Homepage Thomas Opitz

Senior Research Scientist ("Directeur de Recherche") within the MathNum division of INRAE, 
in the Biostatistics and Spatial Processes unit located in Avignon in the South of France. 
 

Table of contents of this page

1. Contact
2. Research interests
3. PhDs and Postdocs (past and present)
4. Grants and projects
5. Collective responsibilities
6. Publications
  6.1 Preprints
  6.2 Peer-reviewed articles
  6.3 Other publications
7. Teaching
8. Distinction

 

1. 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

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2. 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

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3. PhD and Postdoc supervisions (past and present)

• Viviana Carcaiso (Postdoc 2024–2025, funded by H2020 FIRE-RES). Prediction with extreme covariates. Joint supervision with Sebastian Engelke (Geneva University) and Juliette Legrand (University of Brest)
• Laurie Leterrier (PhD 2024–2027, funded by ANR EXSTA), Analysis and prediction of compound extremes. Joint supervision with Marine Demangeot (University of Montpellier), Nicolas Meyer (University of Montpellier), Philippe Naveau (LSCE-IPSL)
• Lorenzo Dell'Oro (Visiting PhD, 2024–2025). Gaussian location-scale mixtures for extended modeling of spatiotemporal extreme events. Joint supervision with his main supervisor is Carlo Gaetan (Ca‘Foscari University, Venice, Italy)
• Rita Maatouk (PhD 2024–2027, funded by Chaire Geolearning), Statistical modeling of extreme events on metric graphs
  – Application to flood risk in Europe. Joint supervision with Mike Pereira (Mines Paris – PSL)
• Antoine Heranval (Postdoc 2024-2026, funded by Chaire Geolearning), Analyzing the dynamics of extreme events with marked point processes. Joint supervision with Denis Allard, Édith Gabriel (BioSP, INRAE), Mike Pereira (Centre de Geosciences, Mines Paris – PSL)
• 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)

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4. Projects

• PEPR Maths VivES (2024–2029), Target projet "Climaths" 
• RIMINI Consortium (RIsques et Multi-rIsques : des coNcepts et des formalIsmes différents ?) 2024–2025 (Funded by the XRISQUES MetaProgram of INRAE)
• 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

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5. 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

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6. Publications 
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   6.1 Preprints

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   6.2 Peer-reviewed articles

1. Castel-Clavera, J. et al. A comparative analysis of fire-weather indices for enhanced fire activity prediction with probabilistic approaches. Agricultural and Forest Meteorology.
2. Girard, S., Opitz, T., Usseglio-Carleve, A. Analysis of variability for heavy-tailed extremes. Electronic Journal of Statistics. Link to HAL preprint.
3. Huser, R. Opitz, T., Wadsworth, J. Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes. Environmental Data Science. 
4. Bacro, J.-N., Gaetan, C., Opitz, T., Toulemonde, G. Multivariate peaks-over-threshold with latent variable representations of generalized Pareto vectors. Extremes.  Link to HAL preprint.
5. Legrand, J., Pimont, F., Dupuy, J.-L., Opitz, T. (2024). Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change – A step-by-step marked point process approach using INLA-SPDE. Computo. Link.
6. Zhong, P., Brunner, M., Opitz, T., Huser, R.. Spatial modeling and future projection of extreme precipitation extents. Journal of the American Statistical Association. [Link to arXiv preprint]
7. Koh, J., Opitz, T. Extreme-value modelling of migratory bird arrival dates: Insights from citizen-science data. JRSS A (Discussion paper). Link to arXiv preprint.
8. Di Bernardino, E., Estrade, A., Opitz, T. (2024). Spatial extremes and stochastic geometry for Gaussian-based peaks-over-threshold processes. Extremes. HAL preprint.
9. Gong, Y., Zhong, P., Opitz, T., Huser, R. (2024). Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning. Technometrics. [arXiv preprint]
10. Yadav, R., Huser, R., Opitz, T., Lombardo, L. (2023). Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions. JRSS C. [arXiv preprint]
11. 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. [HAL preprint]
12. Belzile, L., Dutang, C.,  Northrop P. J., Opitz, T. (2023). A modeler’s guide to extreme value software. Extremes.
13. 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. [Link to arXiv preprint]
14. 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.
15. Pimont, F. et al. (2022) Expansion, lengthening and intensification of fire activities under climate change in Southeastern France. International Journal of Wildland Fire. Link to open full text.
16. 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.
17. Koh, J., Pimont, F., Dupuy, J.-L., Opitz, T. (2022). Spatiotemporal wildfire modeling through point processes with moderate and extreme marks. Annals of Applied Statistics. [arXiv preprint]
18. Hu et al. (2022). Stoichiometry of soil carbon, nitrogen, and phosphorus in farmland soils in southern China: Spatial pattern and related dominates. CATENA.
19. Yadav, R., Huser, R., Opitz, T. (2022). A flexible Bayesian hierarchical modeling framework for spatial peaks-over-threshold data. Spatial Statistics. arXiv preprint arXiv:2112.09530.
20. Rivière, M. et al. (2022). A Bioeconomic Projection of Climate-induced Wildfire Risk in the Forest Sector. Earth's Future.
21. Zhong, P., Huser, R. and Opitz, T. (2022). Exact Simulation of Max-Infinitely Divisible Processes. Econometrics and Statistics. arXiv preprint arXiv:2103.00533.
22. Zamberletti, P., Papaïx, J., Gabriel, É., Opitz, T. (2022). Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes. Ecography. Link to bioRxiv preprint.
23. Zhang, Z., Huser, R., Opitz, T., & Wadsworth, J. L. (2022). Modeling spatial extremes using normal mean-variance mixtures. Extremes. arXiv preprint arXiv:2105.05314.
24. Opitz, T., Bakka, H., Huser, R., & Lombardo, L. (2022). High-resolution Bayesian mapping of landslide hazard with unobserved trigger event. Annals of Applied Statistics [Link to arXiv preprint].
25. Zamberletti, P., Sabir, K., Opitz, T.,  Bonnefon, O., Gabriel, E., Papaïx, J. (2022). More pests but less treatments: ambivalent effect of landscape complexity on Conservation Biological Control. PLOS Computational Biology. Link to bioRxiv preprint.
26. 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
27. 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).
28. 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.
29. 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.
30. Yadav, R., Opitz, T. and Huser, R.  ‘Spatial hierarchical modeling of threshold exceedances using rate mixtures’. Accepted for Environmetrics.
31. Pimont, F. et al. (2021). Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood. Ecological applications.
32. 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.'
33. 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.
34. 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.
35. 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.
36. Lombardo, L. et al. (2020). Space-Time Landslide Predictive Modelling. Earth Science Reviews. Link to arXiv preprint.
37. Opitz, T., Allard, D. and Mariethoz, G. (2020) ‘Semi-parametric resampling with extremes’, Spatial Statistics. doi: 10.1016/j.spasta.2020.100445.
38. 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.
39. 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.
40. Engelke, S., Opitz, T. and Wadsworth, J. L. (2019) ‘Extremal dependence of random scale constructions’, Extremes.
41. 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.
42. Mhalla, L., Opitz, T. and Chavez-Demoulin, V. (2019) ‘Exceedance-based nonlinear regression of tail dependence’, Extremes. Springer, pp. 1–30.
43. 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.
44. 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.
45. 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.
46. 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.
47. 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).
48. 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.
49. 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.
50. 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).
51. 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).
52. Opitz, T. (2016) ‘Modeling asymptotically independent spatial extremes based on Laplace random fields’, Spatial Statistics, 16, pp. 1–18.
53. 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).
54. 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.
55. 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.
56. 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.
57. Thibaud, E. and Opitz, T. (2015) ‘Efficient inference and simulation for elliptical Pareto processes’, Biometrika, 102(4), pp. 855–870.
58. 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.
59. Opitz, T. (2013) ‘Extremal t processes: Elliptical domain of attraction and a spectral representation’, J. Multivar. Anal., 122, pp. 409–413.

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   6.3 Other publications: scientific expertise, discussion contributions, articles for the general public, theses

1. Opitz, T. (2024). Discussion (as Proposer) of "Inference for extreme spatial temperature events in a changing climate with application to Ireland" by Healy et al. Journal of the Royal Statistical Society: Series C. HAL preprint.
2. 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.
3. Barbero, R., Girard, S., Opitz, T., Usseglio-Carleve, A. (2023). Les statistiques de l'extrême. Pour la Science. Link.
4. 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]
5. Saby., N. and Opitz, T. (2023). inlabru: Convenient fitting of Digital Soil Mapping models using INLA-SPDE. Pedometron 47, p22–33.
6. Allard, D., Curt, C., Evin, G., Opitz, T. (2022) Analyse multirisque : concepts, méthodes et verrous – un état de l'art prospectif. Rapport technique. Link.
7. Opitz (2021). Spatiotemporal modeling of extreme events and point patterns. Habilitation manuscript. Pdf on HAL
8. 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).
9. Bakka, H. et al. (2018) ‘Discussion of ``Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al’, Bayesian Analysis.

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7. Teaching

• 2020–2024: 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

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8. Distinction

Young Researcher Award of INRAE in 2020 ("Laurier Espoir Scientifique"): General presentation and my portrait

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