Starting January 2024, I will be Research Director within the MathNum division of INRAE in the Biostatistics and Spatial Processes unit in Avignon.
Contact
Mail: thomas POINT opitz AT inrae POINT fr
Telephone: 04 32 72 21 87
Postal address: INRAE-BioSP – Domaine St. Paul – 228, route de l'Aérodrome – 84914 Avignon – France
Research interests
- Theory and statistical learning for multivariate, spatial and spatiotemporal extremes
- Stochastic generators for spatial and spatiotemporal extreme events
- Spatiotemporal risk modeling with Bayesian hierarchical models combining data over various supports and with various observation biases (e.g. Citizen Science programs)
- Applications to climatic, environmental, ecological and epidemiological risks
- Weather and climate extremes
- Wildfires
- Landslides
- Species distributions
- Climate change impacts
PhD and Postdoc supervisions (past and present)
- Chen Yan (Postdoc 2023-2024), Multivariate analysis of extreme events, with applications to multiple risks under climate change. Joint supervision with Stéphane Girard (Statify, Inria), Renaud Barbero (RECOVER, INRAE) and Antoine Usseglio-Carleve (LMA, Avignon University)
- Juliette Legrand (Postdoc, 2022-2023), New methods for modeling fire regimes and extremes of wildfires in Europe. Joint supervision with Jean-Luc Dupuy and François Pimont (URFM, INRAE) within the European Green New Deal project FIRE-RES
- Chloé Serre-Combe (PhD, 2022-2025), Spatiotemporal stochastic generators of extreme precipitation and flood risk assessment in urban environments. Joint supervision with Gwladys Toulemonde and Nicolas Meyer (LEMON, Inria Montpellier and IMAG, Montpellier University)
- Ryan Cotsakis (PhD, 2021-2024), Stochastic geometry tools for space-time extremes. Joint supervision with Elena di Bernardino (3IA Côte d'Azur, Université de Nice)
- Florian Lasgorceux (PhD, 2021-2024), Space-time modeling of species distributions in protected areas using opportunistic data. Joint supervision with Julien Papaix (BioSP, INRAE) and Parc National des Écrins
- Jorge Castel-Clavera (PhD, 2021-2024), Towards improved spatiotemporal wildfire danger indices. Joint supervision with François Pimont, Jean-Luc Dupuy (URFM, INRAE)
- Patrizia Zamberletti (PhD, 2018-2021), Simulation and inference of agricultural landscapes using stochastic geometry; agroecological analysis of numerical simulations of spatially explicit population dynamics model. Joint supervision with Julien Papaix, Edith Gabriel (BioSP, INRAE)
- Fátima Palacíos-Rodriguez (Post-doc) Semiparametric resampling of extreme events over space and time, with an application to precipitation data, and with a view towards extreme risk measures. Joint supervision with Julien Carreau, Gwladys Toulemonde (Montpellier Université)
Projects
- ANR EXSTA (EXtremes, STatistical learning and Applications) 2023-2027 (I am one of its three scientific/geographic coordinators, together with Gilles Stupfler and the leading coordinator Anne Sabourin)
- Member of the Chair of Geolearning
- Joint Inria-INRAE project ANOVEX (2022-2024): Analysis of variability in extremes
- EU Innovation Act 2021-2025, FIRE-RES: Innovative Technologies and Socio-Ecological-Economic Solutions for FIRE RESilient Territories in Europe
- Co-Investigator of a KAUST Competitive Research Grant (2018-2021), Statistical Estimation and Detection of Extreme Hot Spots, with Environmental and Ecological Applications
- LEFE-CERISE, LEFE-FRAISE projects (2016-2021) funded by INSU, Simulation de scénarii intégrant des champs extrêmes spatio-temporelle avec éventuelle indépendance asymptotique pour des études d'impact en science de l'environnement
Distinction
Young Researcher Award of INRAE in 2020 ("Laurier Espoir Scientifique"): General presentation and my portrait
Responsibilities
- Steering committee member of CLIMAE, INRAE's MetaProgramme for bringing together climate change adaptation and mitigation
- Coordinator of RESSTE ("RESeau Statistique pour données Spatio-TEmporelles"), INRAE's research network for spatiotemporal statistics
- Elected member and President of Groupe Environnement et Statistique of the French Statistical Society
- Associate Editor of Extremes
Teaching
- Since 2020: Course "Introduction to extreme-value analysis" at École Centrale Marseille, Master Climaths
- Since 2019: One-day Master Course on Multivariate Extremes within the European ATHENS network, MinesParisTech
- 2018-2020: Course "Statistique spatiale et écologie", M2 Data Science, Marseille
Preprints
- Koh, J., Opitz, T. Extreme-value modelling of migratory bird arrival dates: Insights from citizen-science data. Link to arXiv preprint.
- Legrand, J., Pimont, F., Dupuy, J.-L., Opitz, T. Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change – A step-by-step marked point process approach using INLA-SPDE. Link to HAL preprint.
- Cotsakis, R., di Bernardino, E., Opitz, T. A local statistic for the spatial extent of extreme threshold exceedances. Link to arXiv preprint.
- Bacro, J.-N., Gaetan, C., Opitz, T., Toulemonde, G. Multivariate peaks-over-threshold with latent variable representations of generalized Pareto vectors.
- Girard, S., Opitz, T., Usseglio-Carleve, A. Analysis of variability for heavy-tailed extremes. Link to HAL preprint.
- Zhong, P., Brunner, M., Opitz, T., Huser, R. Spatial modeling and future projection of extreme precipitation extents. [Link to arXiv preprint]
- Gong, Y., Zhong, P., Opitz, T., Huser, R. Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning. arXiv preprint arXiv:2210.07351.
- Di Bernardino, E., Estrade, A., Opitz, T. Spatial extremes and stochastic geometry for Gaussian-based peaks-over-threshold processes. HAL preprint.
- Opitz, T. Spatial random field models based on Lévy indicator convolutions. Link to arXiv preprint.
Publications
- Yadav, R., Huser, R., Opitz, T., Lombardo, L. Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions. JRSS C (Accepted). [arXiv preprint]
- Cotsakis, R., di Bernardino, E., Opitz, T. (2023+) On the perimeter estimation of pixelated excursion sets of 2D anisotropic random fields. Scandinavian Journal of Statistics (Accepted). [HAL preprint]
- Belzile, L., Dutang, C., Northrop P. J., Opitz, T. (2023). A modeler’s guide to extreme value software. Extremes (Accepted).
- Simpson, E., Opitz, T. and Wadsworth J. L. High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and the SPDE approach. Extremes (Accepted). [Link to arXiv preprint]
- Castel-Clavera, J., Pimont, F., Opitz, T., Ruffault, J., Dupuy, J.-L. (2022+) Disentangling the factors of spatio-temporal patterns of wildfire activity in South-eastern France. International Journal of Wildland Fire (Accepted).
- Pimont, F. et al. (2022+) Expansion, lengthening and intensification of fire activities under climate change in Southeastern France. International Journal of Wildland Fire. Accepted. Link to open full text.
- Song et al (2022). Spatio-temporal variation and dynamic scenario simulation of ecological risk in a typical artificial oasis in northwestern China. Journal of Cleaner Production,133302.
- Koh, J., Pimont, F., Dupuy, J.-L., Opitz, T. Spatiotemporal wildfire modeling through point processes with moderate and extreme marks. Annals of Applied Statistics (Accepted) [arXiv preprint]
- Hu et al. (2022). Stoichiometry of soil carbon, nitrogen, and phosphorus in farmland soils in southern China: Spatial pattern and related dominates. CATENA, 217.
- Yadav, R., Huser, R., Opitz, T. A flexible Bayesian hierarchical modeling framework for spatial peaks-over-threshold data. Spatial Statistics (accepted) arXiv preprint arXiv:2112.09530.
- Rivière, M. et al. A Bioeconomic Projection of Climate-induced Wildfire Risk in the Forest Sector. Accepted for Earth's Future.
- Zhong, P., Huser, R. and Opitz, T. Exact Simulation of Max-Infinitely Divisible Processes. Accepted for Econometrics and Statistics. arXiv preprint arXiv:2103.00533.
- Zamberletti, P., Papaïx, J., Gabriel, É., Opitz, T. Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes. Ecography (In press). Link to bioRxiv preprint.
- Zhang, Z., Huser, R., Opitz, T., & Wadsworth, J. L. Modeling spatial extremes using normal mean-variance mixtures. Accepted for Extremes. arXiv preprint arXiv:2105.05314.
- Opitz, T., Bakka, H., Huser, R., & Lombardo, L. High-resolution Bayesian mapping of landslide hazard with unobserved trigger event. Accepted for Annals of Applied Statistics [Link to arXiv preprint].
- Zamberletti, P., Sabir, K., Opitz, T., Bonnefon, O., Gabriel, E., Papaïx, J. More pests but less treatments: ambivalent effect of landscape complexity on Conservation Biological Control. Accepted for PLOS Computational Biology. Link to bioRxiv preprint.
- Allard, D., Clarotto, L., Opitz, T., Romary, T. Discussion on “Competition on Spatial Statistics for Large Datasets”. JABES (2021). https://doi.org/10.1007/s13253-021-00462-2
- Allard, D., Hristopoulos, D. and Opitz, T. Linking Physics and Spatial Statistics: A New Family of Boltzmann-Gibbs Random Fields. Electronic Journal of Statistics (Accepted).
- Zhong, P., Huser, R. and Opitz, T. Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude. Annals of Applied Statistics (Accepted). Link to ArXiv preprint.
- Zamberletti, P., Papaïx, J., Gabriel, E., Opitz, T. Landscape allocation: stochastic generators and statistical inference. Accepted for Annals of Applied Statistics. Link to arXiv preprint.
- Yadav, R., Opitz, T. and Huser, R. ‘Spatial hierarchical modeling of threshold exceedances using rate mixtures’. Accepted for Environmetrics.
- Pimont, F. et al. (2021). Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood. Ecological applications.
- Grente, O. et al. 'Tirs dérogatoires de loups en France : état des connaissances et des enjeux pour la gestion des attaques aux troupeaux. To appear in 'Faune Sauvage.'
- Palacios-Rodriguez, F. et al. ‘Semi-parametric generalized Pareto processes for simulating space-time extreme events’. To appear in Stochastic Environmental Research and Risk Assessment.
- Castro-Camilo, D., Mhalla, L. and Opitz, T. ‘Bayesian space-time gap filling for inference on hot spots: an application to Red Sea surface temperatures’. To appear in Extremes.Link to arXiv preprint.
- Huser, R., Opitz, T. and Thibaud, E. (2020) ‘Max-infinitely divisible models and inference for spatial extremes’, To appear in Scandinavian Journal of Statistics. Link to arXiv preprint.
- Lombardo, L. et al. (2020). Space-Time Landslide Predictive Modelling. Earth Science Reviews. Link to arXiv preprint.
- Opitz, T., Allard, D. and Mariethoz, G. (2020) ‘Semi-parametric resampling with extremes’, Spatial Statistics. doi: 10.1016/j.spasta.2020.100445.
- Opitz, T., Bonneu, F. and Gabriel, E. (2020) ‘Point-process based modeling of space-time structures of forest fire occurrences in Mediterranean France’, Spatial Statistics, In press. doi: 10.1016/j.spasta.2020.100429.
- Bacro, J.-N. et al. (2019) ‘Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data’, Journal of the American Statistical Association. Taylor & Francis, 0(0), pp. 1–26. doi: 10.1080/01621459.2019.1617152.
- Engelke, S., Opitz, T. and Wadsworth, J. L. (2019) ‘Extremal dependence of random scale constructions’, Extremes.
- Lombardo, L., Opitz, T. and Huser, R. (2019) ‘Numerical Recipes for Landslide Spatial Prediction Using R-INLA: A Step-by-Step Tutorial’, in Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier, pp. 55–83.
- Mhalla, L., Opitz, T. and Chavez-Demoulin, V. (2019) ‘Exceedance-based nonlinear regression of tail dependence’, Extremes. Springer, pp. 1–30.
- Fargeon, H. et al. (2018) ‘Assessing the increase in wildfire occurrence with climate change and the uncertainties associated with this projection’, in 8th International conference on forest fire research.
- Lombardo, L., Opitz, T. and Huser, R. (2018) ‘Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster’, Stochastic environmental research and risk assessment. Springer, 32(7), pp. 2179–2198.
- Opitz, T. et al. (2018) ‘INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles’, Extremes. Springer, 21(3), pp. 441–462.
- Tapi Nzali, M. D. et al. (2018) ‘Reconciliation of patient/doctor vocabulary in a structured resource’, Health Informatics journal. SAGE Publications Sage UK: London, England.
- Gabriel, E., Opitz, T. and Bonneu, F. (2017) ‘Detecting and modeling multi-scale space-time structures: the case of wildfire occurrences’, Journal of the French Statistical Society (Special Issue on Space-Time Statistics).
- Huser, R., Opitz, T. and Thibaud, E. (2017) ‘Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures’, Spatial Statistics. Elsevier, 21, pp. 166–186.
- Mornet, A. et al. (2017) ‘Wind storm risk management: sensitivity of return period calculations and spread on the territory’, Stochastic Environmental Research and Risk Assessment. Springer, 31(8), pp. 1977–1995.
- Nzali, M. D. T. et al. (2017) ‘What patients can tell us: topic analysis for social media on breast cancer’, JMIR Medical Informatics. JMIR Publications Inc., 5(3).
- Opitz, T. (2017) ‘Latent Gaussian modeling and INLA: A review with focus on space-time applications’, Journal of the French Statistical Society (Special Issue on Space-Time Statistics), 158(3).
- Opitz, T. (2016) ‘Modeling asymptotically independent spatial extremes based on Laplace random fields’, Spatial Statistics, 16, pp. 1–18.
- RESSTE network (2017). Analyzing spatio-temporal data with R: everything you always wanted to know-but were afraid to ask. Journal of the French Statistical Society (Special Issue on Space-Time Statistics), 158(3).
- Mornet, A. et al. (2015) ‘Index for Predicting Insurance Claims from Wind Storms with an Application in France’, Risk Analysis. Wiley Online Library, 35(11), pp. 2029–2056.
- Opitz, T., Bacro, J.-N. and Ribereau, P. (2015) ‘The spectrogram: A threshold-based inferential tool for extremes of stochastic processes’, Electronic Journal of Statistics. Institute of Mathematical Statistics, 9(1), pp. 842–868.
- Tapi Nzali, M. D. et al. (2015) ‘Construction d’un vocabulaire patient/médecin dédié au cancer du sein à partir des médias sociaux’, 26. Journées Francophones d’Ingénierie des Connaissances (IC), Rennes.
- Thibaud, E. and Opitz, T. (2015) ‘Efficient inference and simulation for elliptical Pareto processes’, Biometrika, 102(4), pp. 855–870.
- Opitz, T. et al. (2014) ‘Breast cancer and quality of life: medical information extraction from health forums’, in Medical Informatics Europe Conference 2014, pp. 1070–1074.
- Opitz, T. (2013) ‘Extremal t processes: Elliptical domain of attraction and a spectral representation’, J. Multivar. Anal., 122, pp. 409–413.
Other publications: scientific expertise, discussion contributions, articles for the general public, theses
- Legrand, J., and Opitz, T. (2023). Contribution to the ‘The First Discussion Meeting on Statistical aspects of climate change’. Journal of the Royal Statistical Society: Series C Applied Statistics, 2023, 72 (4), pp.858-859. Link.
- Barbero, R., Girard, S., Opitz, T., Usseglio-Carleve, A. (2023). Les statistiques de l'extrême. Pour la Science. Link.
- Pimont, F. et al. (2023). Projections des effets du changement climatique sur l’activité des feux de forêt au 21ème siècle : Rapport final. Technical report. [HAL reference]
- Saby., N. and Opitz, T. (2023). inlabru: Convenient fitting of Digital Soil Mapping models using INLA-SPDE. Pedometron 47, p22–33.
- Allard, D., Curt, C., Evin, G., Opitz, T. (2022) Analyse multirisque : concepts, méthodes et verrous – un état de l'art prospectif. Rapport technique. Link.
- Opitz (2021). Spatiotemporal modeling of extreme events and point patterns. Habilitation manuscript. Pdf on HAL
- Pimont et al. (2021). Vers une intensification et une extension de l’activité des incendies dans la zone Méditerranéenne. Contribution to the Cahier Régional Occitanie sur les Changements Climatiques (RECO).
- Bakka, H. et al. (2018) ‘Discussion of ``Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al’, Bayesian Analysis.