boukhobza and H. Dumond from CRAN, and B. Bastien from biopharmaceutical industry Transgene.

We proposed a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. We first applied our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we applied our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments. This work is published in 3 and is implemented in the R package ‘ARMADA’.

6.3.4 Multidimensional statistical analysis of information for clinical use

The start-up EMOSIS develops blood tests relying on flow cytometry in order to improve in vitro diagnosis of vascular thrombosis. This technology leads to multiparametric measurements on tens of thousands cells collected from each blood sample. Manual methods of analysis classically used in flow cytometry are based on data visualization by means of histograms or scatter plots. Recent progresses in the active area of computational methods for dimension reduction suggest many directions of improvement of the classical approaches for the analysis of flow cytometry data. Our first goal is to define and operate such methods. We started to focus on methods of information geometry and topological analysis of data. Once appropriate methods will be identified, an important aspect to consider will be visualization of the results in a way easy to interpret by clinicians and ethically permissible. On a longer term, our ambition is to design more accurate prediction tools for diagnosis.

8.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

8.2.1 ERC projects

N. Champagnat is scientific collaborator of the ERC SINGER (AdG 101054787) on Stochastic dynamics of sINgle cells, coordinated by S. Méléard (Ecole Polytechnique). He is involved in the research axes “From stochastic processes to singular Hamilton-Jacobi equations” and “Lineages and time reversed trajectories” of this project.

9.1.1 Scientific events: organization

9.1.2 Scientific events: selection

9.1.3 Journal

9.1.4 Invited talks

• N. Champagnat has been invited to give talks at the scientific day “Eco-Evo-Math: Building on Twenty Years of Research and Training at the Crossroads of Ecology, Evolution, and Mathematics” in Paris in July, at the “AG du département BioSiS du CRAN” in Nancy in May, at the Journées de Probabilités 2022 in Orbey in May, at the workshop on Pólya urns, stochastic approximation and quasi-stationary distributions in Bath in April, at the Journée cancérologie : innovations et expérience patient, d'hier à demain in Nancy in April, and at the workshop on Population Dynamics and Statistical Physics in Synergy in Oberwolfach in March.
• C. Fritsch has been invited to give talks at the Mathematical models in ecology and evolution in Paris in March, and at the Mathematical models in ecology and evolution conference in Reading in July.
• V. Hass has been invited to give talks at the Mathematical models in ecology and evolution conference in Reading in July, at MPDEE 2022 (Models in Population Dynamics, Ecology and Evolution) in Turin in June, and at the Journées de Probabilités 2022 in Orbey in May. He also gave seminar talks at the "Séminaire de l'équipe SPOC (Statistiques, Probabilités, Optimisation et Contrôle)" in Dijon in May, and at the "Séminaire de probabilités et systèmes dynamiques" in Amiens in March.
• U. Herbach has been invited to give talks at the London Institute for Mathematical Sciences in London in March, at the Laboratoire de Biologie et Modélisation de la Cellule in Lyon in April, at the Journées Math Bio Santé in Besançon in October, and at the Groupe de travail de Statistique in Rouen in December.
• W. Oçafrain has been invited to give a talk at the workshop on Pólya urns, stochastic approximation and quasi-stationary distributions in Bath in April.
• D. Villemonais has been invited to give talks at the Workshop "Singular diffusions: numerical and theoretical aspects" in Nancy in May, at the Journées de Probabilités 2022 in Orbey in May and at the workshop on Pólya urns, stochastic approximation and quasi-stationary distributions in Bath in April. He also gave seminar talks at the Séminaire de calcul stochastique de Strasbourg in December in Strasbourg, at the Séminaire de probabilités d'Orsay in November in Orsay and at the Séminaire FHU - CARTAGE in Nancy in November.
• N. Zalduendo Vidal has been invited to give talks at the Journées de Probabilités 2022 in Orbey in May, and at the Mathematical models in ecology and evolution conference in Reading in July.

9.1.5 Leadership within the scientific community

A. Gégout-Petit is vice-president of the European Network for Business and Industrial Statistics (ENBIS).

9.1.6 Scientific expertise

A. Gégout-Petit was in the hiring committees of a biostatistics ‘Professeur’ position at Sorbonne Univ., and three ‘Chaire de professeur junior’ (CPJ) at CNRS (INSMI, SPJ Monaie), Univ. Lorraine (Biostatistics), and Univ. de Pau et des Pays de l'Adour (Artificial Intelligence).

9.1.7 Research administration

• N. Champagnat is a member of the COMIPERS and the Commission Information Scientifique et Technique of Inria Nancy - Grand Est and Responsable Scientifique for the library of Mathematics of the IECL. He is also local correspondent of the COERLE (Comité Opérationel d'Évaluation des Risques Légaux et Éthiques) for the Inria Research Center of Nancy - Grand Est.
• C. Fritsch is a member of the Commission du Développement Technologique of Inria Nancy-Grand Est and of the Commission du personnel of IECL. She was the local Radar correspondent for the Inria Research Center of Nancy - Grand Est until June.
• A. Gégout-Petit is the head of IECL.

9.2.1 Teaching

BIGS faculty members have teaching obligations at Univ. Lorraine and are teaching at least 192 hours each year. They teach probability and statistics at different levels (Licence, Master, Engineering school). Many of them have pedagogical responsibilities.

• D. Villemonais is the head of the Mathematical Engineering Major of ENSMN, Université de Lorraine.
• T. Bastogne is in charge of the research master program “Santé Numérique et Imagerie Médicale” with the Faculty of Medicine, Université de Lorraine.
• Licence: V. Brodu, Probability Theory tutorial, 40h, L3, first year of ENSMN, Université de Lorraine.
• Licence: V. Brodu, Numerical Analysis tutorial, 20h, L3, first year of ENSMN, Université de Lorraine.
• Master: N. Champagnat, Introduction to Quantitative Finance, 12h, M1, second year of ENSMN, Université de Lorraine.
• Master: N. Champagnat, Introduction to Quantitative Finance, 9h, M2, third year of ENSMN, Université de Lorraine.
• Master: S. Ferrigno, Experimental designs, 4.5h, M1, fourth year of EEIGM, Université de Lorraine.
• Master: S. Ferrigno, Data analyzing and mining, 36h, M1, second year of ENSMN, Université de Lorraine.
• Master: S. Ferrigno, Modeling and forecasting, 32h, M1, second year of ENSMN, Université de Lorraine.
• Master: S. Ferrigno, Training projects, 18h, M1/M2, second and third year of ENSMN, Université de Lorraine.
• Licence: S. Ferrigno, Descriptive and inferential statistics, 60h, L2, second year of EEIGM, Université de Lorraine.
• Licence: S. Ferrigno, Statistical modeling, 60h, L2, second year of EEIGM, Université de Lorraine.
• Licence: S. Ferrigno, Mathematical and computational tools, 20h, L3, third year of EEIGM, Université de Lorraine.
• Licence: S. Ferrigno, Training projects, 40h, L1/L3, first, second and third year of EEIGM, Université de Lorraine.
• Master: C. Fritsch, Inverse problem, 18h, M1, second year of ENSMN, Université de Lorraine.
• Licence: C. Fritsch, Probability Theory tutorial, 40h, L3, first year of ENSMN, Université de Lorraine.
• Master: A. Gégout-Petit, Statistics, modeling, data analysis, 80h, master in applied mathematics, Université de Lorraine.
• Licence: V. Hass, Mathématiques FIGIM 1A, 38h, L1/L2, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Mathématiques FIGIM 2A, 19h, L2, second year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Probabilités, 40h, L3, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Analyse numérique et optimisation, 45h, L3, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Recherche opérationnelle, 18h, L3, first year of ENSMN, Université de Lorraine.
• Master: V. Hass, Méthodes stochastiques pour le calcul, 14h, M1, second year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Mathématiques FIGIM 1A, 70h, L1/L2, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Mathématiques FIGIM 2A, 19h, L2, second year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Probabilités, 40h, L3, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Analyse numérique et optimisation, 45h, L3, first year of ENSMN, Université de Lorraine.
• Licence: V. Hass, Recherche opérationnelle, 18h, L3, first year of ENSMN, Université de Lorraine.
• Licence: R. Loubaton, Inférence statistique, 42h, L3, first year of ENSMN, Université de Lorraine.
• Master: R. Loubaton, Analyse de données, 18h, M1, second year of ENSMN, Université de Lorraine.
• Master: R. Loubaton, Introduction à l'apprentissage automatique, 14h, M1, second year of ENSMN, Université de Lorraine.
• Master: R. Loubaton, Introduction au deep learning, 14h, M1, second year of ENSMN, Université de Lorraine.
• Licence: R. Loubaton, Analyse numérique, 44h, L3, first year of ENSMN, Université de Lorraine.
• Licence: R. Loubaton, Remédiation mathématique pour étudiants étrangers, 36h, L3, first year of ENSMN, Université de Lorraine.
• Licence: R. Loubaton, Géométrie et vecteurs pour la physique, 25h, L1, first year of EEIGM, Université de Lorraine.
• Licence: R. Loubaton, Analyse, 25h, L1, first year of ENGSI, Université de Lorraine.
• Master : A. Rago, Modélisation et Prévision, 14h, M1, second year of ENSMN, Université de Lorraine.
• Licence : A. Rago, Analyse numérique et optimisation, 20h, L3, first year of ENSMN, Université de Lorraine.
• Master : A. Rago, Analyse de données, 18h, M1, second year of ENSMN, Université de Lorraine.
• Master : A. Rago, Statistiques pour la grande dimension, 18h, M2 IMSD/third year of ENSMN, Université de Lorraine.
• Master: D. Villemonais, Probability Theory II, 63h, M1, second year of ENSMN, Université de Lorraine.
• Master: D. Villemonais, Stochastic processes, 32h, Master 2 MFA, Université de Lorraine.
• Master: D. Villemonais, Modeling and forecasting, 14h, M1, second year of ENSMN, Université de Lorraine.
• License: D. Villemonais Probability Theory I, 57h, L3, first year of ENSMN, Université de Lorraine.
• Master: S. Wantz-Mézières, Learning and analysis of medical data, 36h, with J.M. Moureaux, M2 SNIM, Université de Lorraine.
• Licence: S. Wantz-Mézières, Applied mathematics for management, financial mathematics, Probability and Statistics, 160h, IUT Nancy-Charlemagne (L1/L2/L3), Université de Lorraine.
• Licence: S. Wantz-Mézières, Probability, 100h, first year in TELECOM Nancy (initial and apprenticeship cursus), Université de Lorraine.
• Licence: N. Zalduendo Vidal, Probability Theory tutorial, 40h, L3, first year of ENSMN, Université de Lorraine.
• Licence: N. Zalduendo Vidal, Numerical Analysis tutorial, 20h, L3, first year of ENSMN, Université de Lorraine.

9.2.2 Supervision

9.2.3 Juries

• PhD: N. Champagnat, reviewer, PhD thesis of Apolline Louvet, “Modèles probabilistes de génétique des populations pour les populations en expansion”, Institut polytechnique de Paris.
• PhD: A. Gégout-Petit, reviewer, PhD thesis of Guillaume Bottaz-Bottom, “Classification de trajectoires d’observances de patients atteints d’un syndrome d’apnées obstructives du sommeil”, Univ. Grenoble-Alpes.
• HDR: A. Gégout-Petit, reviewer, HDR thesis of Frédéric Proia, “Autorégressifs à coefficients variables — Modèles graphiques partiels — Applications aux sciences du vivant”, Univ. Angers.
• HDR: A. Gégout-Petit, HDR thesis of Romain Azaïs, “Approches algorithmiques pour la statistique : processus déterministes par morceaux et arbres aléatoires”, ENS Lyon.
• PhD: A. Gégout-Petit, thesis of Olivier Coudray, “Un point de vue statistique sur les critères de fatigue: de la classification supervisée à l’apprentissage positif-non labellisé”, Université Paris-Saclay.
• PhD: A. Gégout-Petit, thesis of Cécile Spychala, “Statistical analysis of road accidents in the region Franche-Comté: risk factors for accident injuries and spatial modelling for accident occurrences”, Univ. Besançon, Franche-Comté.
• PhD: B. Scherrer, reviewer, PhD thesis of Léonard Blier, "Some Principled Methodes for Deep Reinforcement Learning", Univ. Paris-Saclay.
• PhD: B. Scherrer, reviewer, PhD thesis of Giovanni Gatti Pinheiro, "Apprentissage par renforcement appliqué au Revenue Management des compagnies aériennes", Univ. Côte d’Azur.
• PhD: B. Scherrer, reviewer, PhD thesis of Chen Yan, "Asymptotically Optimal Policies for Restless Bandits", Univ. Grenoble-Alpes.
• Prize: A. Gégout-Petit, member of the committe for AMIES PhD prize.

9.3.1 Education

• S. Mézières: organisation of a research training week on NeuroOncology and Numerics, for medical and engineering students, January 2022.

9.3.2 Interventions

• C. Fritsch made two interventions in the Lycée Cormontaigne in Metz, as part of the “Chiche!” program, in November.
• S. Ferrigno: Advisor of a group of students, “Traitement statistique de données” Project, various high schools, Nancy.
• S. Ferrigno: Advisor of a group of students, “La main à la Pâte” Project, Institut médico-éducatif (IME), Commercy.
• S. Ferrigno: Advisor of a group of students, “La main à la Pâte”, “C'Génial” Projects, Colleges, Malzéville and Nancy.
• S. Ferrigno: Advisor of a group of students, “La main à la Pâte” Project, elementary schools, Nancy.
• U. Herbach gave a general public conference “Les maths peuvent-elles servir à vaincre le cancer ?” in Ambert in October, for the breast cancer national awareness campaign “Octobre Rose”. On this occasion, he also made several scientific mediation interventions in secondary and high schools of Ambert.
• R. Loubaton gave an introduction to artificial intelligence in the conference "Être humain à l'âge de l'IA" at Paris in July.
• J-M. Monnez gave a masterclass on online data analysis 38 to the BIGS working group in Nancy, in October and November.

International journals

• 1 articleA.Alexis Anagnostakis, A.Antoine Lejay and D.Denis Villemonais. General diffusion processes as the limit of time-space Markov chains.Annals of Applied Probability2023
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• 2 articleR.Romain Azaïs, S.Sandie Ferrigno and M.-J.Marie-José Martinez. cvmgof: an R package for Cramér-von Mises goodness-of-fit tests in regression models.Journal of Statistical Computation and Simulation9262022, 1246-1266
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• 3 articleB.Bérangère Bastien, T.Taha Boukhobza, H.Hélène Dumond, A.Anne Gégout-Petit, A.Aurélie Muller-Gueudin and C.Charlène Thiébaut. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology.Journal of Applied Statistics493March 2022, 764-781
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• 4 articleT.Thierry Bastogne, F.Fanny Caputo, A.Adriele Prina-Mello, S.Sven Borgos and M.Muriel Barberi-Heyob. A state of the art in analytical quality-by-design and perspectives in characterization of nano-enabled medicinal products.Journal of Pharmaceutical and Biomedical Analysis219September 2022, 114911
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• 5 articleT.Thierry Bastogne. iQbD: a TRL-indexed quality-by-design paradigm for medical device engineering.Journal of Medical Devices162June 2022, 021008
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• 6 articleM.Michel Benaïm, N.Nicolas Champagnat, W.William Oçafrain and D.Denis Villemonais. Transcritical bifurcation for the conditional distribution of a diffusion process.Journal of Theoretical ProbabilityNovember 2022
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• 7 articleN.Nicolas Champagnat and D.Denis Villemonais. General criteria for the study of quasi-stationarity.Electronic Journal of Probability2023
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• 8 articleL.Léo Darrigade, M.Marie Haghebaert, C.Claire Cherbuy, S.Simon Labarthe and B.Béatrice Laroche. A PDMP model of the epithelial cell turn-over in the intestinal crypt including microbiota-derived regulations.Journal of Mathematical Biology847June 2022, 1-67
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• 9 articleS.Sophie Deneuve, T.Thierry Bastogne, M.Mirlande Duclos, C.Céline Mirjolet, P.Pascaline Bois, P.Patrick Bachmann, L.Lara Nokovitch, P.-E.Pierre-Eric Roux, D.Didier Girodet, M.Marc Poupart, P.Philippe Zrounba, L.Line Claude, L.Letizia Ferella, A.Alessandro Iacovelli, N.Nicolas Foray, T.Tiziana Rancati and S.Sandrine Pereira. Predicting acute severe toxicity for head and neck squamous cell carcinomas by combining dosimetry with a radiosensitivity biomarker : a pilot study.TumoriMay 2022
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• 10 articleA.Alexis Dijamentiuk, C.Cécile Mangavel, A.Annelore Elfassy, F.Florentin Michaux, J.Jennifer Burgain, E.Emmanuel Rondags, S.Stéphane Delaunay, S.Sandie Ferrigno, A.-M.Anne-Marie Revol-Junelles and F.Frédéric Borges. Invert emulsions alleviate biotic interactions in bacterial mixed culture.Microbial Cell Factories221December 2023, 16
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• 11 articleM.Marine Geoffroy, M.Marine Lemesle, A.Alexandra Kleinclauss, S.Sabine Mazerbourg, L.Levy Batista, M.Muriel Barberi-Heyob, T.Thierry Bastogne, W.Wilfrid Boireau, A.Alain Rouleau, D.Dorian Dupommier, M.Michel Boisbrun, C.Corinne Comoy, S.Stéphane Flament, I.Isabelle Grillier-Vuissoz and S.Sandra Kuntz. AB186 inhibits migration of triple-negative breast cancer cells and interacts with α-Tubulin.International Journal of Molecular Sciences2312June 2022, 6859
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• 12 articleB.Benoît Lalloué, J.-M.Jean-Marie Monnez and E.Eliane Albuisson. Construction and Update of an Online Ensemble Score Involving Linear Discriminant Analysis and Logistic Regression.Applied Mathematics132February 2022, 228-242
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• 13 articleB.Benoît Lalloué, J.-M.Jean-Marie Monnez and E.Eliane Albuisson. Streaming constrained binary logistic regression with online standardized data.Journal of Applied Statistics4962022, 1519-1539
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• 14 articleJ.Julien Randon-Furling, P.Paavo Salminen and P.Pierre Vallois. On a first hit distribution of the running maximum of Brownian motion.Stochastic Processes and their Applications150June 2022
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• 15 articleL.Laurence Schenone, C.Caroline Houillier, M. L.Marie Laure Tanguy, S.Sylvain Choquet, K.Kossi Agbetiafa, H.Hervé Ghesquières, G.Gandhi Damaj, A.Anna Schmitt, K.Krimo Bouabdallah, G.Guido Ahle, R.Remy Gressin, J.Jérôme Cornillon, R.Roch Houot, J.-P.Jean-Pierre Marolleau, L.-M.Luc-Matthieu Fornecker, O.Olivier Chinot, F.Frédéric Peyrade, R.Reda Bouabdallah, C.Cécile Moluçon-Chabrot, E.Emmanuel Gyan, A.Adrien Chauchet, O.Olivier Casasnovas, L.Lucie Oberic, V.Vincent Delwail, J.Julie Abraham, V.Virginie Roland, A.Agathe Waultier-Rascalou, L.Lise Willems, F.Franck Morschhauser, M.Michel Fabbro, R.Renata Ursu, C.Catherine Thieblemont, F.Fabrice Jardin, A.Adrian Tempescul, D.Denis Malaise, V.Valérie Touitou, L.Lucia Nichelli, M.Magali Le Garff-Tavernier, A.Aurélie Plessier, P.Philippe Bourget, C.Caroline Bonmati, S.Sophie Wantz-Mézières, Q.Quentin Giordan, V.Véronique Dorvaux, C.Cyril Charron, W.Waliyde Jabeur, K.Khê Hoang-Xuan, L.Luc Taillandier and C.Carole Soussain. Intensive chemotherapy followed by autologous stem cell transplantation in primary central nervous system lymphomas (PCNSLs). Therapeutic outcomes in real life-experience of the French network.Bone Marrow Transplantation576April 2022, 966-974
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• 16 articleA.Adrien Taccoen, C.Christian Piedallu, I.Ingrid Seynave, A.Anne Gégout-Petit and J.-C.Jean-Claude Gégout. Climate change-induced background tree mortality is exacerbated towards the warm limits of the species ranges.Annals of Forest Science791December 2022, 23
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International peer-reviewed conferences

• 17 inproceedingsS.Shideh Rezaeifar, R.Robert Dadashi, N.Nino Vieillard, L.Léonard Hussenot, O.Olivier Bachem, O.Olivier Pietquin and M.Matthieu Geist. Offline Reinforcement Learning as Anti-Exploration.AAAI 2022 - 36th AAAI Conference on Artificial IntelligenceVancouver, CanadaFebruary 2022
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Scientific books

• 18 bookF.Frédéric Bertrand, M.Myriam Maumy-Bertrand, A.Aurélie Muller, S.Sandie Ferrigno, D.Didier Marx and Y.Yacouba Rabba Idi. Mathématiques pour les sciences de l’ingénieur - Tout le cours en fiches: 3ème édition.Dunod2022, 576 pages
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• 19 bookA.Anne Gégout-Petit, M.Myriam Maumy-Bertrand, G.Gilbert Saporta and C.Christine Thomas-Agnan. Données manquantes.Editions TechnipJune 2022
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Scientific book chapters

• 20 inbookA.Anne Gégout-Petit, M.Myriam Maumy-Bertrand, G.Gilbert Saporta and C.Christine Thomas-Agnan. Une histoire lacunaire.Données manquantesEditions TechnipJune 2022, 1-27
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Reports & preprints

• 21 miscM.Michel Benaïm, N.Nicolas Champagnat, W.William Oçafrain and D.Denis Villemonais. Quasi-compactness criterion for strong Feller kernels with an application to quasi-stationary distributions.April 2022
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• 22 miscN.Nicolas Champagnat and V.Vincent Hass. Existence, uniqueness and ergodicity for the centered Fleming-Viot process.March 2022
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• 23 miscN.Nicolas Champagnat, S.Sylvie Méléard, S.Sepideh Mirrahimi and V.-C.Viet-Chi Tran. Filling the gap between individual-based evolutionary models and Hamilton-Jacobi equations.May 2022
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• 24 miscN.Nicolas Champagnat and D.Denis Villemonais. Quasi-limiting estimates for periodic absorbed Markov chains.2022
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• 25 miscN.Nicolas Champagnat and D.Denis Villemonais. Quasi-stationary distributions in reducible state spaces.January 2022
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• 26 miscB.Bertrand Cloez and C.Coralie Fritsch. Quasi-stationary behavior for an hybrid model of chemostat: the Crump-Young model.May 2022
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• 27 miscA. M.Alexander M.G Cox, E.E Horton and D.D Villemonais. Binary branching processes with Moran type interactions.July 2022
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• 28 miscA.Aurélie Deveau, A.Anne Gégout-Petit and C.Clémence Karmann. Penalized polytomous ordinal logistic regression using cumulative logits. Application to network inference of zero-inflated variables.August 2022
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• 29 miscI.Isabelle Dupin, E.Edmée Eyraud, É.Élise Maurat, J.-M.Jean-Marc Sac-Epee and P.Pierre Vallois. Probabilistic Cellular Automata modeling of intercellular interactions in airways : complex pattern formation in patients with Chronic Obstructive Pulmonary Disease.October 2022
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• 30 miscE.Edmée Eyraud, E.Elise Maurat, J.-M.Jean-Marc Sac-Epee, P.Pauline Henrot, M.Maeva Zysman, P.Pauline Esteves, T.Thomas Trian, H.Hugues Bégueret, P.-O.Pierre-Oliver Girodet, M.Matthieu Thumerel, R.Romain Hustache-Castaing, R.Roger Marthan, F.Florian Levet, P.Pierre Vallois, C.Cécile Contin-Bordes, P.Patrick Berger and I.Isabelle Dupin. Short-range interactions between fibrocytes and CD8+ T cells in COPD bronchial inflammatory response.October 2022
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• 31 miscC.Coralie Fritsch, D.Denis Villemonais and N.Nicolás Zalduendo. The Multi-type Bisexual Galton-Watson Branching Process.June 2022
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• 32 miscJ.-M.Jean-Marie Monnez. Stochastic approximation of eigenvectors and eigenvalues of the Q -symmetric expectation of a random matrix.2022, 1-15
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• 33 miscW.William Oçafrain. A central limit and Berry-Esseen theorem for continuous-time Markov processes conditioned not to be absorbed.March 2022
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• 34 miscW.William Oçafrain. An ergodic theorem for asymptotically periodic time-inhomogeneous Markov processes, with application to quasi-stationarity with moving boundaries.April 2022
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• 35 miscE.Elias Ventre, U.Ulysse Herbach, T.Thibault Espinasse, G.Gérard Benoit and O.Olivier Gandrillon. One model fits all: combining inference and simulation of gene regulatory networks.June 2022
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• 36 miscD.Denis Villemonais and A.Alexander Watson. A quasi-stationary approach to the long-term asymptotics of the growth-fragmentation equation.February 2022
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Other scientific publications

• 37 inproceedingsS.Sandie Ferrigno and M.-J.Marie-José Martinez. GOODNESS-OF-FIT TESTS FOR VARIANCE FUNCTION IN REGRESSION MODELS.CMStatistics 2022London, United KingdomDecember 2022
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Educational activities

• 38 unpublishedJ.-M.Jean-Marie Monnez. Analyse des données en flux. Analyse en composantes principales et méthodes dérivées.October 2022, DoctoralFrance
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Softwares

• 39 softwareS.Sandie Ferrigno, D.Dounia Essaket, A.Arthur Mouchot, H.Hugo Breton and M.Myriam Maumy-Bertrand. quantCurves:Estimate Quantiles Curves.1.0.0March 2022CeCILL
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