# 6. Software contributions

The methods presented in the previous sections were included into mainly two pieces of software dedicated to hidden Markov models and statistical analysis of plant structures.

## 6.1. Chainxem: a Matlab library for HMC/HMT analysis

Chainxem is a Matlab library dedicated to inference in HMC and HMT models. Originally I developed the library during my PhD thesis as an extension of the ancestor of Mixmod<fnref n="8" />, to relax an independence assumption in mixture models and consider time or tree dependencies.

During L. Donini's PhD thesis (Subsection 4.2), the library was extended by the Xerox company to include connexions with timeout optimization and reinforcement learning models.

Then extensions to categorical and multivariate observations were motivated by new research work on eye-movement analysis, as a precursory implementation of the models developed later in B. Olivier's PhD thesis.

The library now contains about 10,000 lines of code. It is available at http://mistis.inrialpes.fr/people/jbdurand/software.html#chainxem_en.

## 6.2. Tree Statistic: statistical models on trees for plant structure analysis

Tree Statistic is a python module dedicated to the statistical analysis of plant structure data. This is a part of the Structure Analysis<fnref n="9" /> component of the OpenAlea<fnref n="10" /> project. OpenAlea is an open source platform aiming at connecting different models from the plant research community. It includes modules to analyse and model plant architecture, growth and functioning. The principle is to provide some software architecture to compile libraries developed with different programming languages and to make them available in python, through wrappers if they are not native python libraries.

Tree Statistic is an extension of the Sequence Analysis module, dedicated to the analysis of biological sequences. It mainly contains Markov-switching models, including hidden semi-Markov or variable-order Markov models (including Generalized Linear Mixed Models as emission distributions) and multiple change-point detection. It is more specifically oriented towards discrete observations (nominal-, ordinal-, integer-valued) but also includes some continuous observation distributions. The module was used and extended during B. Olivier's PhD thesis, in relation with hidden Semi-Markov models (see Subsection 4.3). The core of the library is composed by C++ classes with python wrappers implemented through Boost.Python.

Tree Statistic relies on the same base classes (histograms, discrete and continuous distributions) and aims at providing statistical models for the analysis of tree-structured data. It provides data structures and (hidden) Markov models on trees, as presented in Sections 2 and 5. As explained in these sections, general dependency assumptions within trees rely on modelling

<footnote n="8">⁸http://www.mixmod.org/?lang=en</footnote>

<footnote n="9">⁹https://github.com/openalea/StructureAnalysis</footnote>

<footnote n="10">¹⁰https://github.com/openalea</footnote>
multivariate count data and identifying probabilistic graphical models, also included as com-
ponents of the library. Since my post-doctoral fellowship at CIRAD/AMAP, I have been the
maintainer and main developer of the library. During his PhD studies, P. Fernique contributed
significantly by adding graphical models and parametric multivariate count distributions. The
library now contains about 50,000 lines of code, among which 75% is C++, 15% is native
python and the remainder is Boost.Python. The library is interfaced with other Structure
Analysis components: sequence analysis, multiscale tree graphs (Godin and Caraglio, 1998)
and PlantGL. The latter is a graphical toolkit for the creation, simulation and analysis of 3D
virtual plants and was used to produce the 3D figures of this manuscript (e.g., Figures 23 and
27 in Section 5). We plan to unify data structures with the Tree Matching library dedicated
to computation of edit distances and mappings between tree graphs (see Subsection 2.2) and
make them a coherent “Tree Analysis” component of Structure Analysis.
# 7. Conclusion and perspectives

As a conclusion, our main contribution is the specification, implementation and development of numerical methods for hidden Markov models on sequences and tree structures, particularly for the analysis of plant growth and architecture. This led us to develop new tools and selection methods in graphical probabilistic and multivariate count modelling. Motivated by specific contexts of applications, new families of hidden Markov chains were also proposed: coupling HMCs with decisions processes and optimization in the context of optimal timeout modelling, coupling HSMCs in the case of heterogeneous, asynchronous signals with application to joint EEG and eye-movement analysis. These models try to address generic questions and to go beyond the original motivation justifying their development. Plant structure analysis was, in a first step, mainly driven by cognitive motivations: can we characterize plant structures through quantitative models? How well do they predict tree shapes, their variability and the effect of environmental or other factors? Our hidden Markov tree models provided partial answers to the question: They often had to be complemented by other approaches, such as edit distance computation, tree matching, supervised and unsupervised machine learning at different scales, whenever thorough model integration seemed hardly possible. In such contexts, workflows of treatments were applied, often not accounting for the uncertainty or variability underlying the models at previous steps of the analysis.

We originally analysed data sets that were rather sparse and collected manually. However some years later, the automatic collection of data allowed plant researchers to access bigger collections of structured measurements, the structure often being accessible only indirectly. Thus, a need for reconstruction methods emerged and we participated (modestly) to this new challenge. As a corollary, measurements of structured data on whole progenies were accessible. This raised the question of accounting for new sources of variability (now genetic) with their potential interactions with ontogeny and environment. Then, how to assess their impact on complex structures (tree graph, multiscale tree or more general graphs)? We addressed the problem by incorporating mixed effects in models acting at local scales, with the assumption that global properties would essentially emerge from local interactions included in the models.

Although perspectives are provided separately in each subsection of the manuscript, we would like to present ongoing work and more global future research avenues. Some research partners focused on ecological questions, now considering larger spatial scales. Instead of isolated individuals or forest / orchard plots, tropical forests are the object of interest, with the aim of monitoring their evolution, particularly in relation with global warming, from the points of view of composition, leaf area density and production (covering both storage and exchange with atmosphere). Data acquisition is achieved by satellite imaging, terrestrial and aerial LiDAR. In such environments, data are characterized by much more sources of heterogeneity, primarily related to devices, climate conditions at various time scales – particularly both the day of measurement (rain, wind) and at coarser time scales, thus impacting tree growth globally – soil and forest composition or other semi-local environmental features, stage of plant development, repeated measurements on a same vs. different individuals and interception of laser beams by leaves vs. wood. From a statistical point of view, these sources may be viewed as many latent
<figure><img src="image_34.png" /><figcaption>Figure 32: Total cow population (left-hand part) and BSE (right-hand part) counts in the different districts. Levels of grey correspond to intervals whose bounds are indicated in legends, e.g. white corresponds to 0 to 4576 cows.</figcaption></figure>

variables subject to some hierarchy through spatial and time scales, from plant elementary components to forests.

From a statistical point of view, new topics were recently investigated in my research team, particularly regarding hidden Markov random fields (HMRFs) and Bayesian non-parametric (BNP) analysis. Contributions from both fields joined when considering image segmentation. HMRFs are a popular approach (Kato and Zerubia, 2012) but suffer from the high complexity of model selection, which determines the unknown number of segments. HMRFs are state-space models where categorical latent state variables take their values in {1, ..., K}, K being an unknown number of states. In classical settings, different models have to be estimated separately for each considered value of K. Then these models have to be compared using statistical criteria. One possibility to avoid successive estimation and assessment of each model is offered by reversible jump MCMC methods (Kato, 2008), in which the very high dimension of parameter and latent variable space can make convergence critically slow. Chatzis and Tschpenakis (2010) and later, Lü *et al.* (2019) proposed a Bayesian non-parametric prior on the MRF, thus allowing a *priori* unbounded values of K. Inference was addressed by a variational Bayesian EM (VBEM) algorithm, which is less CPU-demanding than MCMC. The emission distributions were Gaussian. During F. Dama's masters internship, we developed an extension to discrete observation processes (modelled in a first step as Poisson) and are now considering further extensions to zero-inflated or over- and underdispersed data. Firstly, we applied this model to disease mapping in cases of bovine spongiform encephalopathy (BSE) in France. The data set is depicted in Figure 32. The associated segmentation and estimated emission parameters are represented in Figure 33.

We now believe that forest monitoring and reconstruction from LiDAR data could be addressed in a principled way using an integrated Bayesian framework. The global model would
<figure><img src="image_35.png" /><figcaption>Figure 33: BSE case ratio (left-hand part) and BNP-MRF segmentation with MAP (right-hand part). For BSE case ratio, colours correspond to intervals whose bounds are indicated in legend, e.g. white corresponds to 0 to 6.4 cases per 10⁵ cows.</figcaption></figure>

include a hierarchy of latent variables and model blocks associated with the different scales of representation (plant elementary components, axes, individuals, species or ecological clusters, homogeneous forest plots, whole forest), including available covariates. For example, the model block associated with homogeneous forest plots could be a BNP-MRF with multivariate count distributions (e.g., multinomial distributions if homogeneous plots are defined by similar species proportions, or graphical discrete models as in Section 3 if changes in dependencies and total numbers of trees are considered as discriminant). To make the approach feasible from the viewpoint of computing time and memory, the data set could be processed in partially redundant slices, accounting for conditional independence relationships: given the cluster value representing homogeneous growth conditions, individuals are assumed to be independent, etc. However, since observations are essentially one cloud of points, slices would have to be overlapping to ensure that each individual is fully contained within at least a slice. Once individuals are segmented, points are not relevant any more at coarser scales and can be freed in memory.

In a different but related context, we aim at estimating leaf area densities (LADs) from similar measurements, except that they consists in the number of hits of laser beams with leaves, together with segment lengths between hits (or cases of non-interception), referred to as free path lengths, instead of 3D points.

Let $(\theta, \Phi)$ be respectively the elevation and azimuth components of the beam incidence direction. On the one hand and under classical assumptions, LAD is related to an attenuation coefficient $k(\theta, \Phi)$, a clumping factor $\Omega$ and a ratio $G(\theta, \Phi)$ of foliage area projected in direction $(\theta, \Phi)$ to actual area by the following equation:

$$k(\theta, \Phi) = G(\theta, \Phi) \cdot \Omega \cdot \text{LAD}.$$
On the other hand, $k(\theta, \Phi)$ is related to the observed free path lengths ($l_i$)$_{1\le i\le n}$ by the likelihood function

$$L(\theta, \Phi) = \prod_{i=1}^{n} k(\theta, \Phi)^{S_i h_i} e^{-k(\theta, \Phi)S_i l_i}$$

where $S_i$ is the incoming beam section and $h_i$ is a binary variable indicating absence of hits.

Using both equations, LAD estimation is achieved by estimating $\Omega$, specifying a model for $G(\theta, \Phi)$ with a normalization ensuring identification of LAD, and estimating its parameters by likelihood maximization. Moreover, $G$ is determined by the leaf angle distribution $p(\theta_{L,j}, \Phi_{L,j})$ at leaf $j$, which in the end is the main focus of statistical modelling. State-of-the-art models assume independence of $\theta_L$ and $\Phi_L$, uniformly distributed $\Phi_L$ and i.i.d. $\theta_{L,j}$ (see Pisek et al., 2013 and Pimont et al., 2018). However, it is expected that within tree crowns, $(\theta_{L,j})_j$ are spatially clustered into aggregated patches. This is why we aim at taking into account spatial aggregation and dependencies by incorporating the distribution of $\theta_L$ within a BNP-MRF model operating at voxel scale, thus representing spatial dependencies of leaf orientation between contiguous voxels.
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# A. Curriculum Vitæ

## DETAILS

<table><tr><td>Jean-Baptiste DURAND</td><td></td></tr><tr><td>Date and place of birth</td><td>January 16<sup>th</sup>, 1976 in Grenoble, France</td></tr><tr><td>Nationality</td><td>French</td></tr><tr><td>Personal Address</td><td>7 rue André Chénier,<br/>38 400 Saint Martin d'Hères</td></tr><tr><td>Professional Address</td><td>Laboratoire Jean Kuntzmann<br/>CS 40 700 – 38058 Grenoble cedex 9<br/>France</td></tr><tr><td>Phone</td><td>+33 (0)4 57 42 17 33</td></tr><tr><td>Email</td><td>Jean-Baptiste.Durand@univ-grenoble-alpes.fr</td></tr><tr><td>Webpage</td><td>http://mistis.inrialpes.fr/people/jbdurand/</td></tr></table>

## EDUCATION AND QUALIFICATIONS

* 1999-2002: **PhD** in Applied Mathematics, Université Grenoble I (Joseph Fourier), France. Title: "Hidden Markov models: inference, model selection and applications" under the supervision of G. Celeux, INRIA Rhône-Alpes.

* 1998-1999: **DEA (MSc)** in Applied Mathematics at Université de Grenoble I. Subfield: Statistics and Probability.

* 1996-1999: **Engineering degree at ENSIMAG**, école Nationale Supérieure d'Informatique et de Mathématiques Appliquées de Grenoble. Option: Decision Mathematics and Economy / Statistics and Probability, with honours.
# WORK EXPERIENCE

* 2019-2020: detached to Inria, Rhône-Alpes, Mistis research team.

* 2004-today: Assistant Professor at Ensimag, Grenoble INP. Member of Laboratoire Jean Kuntzmann, Inria Mistis research team.

* 2009-2011: detached to Inria Sophia-Antipolis, Virtual Plants team in Montpellier.

* 2003-2004: Post-doctoral fellow at AMAP laboratory (botanique et bioinformatique de l'architecture des plantes) in Montpellier.

* Working part-time (80 %) in 2008-2009, 2012-2013, 2014-2015.

# CO-SUPERVISION

## POST-DOCTORAL FELLOWS

* 2013-2014: R. Azaïs (Inria). Multitype branching processes and applications to lossy compression and clustering of trees. R. Azaïs is now a researcher at Inria (Mosaic team).

## PHD STUDENTS

* 2015-2019: B. Olivier (Université Grenoble Alpes). Topic: "Joint analysis of eye movements and EEGs using hidden Markov models". Cosupervision with M. Clausel (10%) and Pr. A. Guérin-Dugué (40%), defended on June 26th 2019. I obtained a derogation from the doctoral school to be the official supervisor. B. Olivier is employed by MeteoSwift in Grenoble.

* 2011-2014: P. Fernique (Université de Montpellier 2). Topic: "A framework for statistical modelling of tree-structured data – Applications to plant growth modelling at microscopic and macroscopic scales". Cosupervision with Y. Guédon, defended in December 2014. P. Fernique is now the head of a project in biostatistics at Limagrain, Clermont-Ferrand.

* 2007-2010: L. Donini (Université de Grenoble 1). Topic: "Statistical learning for structured multivariate data. Application to the optimization of printer networks". Industrial PhD with the Xerox Company, cosupervised with S. Girard (Inria, 25%), V. Ciriza and G. Bouchard (XRCE, 25% each). L. Donini did not defend the PhD and quit after the end of the 3rd year in favour of a position into another company.
## PHD ORAL EXAMINATIONS

* November 17th, 2016: B. Henry (Université de Lorraine). Supervised by N. Champagnat and D. Ritchie. Topic: "Non-Markovian branching processes in population dynamics and genetics".

## PHD COMMITTEES

* 2014-2015: M. Garbez (Agrocampus Ouest, Centre d'Angers, industrial PhD). Topic: "Modelling the architecture and visual components of rose trees" (defended on November 23rd, 2016).

* 2011-2012: J. Peyhardi (Université de Montpellier 2). Topic: "A new generalized linear model (GLM) framework for analysing categorical data; application to plant structure and development" (defended on December 9th, 2013)

## SUPERVISION OF MASTER STUDENTS

* 2020: J. Li (Master 2 MSIAM, Université Grenoble Alpes and Grenoble INP). Topic: "Bayesian nonparametric models for hidden Markov random fields with medical applications". Cosupervision with S. Achard and F. Forbes (Inria Mistis).

* 2019: F. Dama (Master 2 MSIAM, Université Grenoble Alpes and Grenoble INP). Topic: "Bayesian non-parametric models for hidden Markov random fields on count variables: application to disease mapping". Cosupervision with F. Forbes and J. Arbel (Inria Mistis).

* 2015: L. Liu (Master 2 MSIAM, Université Grenoble Alpes and Grenoble INP). Topic: "Coupled hidden Markov models for joint analysis of eye movements and EEGs". Cosupervision with S. Achard and A. Guérin-Dugué (GIPSA-lab).

* 2011: P. Fernique (Master 2 in Bioinformatics, Université de Montpellier 2). Topic: "Modelling branching properties of plants with multitype branching processes".

* 2010: J. Peyhardi (Master 2 Ingénierie Mathématiques Statistiques et économie, sub-field Statistique et Fiabilité, Université de Bordeaux 2). Topic: "Markov models with generalized linear mixed transition kernels".
# SYNTHESIS OF RESEARCH, TEACHING AND OTHER ACTIVITIES

## Research

My career has been guided by two main directions of research: hidden Markov models and applications to plant structure analysis. The precise contributions obtained in these fields are provided in the main body of this document; however in what follows, an overview of my involvement in some formal research projects is provided.

As I left the AMAP laboratory in 2004 and joined the University of Grenoble, my collaborators and I had the opportunity to answer a call named ACI from the Ministry of Research. We gathered researchers from the plant science community in Montpellier, P. Ferraro who was issued from this community and then appointed as a teaching assistant in LaBRI (Bordeaux) and newcomers in applied probability and statistics from the University of Lyon. The topic of the project was the development of new methods in statistics and combinatorics dedicated to the analysis of tree-structured data, with a main focus on multitype branching processes (on the stochastic side) and edit distance computations (on the deterministic side), with their applications on plant architecture modelling. The project lasted from 2004 to 2007 and regarding the first axis, we applied hidden Markov tree (HMT) models to new data sets: Beech trees and *Symphonia Globulifera*. From a methodological point of view, the focus was on the one hand on combining HMTs and edit distances to obtain biologically relevant comparisons of tree structures and on the other hand, on validating our models by defining new statistical indices on trees, determining their distributions under HMT assumptions and assessing the fit between data and these distributions.

Then I had a professional mission at Inria in 2009. The national system of calls for project changed and was renamed “ANR calls”; P. Ferraro wrote a project with the same core of partners as the ACI project, now extending targeted applications of tree analysis (e.g., to RNA data secondary structure). The project was not funded and I was in charge of writing a new project, focused this time on the analysis of tree-structured data issued from plant phenotyping. The scope of the project was to develop a similar set of models and algorithms to analyse these data as what was developed in the case of sequence analysis issued from omics: segmentation, alignment and comparison of DNA and RNA sequences. From a cognitive point of view, the aim was to separate different components of plant growth (which are detailed in Subsection 5.1). I thus coordinated the writing of a 15-pages document involving three laboratories in applied mathematics or plant biology. The project was not funded and was extended in 2010 to involve an additional partner: the “Institut de Recherche en Horticulture et Semences” laboratory in Angers, whose interest in our project was mainly focused on modelling rose bushes. I visited the lab to present our project and collect the needs for structured data analysis of their researchers. Once again the project was not funded but we could recycle it in a call from University of Montpellier 2. Formally the project (entitled as *Modelling architectural plasticity of plants: effect of genetic factors in response to environmental stress*) was headed by C. Trottier, who was with the University. In 2009, we co-supervised J. Peyhardi’s master thesis in Montpellier; he then went into a PhD thesis co-supervised by C. Trottier and Y. Guédon. In 2010 I supervised P. Fernique’s master thesis and our project yielded 50% in
his fundings for future PhD studies. His work, although focused on applications in plant
growth modelling, was actually quite broad in terms of statistical models since it addressed the
issue of identifying dependencies in multivariate counts. During the same year, I participated
in recruiting a post-doctoral fellow at Inria, R. Azaïs and supervised him with C. Godin.
During this 2-years period, I also took part to the presentation of a stand for two days at
“Palais de la Découverte” in Paris. The topic was “From genes to flowers” and the exhibition,
headed by C. Godin, was advertised on http://www.palais-decouverte.fr/fr/ressources/
docs-1chercheur1manip/modelisation-de-la-croissance-des-plantes/.

In 2013, I initiated some discussions with an occasional teacher in statistics at Ensimag, I.
Joly, on our research activities. As a researcher in transportation economics, he was inter-
ested in new models accounting for the choices of modes (or durations) of transport for the
members of families, together with the associated activities requiring transport. More specif-
ically, the aim was to predict and explain the dependencies within family members as well as
regarding (possibly shared) resources (car, bicycles, season tickets). We answered a call for
research projects from University of Grenoble 2 entitled as *Statistical modelling of multivariate
counts. Applications to the analysis of transportation habits*. We involved other members of
my research team, among whom M.-J. Martinez who formally headed the project since she was
with University of Grenoble 2, while I was in charge of writing and coordinating the project.
J. Peyhardi joined the project, given his interest for count data and economics. The funding
was mainly dedicated to a few travels, material and for organizing a workshop on statistics
and transports.

During the same period, I started preliminary analyses with A. Guérin-Dugué (Gipsa-Lab,
Grenoble) on eye-movement experiments. We focused on scanpath modelling using HMC mod-
els (using our Matlab library *Chainxem*) and since HMCs revealed themselves unable to provide
meaningful segmentations, we used HSMC models (using the Sequence Analysis component
of the OpenAlea<fnref n="11" /> project). However, these were not appropriate for joint modelling of eye
movements and EEGs and we thus identified needs for new, generic models for analysing cou-
pled, heterogeneous processes with asynchronous regime switches. It became manifest that
researchers from four laboratories in Grenoble shared common interests and needs for stochas-
tic models to analyse eye movements, though in different contexts (free exploration of images
or texts, colour vision models, comparison of image exploration between children and adults)
or scales (micro- vs. macro-saccades). We thus submitted a multidisciplinary project called
*Oculo-Nimbus*<fnref n="12" /> to a university call. Originally J.-F. Cœurjolly was the corresponding member
for LJK but he went on a work leave program to Canada and I took the responsibility of repre-
senting LJK to present our project (often with A. Guérin-Dugué) to institutional meetings and
to make periodic reports on project advances. *Oculo-Nimbus* provided funding for B. Olivier’s
PhD thesis and other post-doctoral or PhD fellowships involving the other axes only. This also
funded a workshop on eye-movement analysis we organized in Grenoble in 2018.

While I was supervising J. Peyhardi's master thesis in Montpellier, E. Costes (AGAP laboratory, Montpellier) proposed that together with C. Trottier and Y. Guédon we took part to a project on joint modelling of growth and alternation in flowering in apple trees. In 2010, E.

<footnote n="11">¹¹https://github.com/openalea</footnote>

<footnote n="12">¹²https://persyval-lab.org/fr/sites/oculo-nimbus</footnote>
Costes imagined an extension of this work, focusing on a reduction of the phenotyping work-
load required to assess yearly numbers of flowers. We co-supervised Y. Holtz’s master thesis
on that topic and the combination of these new methods and results led to a publication in
the *Journal of Experimental Botany* (Durand et al., 2013a). Now we could extend measure-
ments to new families of apple trees and take benefit from both increased sample sizes, genetic
diversity and links between families to enhance statistical detection of QTLs; this was at the
core of A. Allard’s PhD thesis and led to a publication in *Frontiers in Plant Science* (Durand
et al., 2017). These studies demonstrated the existence of genetic determinisms in alternation,
supporting the assumption of hormonal control on floral induction involving developing fruits.
But the role of nutritional competition between reproductive and vegetative growth should also
be considered. From 2015 to 2019, I took part to a French-German ANR research project called
AlternApp, *Genetic mechanisms underlying alternate cropping in apple (Malus x domestica)*<fnref n="13" />,
which focused on examining these two assumptions by genetic and genomics approaches. I
was in charge of applying and extending our statistical methodology to new data sets and
phenotyping protocols.

Teaching

When I became appointed as a teaching assistant at Ensimag in 2004, I was in charge of
creating a new 36H course in statistical learning. I was totally free to decide the contents and
chose to teach neural networks, probabilistic graphical models, mixture and hidden Markov
models, computational statistics for Bayesian models and model selection. These topics had
the advantages of being useful for engineers and to belong to research fields. The course
contained about one half practical work. This course received the best ratings by the students
among all the courses I ever gave; unfortunately the contents of the 3-years training program of
engineers changed the year after and since this course could not fit in the schedules any more,
it was discarded. I also succeeded some colleagues in introductory courses in statistics and
multivariate analysis. I contributed to other courses in probability, proposed and supervised
some student projects.

From 2006 to 2008 I coordinated “Information and Communication Technologies in Educa-
tion” at Ensimag, together with F. Hetroy in computer science. This globally consists in ped-
agogic and technology watch, especially but not exclusively in new technologies for education.
We particularly focused on problem- and project-based learning and went in Louvain-La-Neuve
for a week to follow a training program. Then I applied these principles in my course in multi-
variate statistics and promoted the method at Ensimag. I also trained colleagues in statistics to
multivariate analysis using the R software and headed two projects. The first one is an online
French-English dictionary of mathematical concepts with their pronunciations (including audio
recordings). The second one is a platform to organize student projects satisfying hierarchical
constraints in nested groups, including forming teams, scheduling defences and gathering de-
liverables. The latter project was funded by a competitive university call; I thus wrote the
project including its specification and hired a technician to develop the platform.

<footnote n="13">¹³https://umr-agap.cirad.fr/recherche/projets-de-recherche/alternap</footnote>
In 2004, O. Gaudoin and I decided to propose training sessions in the continuing education department of the university (35H per year). We thus built industry-oriented training programs in data analysis, or introductory courses in statistics and probability. This lasted about three years, until the global load of the teachers became too heavy regarding conventional training and we had no time to ensure the sessions any longer.

From 2006 to 2009 I was in charge of coordinating 5-weeks full-time projects in probability, statistics and finance for teams of second-year students (call to projects, manage student teams, enforce rules and schedules, gather reports, organise and attend defences).

From 2014 to 2019, I was the head of the teaching staff in probability, statistics and finance at Ensimag. This mainly consists in checking the consistency of courses and training programs, the application of reforms decided by our university, committee of accreditation or ministry, compute the total teaching load of the staff, check the repartition between teachers and ensure that our topics remain visible and promoted in the various training pathways of the school. I had also to watch evolutions in educational programs at bachelor level, so that our pre-requisites and refresher courses remained adequate.

In 2014, Grenoble INP Ensimag and Grenoble Ecole de Management offered a new training program dedicated to big data ¹⁴and I was in charge of defining its contents in machine learning.

In 2014 and 2015, I was a trainer in training sessions in Python for higher school preparatory teachers (introduction to scientific libraries).

Around 2013-2014, the number of research master's students in applied mathematics had fallen low, partly due to lack of possibilities for our Ensimag students to join the programs. I contributed in conceiving new programs in data science (now accessible to Ensimag students too). These were shared by two universities and two departments, Applied Mathematics (MSIAM<fnref n="15" />) and Computer Science. From 2015 to 2019, I was the head of the Statistics and Data Science track (with the help of O. Gaudoin in 2015). This occupation was particularly time-demanding. The co-accreditation by two universities and departments was a source for numerous administrative complications. The number of students increased regularly, from 12 in 2015 to 50 in 2019. This position involved assessment of application files, information and selection of students regarding fellowships, collecting and validating students' choices for optional courses, maintaining multiple university webpages, organizing committees to improve the training program, creating partnerships with other universities (particularly MIPT Moscow). Some shared courses had more than 100 students. Part of the courses were shared with other master programs (signal processing, operation research). The complexity was such that I spent more than 300 hours per year just in organization, with sometime deficient support of administrative staff (actually no support at all during several months). The heads of the shared tracks and master program agreed on the relevance of sharing more courses: modelling activities, data competitions, but there was some lack of volunteering teachers so I had to get personally involved and in the end, was in charge of four different courses in the master, which added to the Ensimag courses. In 2019, we were the first French program to obtain an ECMI (European Consortium for Mathematics in Industry<fnref n="16" />) accreditation. We published the principles

<footnote n="14">¹⁴https://ensimag.grenoble-inp.fr/fr/formation/big-data-analyse-management-et-valorisation-responsable</footnote>

<footnote n="15">¹⁵http://msiam.imag.fr/</footnote>

<footnote n="16">¹⁶https://ecmiindmath.org/</footnote>
of a training program in data science in the journal *Statistique et Enseignement* (Amini *et al.*, 2016) and published the scientific contents in a book: *Data Science. Cours and exercices. Eyrolles* (éd.), 2018.

Since 2016, I have been in charge of a training project on data competitions. The aim is to organise and promote competitions in data analysis as training activities at the scale of the joint universities in Grenoble. The project includes a platform dedicated to hosting and running data competitions and a multimodal classroom to favour team working. The classroom includes clusters of tables, plugs and screens with mobile chairs and shelves, with shareable large screens. The project was presented in conferences (Durand 2017, 2019), one of which was in Grenoble; I was part of the organising committee.

The teaching load regularly increased from 2004 (192H) to 2018 (242H), with a pike in 2016 (263H), not mentioning unrewarded hours.

## Other responsibilities

From 2011 to 2017, I was in charge of organising the seminars in Applied Probability and Statistics at LJK. This is the weekly seminar of our department.

From 2008 to 2013, I was the Health and Safety Manager at LJK. During the first year, this mainly consisted in transmitting information regarding occasional intrusions and damages caused to the building. However after one year the task was much more demanding since the university required the list of every room in the building with an inventory of each possible risk (in a very broad sense: having one's feet tangled in wire, the use of chained multi sockets or keeping stacks of paper, etc.).

## Organizing committees at workshops and conferences

* Grenoble Workshop on Models and Analysis of Eye Movements, Université de Grenoble, June 6–8 2018.

* CFIES'2017 (Colloque Francophone International sur l'Enseignement de la Statistique), Université de Grenoble, September 6–8 2017.

* Workshop of AIGM network (Algorithmic Issues for inference in Graphical Models): 2011 session in Montpellier and 2015 session in Grenoble.

## Selection committee for assistant professor positions

* 2018: Grenoble INP / Ensimag (position n. 0664, sections 26-27 Applied Mathematics and Computer Science)
Reviewing activity for the following journals:

* Statistics and Computing

* Behavior Research Methods

* PLOS Computational Biology (invited editor)

* Journal of Mathematical Biology

* Signal Processing Letters

* International Journal of Wavelets, Multiresolution and Information Processing

* IEEE Transactions on Signal Processing

* IEEE Transactions on Image Processing

* Transactions on Reliability

* International Journal of Computational Materials Science and Engineering
# B. List of publications<fnref n="1" />

*Foreword related to authors' order in publications.*

Publications in Applied Mathematics usually sort authors by decreasing importance of contributions, or by alphabetical order in case of equal contributions.

Publications in Plant Science frequently place as last authors PhD supervisors or team leaders, even if their contribution is quite significant.

## Preprint

* P. Fernique, J. Peyhardi and J.-B. Durand. Multinomial distributions for the parametric modeling of multivariate count data. https://hal.inria.fr/hal-01286171

## Book chapter

* M. Clausel and J.-B. Durand. Modèles Génératifs. In *Data Science. Cours and exercices*. Eyrolles (éd.), p. 125-156, 2018.

## Peer-reviewed international journals

* M. Garbez, R. Symoneaux, É. Belin, Y. Caraglio, Y. Chéné, N. Dones, J.-B. Durand, G. Hunault, D. Relion, M. Sigogne, D. Rousseau and G. Galopin. Ornamental plants architectural characteristics in relation to visual sensory attributes: a new approach on the rose bush for objective evaluation of the visual quality. *European Journal of Horticultural Science* 83(3):187-201, 2018.

* J.-B. Durand, A. Allard, B. Guitton, E. Van de Weg, M. Bink and E. Costes. Predicting Flowering Behavior and Exploring Its Genetic Determinism in an Apple Multi-family Population Based on Statistical Indices and Simplified Phenotyping. *Frontiers in Plant Science* 8:858-872, 2017.

* W. Yang, B. Pallas, J.-B. Durand, S. Martinez, M. Han and E. Costes. The impact of long-term water stress on tree architecture and production is related to changes in transitions between vegetative and reproductive growth in the “Granny Smith” apple cultivar. *Tree Physiology*, 36(11):1369-1381, 2016.

<footnote n="1">¹Electronic version of documents available at http://mistis.inrialpes.fr/people/jbdurand/</footnote>
• J.-B. Durand and Y. Guédon. Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models. *Statistics and Computing*, 26(1):549-567, 2016.

• J.-B. Durand, B. Guitton, J. Peyhardi, Y. Holtz, Y. Guédon, C. Trottier and E. Costes. New insights for estimating the genetic value of segregating apple progenies for irregular bearing during the first years of tree production. *Journal of Experimental Botany*, 64:5099-5113, 2013(a).

• J.-B. Durand, S. Girard, V. Ciriza and L. Donini. Optimization of power consumption and device availability based on point process modelling of the request sequence. *Journal of the Royal Statistical Society Series C*, 62(2):151-162, 2013(b).

• C. Hatt, F. Mankessi, J.-B. Durand, F. Boudon, F. Montes, M. Lartaud, J.-L. Verdeil and O. Monteeuis. Characteristics of Acacia mangium shoot apical meristems in natural and in vitro conditions in relation to heteroblasty. *Trees - Structure and Function*, 26(3):1031-1044, 2012.

• J.-B. Durand, Y. Guédon, Y. Caraglio and E. Costes. Analysis of the Plant Architecture via Tree-structured Statistical Models: the Hidden Markov Tree Models. *New Phytologist*, 166(3): 813-825, 2005.

• J.-B. Durand and O. Gaudoin. Software reliability modelling and prediction with hidden Markov chains. *Statistical Modelling - An International Journal*, 5(1):75-93, 2005.

• G. Celeux and J.-B. Durand. Selecting Hidden Markov Model State Number with Cross-Validated Likelihood. *Computational Statistics*, 23(4):541-564, 2008.

• J.-B. Durand, P. Gonçalvès and Y. Guédon. Computational Methods for Hidden Markov Tree Models – An Application to Wavelet Trees. *IEEE Transactions on Signal Processing*, 52(9):2551-2560, 2004a.
## Peer-reviewed journals with national audience

* M.-R. Amini, J.-B. Durand, O. Gaudoin, E. Gaussier and A. Iouditski. Data Science : une formation internationale de niveau Master en science des données. *Statistique and Enseignement*, Société Française de Statistique, 7(1):95-102, 2016.

* J.-B. Durand, L. Bozzi, G. Celeux and C. Derquenne. Analyse de courbes de consommation électrique par chaînes de Markov cachées. *Revue de Statistique Appliquée*, LII(4):71-91, 2004b.

## Peered-reviewed international conferences

* R. Azaïs, J.-B. Durand and C. Godin. Approximation of trees by self-nested trees. In *Proceedings of the 21st Meeting on Algorithm Engineering and Experiments (ALENEX 2019)*, 7-8 January 2019, pp.39-53. San Diego (USA).

* B. Olivier, J.-B. Durand, A. Guérin-Dugué and M. Clausel. Eye-tracking data analysis using hidden semi-Markovian models to identify and characterize reading strategies. In *Proceedings of the 19th European Conference on Eye Movements (ECEM2017)*, 20-24 August 2017. Wuppertal (Germany).

* J.-B. Durand, A. Allard, B. Guitton, E. Van de Weg, M. Bink and E. Costes. Genetic determinism of flowering regularity over years in an apple multi-family population. In *International Symposium on Flowering, Fruit Set and Alternate Bearing*, 19-23 June 2017. Palerme (Italy).

* B. Pallas, J. Ngao, J.-B. Durand, S. Martinez, S. Bluy, J.-J. Kelner and E. Costes. The Analysis of the Impact of Carbon Source-sink Relationships on Flowering Patterns Reveals That Apple Tree Growth and Functioning are Determined by Mechanisms Occurring at the Tree and Shoot Scales. In *Acta Horticulturae, Proceedings of the XI International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems*, 28 August-2 September 2016. Bologna (Italy).

* P. Fernique, A. Dambreville, J.-B. Durand, C. Pradal, P.-É. Lauri, F. Normand and Y. Guédon. Characterization of mango tree patchiness using a tree-segmentation/clustering approach. In *Proceedings of the International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA2016)*, 7-11 November 2016. Qingdao (China).
* R. Azaïs, J.-B. Durand and C. Godin. Lossy compression of unordered rooted trees. In *Data Compression Conference DCC2016*, 29 March-1st April 2016. Cliff Lodge, Snowbird, Utah (USA).

* J.-B. Durand and Y. Guédon. Quantifying and localizing state uncertainty in hidden Markov models using conditional entropy profiles. In *Compstat2014*, 19-22 August 2014. Geneva (Switzerland).

* P. Fernique, J.-B. Durand and Y. Guédon. Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes. In *Compstat2014*, 19-22 August 2014. Geneva (Switzerland).

* A. Dambreville, P. Fernique, C. Pradal, P.-É. Lauri, F. Normand, Y. Guédon and J.-B. Durand. Deciphering mango tree asynchronisms using Markov tree and probabilistic graphical models. In *Proceedings of the Seventh International Workshop on Functional-Structural Plant Models (FSPM2013)*, 9-14 June 2013(c). Saariselkä (Finland).

* J.-B. Durand, B. Guitton, J. Peyhardi, Y. Holtz, Y. Guédon, C. Trottier and E. Costes. Estimating the genetic value of F1 apple progenies for irregular bearing during first years of production. In *Proceedings of the Seventh International Workshop on Functional-Structural Plant Models (FSPM2013)*, 9-14 June 2013(d). Saariselkä (Finland).

* C. Preuksakarn, F. Boudon, P. Ferraro, J.-B. Durand, E. Nikinmaa and C. Godin. Reconstructing Plant Architecture from 3D Laser scanner data. In *Proceedings of the Sixth International Workshop on Functional-Structural Plant Models (FSPM10)*, 12-17 September 2010. University of California, Davis (USA).

* J.-B. Durand, Y. Caraglio, P. Heuret and E. Nicolini. Segmentation-based approaches for characterising plant architecture and assessing its plasticity at different scales. In *Proceedings of the Fifth International Workshop on Functional-Structural Plant Models (FSPM07)*, 4-9 November 2007. Napier (New-Zealand).

* P. Heuret, J.-B. Durand, E. Nicolini, S. Coste and Y. Caraglio. Exploring morphogenetical gradients plasticity using hidden Markov tree models in young individuals of the tropical specie *Symphonia globulifera* (Clusiaceae). In *Proceedings of the Fifth International Workshop on Functional-Structural Plant Models (FSPM07)*, 4-9 November 2007. Napier (New-Zealand).
* S. Dufour-Kowalski, C. Pradal, N. Dones, P. Barbier de Reuille, F. Boudon, J. Chopard, D. DaSilva, J.B Durand, F. Theveny, P. Ferraro, C. Fournier, Y. Guedon, C. Smith, S. Stoma, C. Godin and H. Sinoquet. OpenAlea: An open-software plateform for the integration of heterogenous FSPM components In *Proceedings of the Fifth International Workshop on Functional-Structural Plant Models (FSPM07)*, 4-9 November 2007. Napier (New-Zealand).

* C. Pradal, F. Boudon, N. Dones, J.-B. Durand, P. Barbier de Reuille, C. Fournier, H. Sinoquet and C. Godin. OpenAlea - A platform for plant modelling, analysis and simulation. In *Europython conference*, 3-6 juillet 2006. Geneva (Switzerland).

* J.-B. Durand, Y. Guédon, Y. Caraglio and E. Costes. Analysis of the Plant Architecture via Tree-structured Statistical Models: the Hidden Markov Trees In *Proceedings of the Fourth International Workshop on Functional-Structural Plant Models (FSPM04)*, édité by Godin *et al*., UMR AMAP publisher, 7-11 June 2004, p. 61-64. Montpellier (France).

* J.-B. Durand and O. Gaudoin. Software reliability modelling and assessment with hidden Markov chains In *4th International Conference on Mathematical Methods in Reliability*, World Scientific Publishing publisher, Series on Quality, Reliability and Engineering Statistics, June 2004. Santa-Fe (USA).

* G. Celeux and J.-B. Durand. Choosing the order of a hidden Markov chain through cross-validated likelihood. In *Compstat2002*, 24-28 August 2002. Berlin (Germany).

* J. Martin and J.-B. Durand. Automatic Handwriting Gestures Recognition using Hidden Markov Models. In *Proceedings of the Fourth IEEE International Conference on Face and Gesture Recognition (FG2000)*, IEEE Press, New Jersey, Piscataway, 28-30 March 2000, pp. 403–409. Grenoble (France).

* K. Schwerdt, J.L. Crowley and J.-B. Durand. Robustification of detection and tracking of faces. In *Joint TMR Workshop on Computer Vision and Mobile Robotics*, September 1998, pp. 155–161. Santorini (Greece).

9 Communications at “Journées de statistique”<fnref n="17" /> (not detailed)

<footnote n="17">¹⁷French-speaking conference</footnote>
## Other communications

* J.-B. Durand. Compétitions d'analyse des données à l'Université Grenoble Alpes: motivations, organisation et retours d'expérience. In *Colloque Francophone International sur l'Enseignement de la Statistique (CFIES2019)*, 25-27 September 2019. Strasbourg (France).

* J.-B. Durand. Challenges d'analyse de données : une formation par la pratique transversale and multidisciplinaire en science des données. In *Colloque Francophone International sur l'Enseignement de la Statistique (CFIES2017)*, 6-8 September 2017. Grenoble (France).

* J.-B. Durand, A. Guérin-Dugué and B. Lemaire. Analysis of eye movements and EEGs in reading tasks. In *Workshop on eye-movement analysis*, GIPSA-LAB, October 2015. Grenoble (France).

* J.-B. Durand and P. Fernique. Approche graphique pour la modélisation statistique de la dépendance entre activités journalières. In *Workshop "Statistique, Transport and Activities"*, Laboratoire Jean Kuntzmann, November 2013(e). Grenoble (France).

* P. Fernique, J.-B. Durand and Y. Guédon. Parametric modelling of multivariate count data using probabilistic graphical models. In *3rd Workshop on Algorithmic issues for Inference in Graphical Models – AIGM13*, September 2013(f). Paris (France).

* J.-B. Durand. Statistical models of sequences and trees in OpenAlea. In *First OpenAlea workshop on Functional-Structural Plant Modelling*, 14-15 June 2011. Montpellier (France).

* L. Donini, V. Ciriza, J.-B. Durand and S. Girard. A Statistical Model for Optimizing Power Consumption of Printers. In *XIG R&T Conference*, Xerox Corporation, May 2008. Webster (USA).