7.1.1 ARMADA

• Name:
  A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence
• Keywords:
  Biostatistics, Aggregated methods, High Dimensional Data, Personalized medicine, Variable selection
• Functional Description:
  Two steps variable selection procedure in a context of high-dimensional dependent data with few observations. A first step is dedicated to eliminate the dependency between variables (clustering of variables, followed by factor analysis inside each cluster). A second step consists in variable selection by aggregation of adapted methods.
• News of the Year:
  This package is a new one.
• URL:
  https://­cran.­r-project.­org/­web/­packages/­armada/
• Publications:
  hal-02173568, hal-02363338
• Contact:
  Aurélie Muller
• Participants:
  Aurélie Muller, Anne Gegout Petit

7.1.2 cvmgof

• Keywords:
  Regression, Test, Estimators
• Scientific Description:
  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. cvmgof focuses on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. It implements 2 nonparametric "directional" tests and one nonparametric "global" test, all based on generalizations of the Cramer-von Mises statistic.
• Functional Description:
  cvmgof is an R library devoted to Cramer-von Mises goodness-of-fit tests. It implements three nonparametric statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
• News of the Year:
  New version available on CRAN website since Jan 11 2021
• URL:
  https://­cran.­r-project.­org/­web/­packages/­cvmgof/­index.­html
• Publication:
  hal-03101612
• Contact:
  Romain Azais
• Participants:
  Sandie Ferrigno, Marie-José Martinez, Romain Azais

7.1.3 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.
• URL:
  https://­github.­com/­ulysseherbach/­harissa
• Publications:
  hal-03370296, hal-03370228, hal-01646910
• Contact:
  Ulysse Herbach

8.1.1 Reconstruction of epigenetic landscapes from single-cell data

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. A first paper proposing a reconstruction method has been submitted 26 in the framework of an international collaboration (USA, Switzerland, France). Another paper is about to be submitted 28, dealing more specifically with the inference of the underlying networks.

Joint work with Nan Papili Gao (ETH Zurich), Olivier Gandrillon (ENS Lyon), András Páldi (EPHE, Paris), and Rudiyanto Gunawan (University at Buffalo, New York)

8.1.2 Modeling and estimation of circulating tumor DNA (ctDNA) dynamics for detecting resistance to targeted therapies

Continuation of the ITMO Cancer project, supervised by Nicolas Champagnat, concerning the modeling of circulating tumor DNA (ctDNA) to detect the appearance of resistance to targeted therapies (personalized medicine). After a phase of investigation of possible scenarios in collaboration with Alexandre Harlé of the Institute of Cancerology of Lorraine (ICL), a final model was selected. Based on a mathematical analysis, the members of the project then designed a statistical inference algorithm (learning the parameters of the model, including the genealogical tree of mutations for each patient) which is intended to be validated on real data currently being acquired at the Nancy CHRU. The general idea is to exploit a “variational principle” that allows to explore the discrete space of family trees, of very large size, through a “pivot” space of continuous parameters, easy to optimize (and in reasonable numbers). A paper detailing the model and its inference is in preparation. The previous method allows for the reconstruction of intratumoral heterogeneity, i.e. the subclone composition of the tumor. Based on these data, we are currently studying models of stochastic tumor growth with an emphasis on interactions between the clones to assess the effects of different treatment strategies.

8.1.3 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 corresponds usually 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.

This year, we solved a general conjecture on the Fleming-Viot particle systems approximating QSDs: in cases where several QSDs exist, it is expected that the stationary distributions of the Fleming-Viot processes approach a particular QSD, called minimal QSD. We proved that this holds true for general absorbed Markov processes with soft obstacles in 7. We also obtained in 8 criteria based on Lyapunov functions allowing to check general conditions of  47 which characterize the exponential uniform convergence in total variation of conditional distributions of an absorbed Markov process to a unique quasi-stationary distribution. Among the various applications they give, they prove that these conditions apply to any logistic Feller diffusions in any dimension conditioned to the non-extinction of all its coordinates. This question was left partly open since the first work of Cattiaux and Méléard on this topic  46.

Together with M. Benaïm (Univ. Neuchâtel), we studied in 4 stochastic algorithms to approximate quasi-stationary distributions of diffusion processes absorbed at the boundary of a bounded domain. We considered a reinforced version of the diffusion, which is resampled according to its occupation measure when it reaches the boundary. We showed that its occupation measure converges to the unique quasi-stationary distribution of the diffusion process. We also obtained in 24 general criteria ensuring existence, uniqueness and/or exponential convergence properties for quasi-stationary distributions. The criteria were specifically designed to apply to degenerate processes such as hypoelliptic diffusions. We also provided in 25 a counterexample to the uniqueness of a quasi-stationary distribution for a diffusion process which satisfies the weak Hörmander condition.

Together with R. Schott (IECL, Univ. Lorraine), we studied in 6 models of deadlocks in distributed systems, using the approach we developped in 8 to study quasi-stationary distributions, in order to characterize and compute numerically the asymptotic behaviour of the deadlock time and the behaviour of the system before deadlock, both for discrete and for diffusion models.

8.1.4 Evolutionary models of food webs

We studied models of food web adaptive evolution in 10. We identified the biomass conversion efficiency as a key mechanism underlying food web evolution and discussed the relevance of such models to study the evolution of food webs.

In collaboration with S. Billiard (Univ. Lille).

8.1.5 Adaptive dynamics in biological populations

We studied evolutionary models of bacteria with horizontal transfer in 5. Horizontal transfer is a common mechanism of DNA exchange between micro-organisms that is thought to be responsible for fast evolution of antibiotic resistance for bacteria or evolution of virulence for pathogenes. We considered a scaling of parameters taking into account the influence of negligible but non-extinct populations, allowing us to study specific phenomena observed in these models (re-emergence of traits, cyclic evolutionary dynamics and evolutionary suicide). This work is done in collaboration with S. Méléard (École Polytechnique) and V.C. Tran (Univ. Paris Est Marne-la-Vallée).

We also worked on general evolutionary models of adaptive dynamics under an assumption of large population and small mutations. This year, we obtained 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 work next year.

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

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 2 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 develop the R package cvmgof  36, 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 at the start of 2021, this is its third version. A first article 1 has been published on this work in October 2021.

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 it in terms of performance with directional tests associated with the variance of the model. Many simulations are in progress. This will also make it possible to propose a more general package-type tool making it possible to validate the regression models used in practice.

To complete this work, it would be interesting 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 could be compared with that done when using only a global test. In particular, the well-known problem of multiple testing could be discussed by comparing the results obtained from multiple test procedures with those obtained when using a global test strategy. Another perspective of this work would be to develop a similar tool for other statistical models widely used in practice such as generalized linear models.

Join work with Romain Azaïs (INRIA, ENS Lyon) and Marie-José Martinez (LJK, Université Grenoble Alpes).

8.3.2 Online data analysis

Widening the scope of an eigenvector stochastic approximation process and application to streaming PCA and related methods. This article in collaboration with A. Skiredj was presented in the 2020 Activity Report (Section 8.3.5) and is now published in Journal of Multivariate Analysis 15.

Streaming constrained binary logistic regression with online standardized data. This article in collaboration with E. Albuisson was presented in the 2020 Activity Report (Section 8.3.5) and is now accepted in Journal of Applied Statistics 13.

Construction and update of an online ensemble score involving linear discriminant analysis and logistic regression. This article in collaboration with E. Albuisson was presented in the 2020 Activity Report (Section 8.3.5) and is being submitted 30,  63.

Stochastic approximation of eigenvectors and eigenvalues of the Q-symmetric expectation of a random matrix. Application to streaming PCA. In this analysis, we have studied the convergence of stochastic approximation processes of the Oja type for estimating eigenvectors of the unknown $Q$-symmetric expectation $B$ of a random matrix, the metric $Q$ being unknown. We have established a theorem of a.s. convergence of these processes with assumptions on the noisy observations $B_n$ of $B$ that are more general than in previous results. The estimation of eigenvectors corresponding to eigenvalues of $B$ in decreasing order is obtained using at step $n$ a Gram-Schmidt orthonormalization with respect to a random metric $Q_{n+1}$ such that $Q_{n+1}$ converges a.s. to $Q$ as $n$ goes to infinity. We have proved the a.s. convergence of specific processes to corresponding eigenvalues. Corollaries of this theorem apply in particular to cases where $E\left[B_n\right|T_n]$ or $B_n$ converges a.s. to $B$ which were studied by Monnez and Skiredj 15. In the case of a process using only the current observations at each step, we have suggested constructing another process using past and current observations. We have applied these results to the online estimation of principal components in streaming PCA of a random vector, taking into account all the observations up to the current step with possibly different weights assigned to past and current observations.

Other applications to methods related to PCA such as generalized canonical correlation analysis are in progress.

8.3.3 Change-point detection thresholds in the sequential context

To apply our algorithms of change-point to real data, we turned to some EMG signal data provided by INRS. The study concerns the development of trapezius muscle myalgia in the workplace. We apply change-point detection to characterize different computer activities carried out during an experimental day. Our analysis allowed us to characterize activities according to the frequency and amplitude of jumps and to distinguish office activities using the mouse from those using the keyboard. This work was presented in a conference paper 19.

8.4.1 Analysis of diffuse low-grade gliomas growth

In the aim of understanding the growth of low-grade glioma, we investigate multiple fields of information available in clinical practice: patient-related predictors, variables related to tumor tissue and genetics. Monitoring growth through regular MRIs gives us access to many imaging-related variables, including an original one measuring tumor infiltration (thesis defended in 2021: Cyril Brzenczek CRAN, article in preparation). Our last efforts have focused on the statistical analysis of the database composed of these variables. We have obtained a regional fund PACTE to host this database and use it for teaching, dissemination and development of experimentation tools: PIANO platform.

Join work with J.M. Moureaux, Y. Gaudeau (CRAN), F. Rech, L. Taillandier, M. Blonski, T. Obara (CHRU Nancy)

8.4.2 Estimation of reference curves for fetal weight

In Epidemiology, we are working with INSERM to study 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). It's 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 require strong assumptions. We therefore propose to work with semi-parametric LMS methods, by modifying the response variable (fetal weight) with, among others, Box–Cox transformations. A first article detailing these methodologies applied to the EDEN data should be submitted next year and is the object of the communication 31.

Alternative nonparametric methods as Nadaraya-Watson kernel estimation, local polynomial estimation, B-splines or cubic splines are also developed in this context to construct these curves. The practical implementation of these methods required 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. Then, a first version of an R package has been developed to propose a tool to construct nonparametric reference curves. It will soon be available on GitHub. In addition, a graphical interface (GUI) intended for practitioners is being developed to allow intuitive visualization of the results given by the package and an article is in progress.

Join work with Myriam Maumy-Bertrand (IRMA, Université de Strasbourg) and INSERM.

8.4.3 Construction of parsimonious event risk scores by an ensemble method. An illustration for short-term predictions in chronic heart failure patients from the GISSI-HF trial

This article in collaboration with E. Albuisson and D. Lucci was presented in the 2020 Activity Report (Section 8.4.2) and is now published in Applied Mathematics 14.

8.4.4 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 the gene expression in cells where the expression of 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 addressed the general problem of prediction as defined above, and constructed and proposed an inference method for a new gene network model for which such a prediction is possible. Next year, we expect to identify potential therapeutic targets for which silencing experiments could be conducted.

8.4.5 A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology

We propose 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. A simulation study shows the interest of the decorrelation inside the different clusters of covariates. We first apply 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 apply 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 2 and is implemented in R package ‘ARMADA’.

In collaboration with T. Boukhobza and H. Dumond from CRAN, and B. Bastien from biopharmaceutical industry Transgene.

8.4.6 Projects linked with the COVID 19 pandemic

Seroprevalence study Pierre Vallois is the scientific coordinator of the seroprevalence study COVAL Nancy held in Nancy in July 2020 in collaboration with CHRU de Nancy (CIC épidémiologie clinique and Laboratoire de Virologie).

Background. The World Health Organisation recommends monitoring the circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate anti–SARS-CoV-2 total immunoglobulin (IgT) antibody seroprevalence and describe symptom profiles and in vitro seroneutralization in Nancy, France, in spring 2020.

Methods. Individuals were randomly sampled from electoral lists and invited with household members over 5 years old to be tested for anti–SARS-CoV-2 (IgT, i.e. IgA/IgG/IgM) antibodies by ELISA (Bio-rad). Serum samples were classified according to seroneutralization activity 50 % (NT50) on Vero CCL-81 cells. Age- and sex-adjusted seroprevalence was estimated. Subgroups were compared by chi-square or Fisher exact test and logistic regression.

Results. Among 2006 individuals, 43 were SARS-CoV-2–positive; the raw seroprevalence was 2.1 % (95 % confidence interval 1.5 to 2.9), with adjusted metropolitan and national standardized seroprevalence 2.5 % (1.8 to 3.3) and 2.3 % (1.7 to 3.1). Seroprevalence was highest for 20- to 34-year-old participants (4.7 % [2.3 to 8.4]), within than out of socially deprived area (2.5 % vs 1 %, P=0.02) and with than without intra-family infection (p<10-6). Moreover, 25 % (23 to 27) of participants presented at least one COVID-19 symptom associated with SARS-CoV-2 positivity (p<10-13), with anosmia or ageusia highly discriminant (odds ratio 27.8 [13.9 to 54.5]), associated with dyspnea and fever. Among the SARS-CoV-2-positives, 16.3 % (6.8 to 30.7) were asymptomatic. For 31 of these individuals, positive seroneutralization was demonstrated in vitro.

Conclusions. In this population of very low anti-SARS-CoV-2 antibody seroprevalence, a beneficial effect of the lockdown can be assumed, with frequent SARS-CoV-2 seroneutralization among IgT-positive patients.

The results were published first in Medrxiv corresponding to 27 and in a peer-reviewed international journal 11.

SARS‐CoV‐2 positive patients in hospital predictions Participants : A. Gégout-Petit, U. Herbach, N. Thorr.

In collaboration with H. Berry, D. Gemmerlé, T. Lepoutre, D. Maucourt and D. Parsons.

We followed Inria's recommendations to get involved in the fight against COVID 19. We tried to collaborate with the LCPME laboratory in the purpose to predict the number of SARS‐CoV‐2 positive patients from the Grand Nancy metropolitan at the Nancy University Hospital from the concentration of SARS-Cov-2 residues in waste water. We have encountered difficulties with the Obépine network in obtaining raw data rather than mere indicators. We made predictions from the incidence rates available on Santé Publique France. The predictions are available on the siwam website. Inria hired Nicolas Thorr as engineer during 6 months for this project.

10.1.1 Participation in International Programs

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• N. Champagnat has been invited to give a talk at the online workshop Biostochastic Networks 2021 in November and at the online Journées Scientifiques du GE2MI sur le thème EDP et modèles biomathématiques in December.
• V. Hass has been invited to give talks at the École de la Chaire MMB in Aussois in June, at the Journées de probabilités in Guidel in June and at the Colloque des Jeunes Probabilistes et Statisticiens (JPS2021) in Saint-Pierre d’Oléron in October.
• R. Loubaton has been invited to give a talk at the Congrès des Jeunes Chercheurs en Mathématiques Appliquées 2021 (CJC-MA 2021) in Palaiseau in October.
• N. Zalduendo Vidal has been invited to give a talk at the Research School of the Chaire Modélisation Mathématique et Biodiversité at Aussois in June and at the Etheridge Group Seminar at Oxford University in June.
• U. Herbach has been invited to give a talk at the Biohasard 2021 online conference in June.

11.1.5 Leadership within the scientific community

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

11.1.6 Scientific expertise

• C. Fritsch has been a member of the Committee for junior permanent research positions of Inria Nancy - Grand Est.
• A. Gégout-Petit has been a member of several hiring committees: as President for Université Technologique de Compiègne (MCF 26e section); Sorbonne Université (PR 26th section); National jury for 46.1 Professor recruitment; University of Luxembourg, Assistant professor in statistics.

11.1.7 Research administration

• N. Champagnat is a member of the coordination committee of MODCOV19, a platform of coordination of research actions about modeling of SARS-CoV-2 (Covid-19) pandemic. He heads the bibliographic awareness group.
• N. Champagnat is a member of the Comité de Centre, 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 member of the Commission Parité-Égalité of IECL until August. She is the local Radar correspondent for the Inria Research Center of Nancy - Grand Est.
• A. Gégout-Petit is the head of “Institut Élie Cartan de Lorraine” (mathematics laboratory of Université de Lorraine).

11.2.1 Teaching

BIGS faculty members have teaching obligations at Université de 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, France.
• T. Bastogne is in charge of the research master program “Santé Numérique et Imagerie Médicale” with the Faculty of Medicine, Université de Lorraine, France.
• Master: N. Champagnat, Introduction to Quantitative Finance, 12h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: N. Champagnat, Introduction to Quantitative Finance, 9h, M2, third year of ENSMN, Université de Lorraine, France.
• Master: N. Champagnat, Problèmes inverses, 15h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: S. Ferrigno, Experimental designs, 4.5h, M1, fourth year of EEIGM, Université de Lorraine, France.
• Master: S. Ferrigno, Data analyzing and mining, 63h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: S. Ferrigno, Modeling and forecasting, 43h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: S. Ferrigno, Training projects, 18h, M1/M2, second and third year of ENSMN, Université de Lorraine, France.
• Master: A. Muller-Gueudin, Probability and Statistics, 160h, second year of ENSEM and ENSAIA, Université de Lorraine, France.
• Master: A. Muller-Gueudin, Scientific calculation with Matlab, 20h, second year of ENSAIA, Université de Lorraine, France.
• Master: A. Gégout-Petit, Statistics, modeling, data analysis, 80h, master in applied mathematics, Université de Lorraine, France.
• Master: U. Herbach, Data analyzing and mining tutorial, 18h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: U. Herbach, Introduction to probability theory, 18h, M1, second year of ENSEM (apprenticeship cursus), Université de Lorraine, France.
• Master: R. Loubaton, Analyse de données, 16h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: R. Loubaton, Introduction à l'apprentissage automatique, 6h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: S. Wantz-Mézières, Learning and analysis of medical data, 36h, with J.M. Moureaux, Master SNIM, Université de Lorraine, France.
• Master: D. Villemonais, Probability Theory II, 63h, M1, second year of ENSMN, Université de Lorraine, France.
• Master: D. Villemonais, Stochastic processes, 32h, Master 2 MFA, Université de Lorraine, France.
• Master: D. Villemonais, Modeling and forecasting, 14h, M1, second year of ENSMN, Université de Lorraine, France.
• Licence: S. Wantz-Mézières, Applied mathematics for management, financial mathematics, Probability and Statistics, 160h, IUT Nancy-Charlemagne (L1/L2/L3), Université de Lorraine, France.
• Licence: S. Wantz-Mézières, Probability, 100h, first year in TELECOM Nancy (initial and apprenticeship cursus), Université de Lorraine, France.
• Licence: A. Muller-Gueudin, Statistics, 60h, first year of ENSAIA, Université de Lorraine, France.
• Licence: S. Ferrigno, Descriptive and inferential statistics, 60h, L2, second year of EEIGM, Université de Lorraine, France.
• Licence: S. Ferrigno, Statistical modeling, 60h, L2, second year of EEIGM, Université de Lorraine, France.
• Licence: S. Ferrigno, Mathematical and computational tools, 20h, L3, third year of EEIGM, Université de Lorraine, France.
• Licence: S. Ferrigno, Training projects, 40h, L1/L3, first, second and third year of EEIGM, Université de Lorraine, France.
• Licence: C. Fritsch, Probability Theory tutorial, 40h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: V. Hass, Complément d'analyse, 38h, L1, FST, Université de Lorraine, France.
• Licence: V. Hass, Analyse numérique et optimisation, 46h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: V. Hass, Probabilités, 40h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: V. Hass, Mathématiques FIGIM 1A, 35h, L1/L2, first year of ENSMN, Université de Lorraine, France.
• Licence: V. Hass, Mathématiques FIGIM 2A, 21h, L2, first year of ENSMN, Université de Lorraine, France.
• Licence: U. Herbach, Statistics tutorial, 39h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: R. Loubaton, Inférence statistique, 21h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: R. Loubaton, Probabilités, 20h, L2, FST, Université de Lorraine, France.
• Licence: R. Loubaton, FST, Méthodes Numériques, 10h, L2, FST, Université de Lorraine, France.
• Licence: R. Loubaton, Latex, 9h, L2, FST, Université de Lorraine, France.
• Licence: R. Loubaton, Remédiation mathématique, 30h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: R. Loubaton, Analyse numérique et optimisation, 40h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: D. Villemonais, Probability Theory, 57h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: N. Zalduendo Vidal, Probability Theory tutorial, 40h, L3, first year of ENSMN, Université de Lorraine, France.
• Licence: N. Zalduendo Vidal, Numerical Analysis tutorial, 20h, L3, first year of ENSMN, Université de Lorraine, France.

11.2.2 Supervision

11.2.3 Juries

• PhD: N. Champagnat, reporter, thesis of Maxime Berger, “Le comportement critique de la quasi-espèce”, Université PSL.
• PhD: N. Champagnat, reporter, thesis of Felipe Munoz-Hernandez, “Approximation quantitative en grande population de modèles stochastiques avec interaction ou environnement variable”, Institut Polytechnique de Paris.
• PhD: N. Champagnat, reporter, thesis of Julie Tourniaire, “Spatial dynamics of interfaces in ecology: deterministic and stochastic models”, Institut Polytechnique de Paris.
• PhD: A. Gégout-Petit, president, thesis of A. Conanec Rago, Université de Bordeaux.
• PhD: A. Gégout-Petit, president, thesis of S. Yacheur, Université de Lorraine.
• PhD Prize: A. Gégout-Petit, jury member, 2021 AMIES PhD Prize.
• PhD: B. Scherrer, reporter, thesis of Yannis Flet-Berliac, “Sample-Efficient Deep Reinforcement Learning for Control, Exploration and Safety”, Université de Lille.
• PhD: B. Scherrer, reporter, thesis of Marc Etheve, “Using machine learning to solve repeated optimization problems”, CNAM Paris (CIFRE with EDF).

11.3.1 Education

• S. Ferrigno: Advisor of a group of students (EEIGM), "Traitement statistique de données" project, various high schools, Nancy, 2021
• S. Ferrigno: Advisor of a group of students (EEIGM), "La main à la Pâte" project, Institut médico-éducatif (IME), Commercy, October-November 2021
• S. Mézières: organisation of a research training week on Neurooncology and Numerics, for medical and engineering students, January 2021

11.3.2 Interventions

• C. Fritsch made two interventions in the Lycée Cormontaigne in Metz, as part of the “Chiche!” program, in December 2021.

International journals

• 1 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 SimulationOctober 2021
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• 2 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 Statistics2021, 23
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• 3 articleT.Thierry Bastogne. iQbD: a TRL-indexed quality-by-design paradigm for medical device engineering.Journal of Medical Devices2021
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• 4 articleM.Michel Benaïm, N.Nicolas Champagnat and D.Denis Villemonais. Stochastic approximation of quasi-stationary distributions for diffusion processes in a bounded domain.Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques5722021, 726-739
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• 5 articleN.Nicolas Champagnat, S.Sylvie Méléard and V. C.Viet Chi Tran. Stochastic analysis of emergence of evolutionary cyclic behavior in population dynamics with transfer.Annals of Applied Probability3142021, 1820-1867
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• 6 articleN.Nicolas Champagnat, R.René Schott and D.Denis Villemonais. Analysis of distributed systems via quasi-stationary distributions.Stochastic Analysis and Applications3662021, 981-998
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• 7 articleN.Nicolas Champagnat and D.Denis Villemonais. Convergence of the Fleming-Viot process toward the minimal quasi-stationary distribution.ALEA : Latin American Journal of Probability and Mathematical Statistics182021, 1-15
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• 8 articleN.Nicolas Champagnat and D.Denis Villemonais. Lyapunov criteria for uniform convergence of conditional distributions of absorbed Markov processes.Stochastic Processes and their Applications135May 2021, 51-74
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• 9 articleA. M.Alexander M. G. Cox, E. L.Emma L. Horton, A. E.Andreas E. Kyprianou and D.Denis Villemonais. Stochastic Methods for Neutron Transport Equation III: Generational many-to-one and $k_{\mathrm{𝚎𝚏𝚏}}$.SIAM Journal on Applied Mathematics813May 2021
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• 10 articleC.Coralie Fritsch, S.Sylvain Billiard and N.Nicolas Champagnat. Identifying conversion efficiency as a key mechanism underlying food webs evolution : a step forward, or backward ?Oikos13062021, 904-930
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• 11 articleA.Anne Gégout-Petit, H.Hélène Jeulin, K.Karine Legrand, N.Nicolas Jay, A.Agathe Bochnakian, P.Pierre Vallois, E.Evelyne Schvoerer and F.Francis Guillemin. Seroprevalence of SARS-CoV-2, Symptom Profiles and Sero-Neutralization in a Suburban Area, France.Viruses136June 2021, 1076
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• 12 articleJ.-P.Jean-Philippe Jehl, P.Pan Dan, A.Arnaud Voignier, N.Nguyen Tran, T.Thierry Bastogne, P.Pablo Maureira and F.Franck Cleymand. Transverse isotropic modelling of left-ventricle passive filling: mechanical characterization for epicardial biomaterial manufacturing.Journal of the mechanical behavior of biomedical materials119July 2021, 104492
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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 Statistics2021
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• 14 articleB.Benoît Lalloué, J.-M.Jean-Marie Monnez, D.Donata Lucci and E.Eliane Albuisson. Construction of Parsimonious Event Risk Scores by an Ensemble Method. An Illustration for Short-Term Predictions in Chronic Heart Failure Patients from the GISSI-HF Trial.Applied Mathematics127July 2021, 627-653
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• 15 articleJ.-M.Jean-Marie Monnez and A.Abderrahman Skiredj. Widening the scope of an eigenvector stochastic approximation process and application to streaming PCA and related methods.Journal of Multivariate Analysis182March 2021, 104694
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• 16 articleM.Mathias Waelli, E.Etienne Minvielle, M. X.Maria Ximena Acero, K.Khouloud Ba and B.Benoit Lalloue. What matters to patients? A mixed method study of the importance and consideration of oncology patient demands.BMC Health Services Research212021, 256
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International peer-reviewed conferences

• 17 inproceedingsR.Robert Dadashi, S.Shideh Rezaeifar, N.Nino Vieillard, L.Léonard Hussenot, O.Olivier Pietquin and M.Matthieu Geist. Offline Reinforcement Learning with Pseudometric Learning.38th International Conference on Machine Learning139virtual, FranceJune 2021
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• 18 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.36th AAAI Conference on Artificial IntelligenceVancouver, CanadaFebruary 2022
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• 19 inproceedingsN.Nassim Sahki, A.Anne Gégout-Petit and S.Sophie Wantz-Mézières. Detection of breaks in EMG signals of upper trapezius muscle activity.JDS 2021 - 52èmes Journées de Statistique de la SFdSNice / Virtual, FranceJune 2021
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Conferences without proceedings

• 20 inproceedingsT.Thierry Bastogne, S.Sanne Bevers, S.Sander Kooijmans, L.Lucie Hassler, S. E.Samir El Andaloussi, R.Raymond Schiffelers and S.Stefaan De Koker. easyQBD: A quality by design SaaS platform. Application to the development of lipid nanoparticles for mRNA delivery..6th Bioproduction CongressLyon, FranceSeptember 2021
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• 21 inproceedingsS.Sanne Bevers, S.Sander Kooijmans, E.Elien van de Velde, M.Martijn Evers, S.Sofie Seghers, J.Jerney Gitz-François, L.Lucie Hassler, K.Karine Breckpot, T.Thierry Bastogne, R.Raymond Schiffelers and S.Stefaan De Koker. Tuning LNPs to target antigen presenting cells in spleen induces CD8 T-cell responses and tumor regression in mice.18th CIMT Annual MeetingMainz, GermanyMay 2021
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• 22 inproceedingsS.Sander Kooijmans, S.Sanne Bevers, E.Elien van de Velde, M. J.Martijn J W Evers, S.Sofie Seghers, J. J.Jerney J J M Gitz-François, L.Lucie Hassler, K.Karine Breckpot, T.Thierry Bastogne, R. M.Raymond M Schiffelers and S.Stefaan De Koker. Rationally designed mRNA-loaded lipid nanoparticles provoke strong antitumor T cell immunity which critically depends on specific immune cell subsets.Annual Meeting of the Controlled Release Society, CRS 2021Virtual, United StatesJuly 2021
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Doctoral dissertations and habilitation theses

• 23 thesisN.Nassim Sahki. Data-driven methodology for sequential change-point detection for physiological signals.Université de Lorraine; École doctorale IAEM Lorraine - Informatique, Automatique, Électronique - Électrotechnique, Mathématiques de LorraineNovember 2021
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Reports & preprints

• 24 miscM.Michel Benaïm, N.Nicolas Champagnat, W.William Oçafrain and D.Denis Villemonais. Degenerate processes killed at the boundary of a domain.2021
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• 25 miscM.Michel Benaïm, N.Nicolas Champagnat, W.William Oçafrain and D.Denis Villemonais. Transcritical bifurcation for the conditional distribution of a diffusion process.December 2021
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• 26 miscN. P.Nan Papili Gao, O.Olivier Gandrillon, A.András Páldi, U.Ulysse Herbach and R.Rudiyanto Gunawan. Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data.February 2021
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• 27 miscA.Anne Gégout-Petit, H.Hélène Jeulin, K.Karine Legrand, A.Agathe Bochnakian, P.Pierre Vallois, E.Evelyne Schvoerer and F.Francis Guillemin. Seroprevalence of SARS-CoV-2, symptom profiles and seroneutralization during the first COVID-19 wave in a suburban area, France.June 2021
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• 28 miscU.Ulysse Herbach. Gene regulatory network inference from single-cell data using a self-consistent proteomic field.October 2021
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• 29 miscS.Svante Janson, C.Cécile Mailler and D.Denis Villemonais. Fluctuations of balanced urns with infinitely many colours.November 2021
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• 30 miscB.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.February 2021
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Other scientific publications

• 31 inproceedingsS.Sandie Ferrigno. Semiparametric reference curves for EDEN cohort.CMStatistics 2021Londres, United KingdomDecember 2021
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Educational activities

• 32 unpublishedJ.-M.Jean-Marie Monnez. Cours d'analyse des données et apprentissage : L'analyse en composantes principales.April 2021, MasterFrance
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• 33 unpublishedJ.-M.Jean-Marie Monnez. Méthodes de classification non supervisée.April 2021, MasterFrance
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Softwares

• 34 softwareU.Ulysse Herbach. Harissa: tools for mechanistic gene network inference from single-cell data.October 2021BSD 3-Clause "New" or "Revised" License
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