Homepage Thomas Opitz

I am research associate at the Biostatistics and Spatial processes lab of INRAE in Avignon.

Contact

Mail: thomas POINT opitz AT inrae POINT fr
Telephone: 04 32 72 21 86
Address: INRAE-BioSP – Domaine St. Paul – 228, route de l'Aérodrome – 84914 Avignon – France

My general research interests

  • Spatio-temporal modeling and prediction of environmental, climatological, ecological and epidemiological risks.
  • Theoretical and statistical tools  at the inferface of two fields:
    • Extreme-Value Theory, which provides a framework for predicting probabilities of events with very extreme magnitudes, and
    • Stochastic Geometry, useful for studying stochastic geometric patterns in agricultural landscapes or in point clouds formed by local extrema of biophysical processes or by locations and times of environmental risk occurrences.
  • Statistical inference in stochastic models using a blend of frequentist and Bayesian inference techniques, with a particular focus on the integrated nested Laplace approximation (INLA).
  • Statistical models combining data available at multiple spatial and temporal scales (e.g., raster, lattice, irregularly spaced locations) and from multiple sources (validated data with strict sampling protocol, citizen science programs, etc.), often large georeferenced datasets.

Current research

  • Bayesian modeling of wildfire activity and landslides using marked log-Gaussian Cox processes.
  • Space-time modeling of extremes in climate and weather data:
    • New theory and inference tools for models of joint extremes, often based on scale or location mixture representations.
    • Modeling  nonstationarity in the trends and the dependence of spatially indexed data.
    • Semiparametric resampling techniques for spatial and spatio-temporal extremes.
    • Applications to meteorological and climatic processes (precipitation, wind speed, temperature, air pollution), with a view towards climate change effects.
  • Stochastic simulation and statistical inference for agricultural landscapes using stochastic geometry tools.
  • Spatial and spatiotemporal stochastic modeling of ecological processes (Asian hornet invasion and efficiency of capturing them; wolf attacks on sheep herds, etc.).
  • Space-time mapping of soil properties with focus on temporal trends.
  • Non-Gaussian convolution models based on infinitely divisible distributions, with a focus on gamma processes and applications to extremes and discrete data.

Supervision

  • 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 Clarke (PhD, 2021-2024), Towards improved spatiotemporal wildfire danger indices
  • 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

  • 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
  • Pari scientifique EA, coordinated by Hocine Bourennane and Nicolas Saby (2018-2020), Innovative approaches for space-time prediction and mapping of soil properties using INLA

Distinction

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

Current responsibilities and research networks

  • Steering committee member of CLIMAE, INRAE's meta-programme for bringing together climate change adaptation and mitigation
  • Steering committee member of RESSTE ("RESeau Statistique pour données Spatio-TEmporelles"), one of INRAE's current research networks.
  • Elected member and webmaster of Groupe Environnement et Statistique of the French Statistical Society.

Teaching

  • Since 2020: Course "Introduction to extreme-value analysis" at École Centrale Marseille, Master Climaths 
  • 2018-2020: Course "Statistique spatiale et écologie", M2 Data Science, Marseille
  • 2019, 2021: One-day Master Course on Multivariate Extremes, ATHENS network, MinesParisTech
  • 2016/2017: Statistique Descriptive 2, L1 STID, IUT Avignon

Preprints

  • Rivière, M. et al. A Bioeconomic Projection of Climate-induced Wildfire Risk in the Forest Sector.
  • Zhang, Z., Huser, R., Opitz, T., & Wadsworth, J. L. Modeling spatial extremes using normal mean-variance mixtures. arXiv preprint arXiv:2105.05314.
  • Zamberletti, P., Papaïx, J., Gabriel, É., Opitz, T. Spatio-temporal point processes as meta-models for population dynamics in heterogeneous landscapes. Link to bioRxiv preprint.
  • Zhong, P., Huser, R. and Opitz, T. Exact Simulation of Max-Infinitely Divisible Processes. arXiv preprint arXiv:2103.00533.
  • Simpson, E., Opitz, T. and Wadsworth J. L. High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and the SPDE approach. [Link to arXiv preprint]
  • Opitz, T.  Spatial random field models based on Lévy indicator convolutions. Link to arXiv preprint.

Publications

  • Opitz, T. et al. 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. ‘Prediction of regional wildfire activity with a probabilistic Bayesian framework’. 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’, To appear in 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.
  • Bakka, H. et al. (2018) ‘Discussion of ``Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al’, Bayesian Analysis.
  • 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.