Homepage Thomas Opitz

Homepage of Thomas Opitz

I am a research associate ("chargé de recherche") at the Biostatistics and Spatial processes lab of INRAE in Avignon.

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

  • My research concerns spatio-temporal modeling and prediction of environmental, climatological, ecological and epidemiological risks.
  • I develop and implement theoretical and statistical tools  at the inferface of Extreme-Value Theory, which provides a framework for predicting probabilities of events with very extreme magnitudes, and stochastic geometry, useful for studying geometric patterns in occurrence locations/times and in agricultural landscapes.
  • For inference in stochastic models, a blend of frequentist and Bayesian inference techniques is used, with a particular focus on the integrated nested Laplace approximation (INLA).
  • Statistical models are constructed to combine data available at multiple spatial and temporal scales (e.g., raster, lattice, irregularly spaced locations) and from multiple sources (validated data with strict observation / transformation protocol, citizen science programs, etc.), often large georeferenced datasets.

Current research

  1. Bayesian modeling of wildfire activity and landslides using marked log-Gaussian Cox processes.
  2. Space-time modeling of extremes in climate and weather data:
    • New theory and inference tools for models with flexible joint tail decay rates, often based on scale or location mixture representation.
    • Modeling  temporal trends and dependence in spatially resolved observations.
    • 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.
  3. Stochastic simulation and statistical inference for agricultural landscapes using stochastic geometry tools.
  4. (Latent) Lévy convolutions: novel spatial-temporal models for non Gaussian data (extremes, count data) based on kernel-smoothed infinitely divisible distributions, with a focus on gamma processes.
  5. Spatial and spatiotemporal modeling of ecological processes (Asian hornet invasion and efficiency of capturing them ;  wolf attacks on sheep herds).
  6. Space-time mapping of soil variables with focus on temporal trends in soil properties.

Projects with funding

  • Co-Investigator of a KAUST Competitive Research Grant  (2018-2021) coordinated by Raphael Huser, with partners at KAUST and Lancaster University:
    "Statistical Estimation and Detection of Extreme Hot Spots, with Environmental and Ecological Applications".
  • PhD project of Patrizia Zamberletti (2018-2021), co-supervised with Julien Papaix and Edith Gabriel at BioSP. Funding: INRAE divisions MIA (25%) and SPE (25%), and Provence-Alpes Côtes d’Azur region (50%).
    "Simulation and inference of agricultural landscapes using stochastic geometry; agroecological analysis of numerical simulations of spatially explicit population dynamics model (sensitivity analysis, statistical learning)".
  • Post-doc project (2017-2019) of Fátima Palacíos-Rodriguez, with funding from MUSE, Labex Numev and Inria:
    "Semiparametric resampling of extreme events over space and time, with an application to precipitation data, and with a view towards extreme risk measures".
  • 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" ("Approches innovantes de prévisions géo-datées des propriétés des sols").

Responsibilities and research networks

  • Steering committee member of RESSTE ("RESeau Statistique pour données Spatio-TEmporelles"), one of INRAE's current research networks.
  • Elected member and webmaster of the "Groupe Environnement et Statistique" of the French Statistical Society.
  • Co-organizer of BioSP's seminar.

Teaching

  • 2020/2021: Course "Introduction to extreme-value analysis" at École Centrale Marseille, Master Climaths 
  • since 2018: Course "Statistique spatiale et écologie", M2 Data Science, Marseille
  • 21/03/2019: One-day master course on Multivariate Extremes, ATHENS network, MinesParisTech
  • 2016/2017: Statistique Descriptive 2, L1 STID, IUT Avignon

Preprints

  • Grente, O. et al. 'Tirs dérogatoires de loups en France : état des connaissances et des enjeux pour la gestion des attaques aux troupeaux. Submitted to 'Faune Sauvage.'
  • Opitz, T. et al. ‘High-resolution Bayesian mapping of landslide hazard with unobserved trigger event’.
  • Opitz, T.  ‘Spatial random field models based on Lévy indicator convolutions’, arXiv preprint arXiv:1710.06826.
  • Palacios-Rodriguez, F. et al. ‘Semi-parametric generalized Pareto processes for simulating space-time extreme events’.
  • Pimont, F. et al. ‘Prediction of regional wildfire activity with a probabilistic Bayesian framework’.
  • Yadav, R., Opitz, T. and Huser, R.  ‘Spatial hierarchical modeling of threshold exceedances using rate mixtures’.
  • Zamberletti, P. et al. ‘Landscape allocation: stochastic generators and statistical inference’. Link to arXiv preprint.
  • Zhong, P., Huser, R. and Opitz, T. ‘Assessing Non-Stationary Heatwave Hazard with Magnitude-Dependent Spatial Extremal Dependence’. arXiv preprint arXiv:2006.01569.

Publications

  • 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.
  • 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.

Current responsibilities

  • Co-organizer of the weekly seminar of BioSP
  • Elected member of the French Statistical Society's "Environment and Statistics" group

Past responsibilities and activities

  • Organizing committee and webmaster of the GdR GeoSto Meeting 2019 in Avignon
  • Program Committee of Spatial Statistics 2019, Sitges, Spain
  • Member of the organizing team and Webmaster of the GdR GeoSto meeting in June 2019 (Avignon)
  • Vice-president of organizing committee and webmaster (web site and registration, submission, review systems) of the METMA IX conference on Space-time modeling and statistics,  13-15 June  2018, Montpellier
  • Webmaster (web site and registration, submission, review systems) of the Journées de Statistique 2017, Avignon
  • Organization and Program Committee of AG MIA 2017 / Journées Maths-Infos / Journées INRA-Inria
  • Program Committee of Spatial Statistics 2015, Avignon