Publications Publications Overview*: Corresponding authorUnderlined: PhD student or Postdoc advisees under my (co-)supervision at the start of main workUnder review or revision:Talento*, M.S.D., Richards, J., Orellana-Pinto, M., and Huser, R., Ombao, H. (2025+), Spectral extreme connectivity analysis of two-state seizure brain waves dataHussain, A., Leonte, D., Belardinelli, F., Huser, R., and Paccagnan, D. (2025+), Multi-agent Q-Learning dynamics in random networks: convergence due to exploration and sparsity, SubmittedSainsbury-Dale*, M., Zammit-Mangion, A., Cressie, N., and Huser, R. (2025+), Neural parameter estimation with incomplete data, arXiv preprint 2501.04330 [arXiv]Jiang*, J., Richards, J., Huser, R., and Bolin, D. (2024+), The efficient tail hypothesis: an extreme value perspective on market efficiency, arXiv preprint 2408.06661 [arXiv]Zhang*, Z., Bolin, D., Engelke, S., and Huser, R. (2023+), Extremal dependence of moving average processes driven by exponential-tailed Lévy noise, arXiv preprint 2307.15796 [arXiv]Zhang*, L., Ma, X., Wikle, C. K., and Huser, R. (2023+), Flexible and efficient emulation of spatial extremes processes via variational autoencoders, arXiv preprint 2307.08079 [arXiv]Redondo*, P. V., Huser, R., and Ombao, H. (2023+), Measuring information transfer between nodes in a brain network through spectral transfer entropy, arXiv preprint 2303.06384 [arXiv]Best Poster Presentation Award, 2023 Extreme-Value Analysis conference (EVA 2023), Milan, ITRunner-up of Best Student Paper Award 2023, Section on Statistics in Imaging (SI), ASAOesting*, M., and Huser, R. (2022+), Patterns in spatio-temporal extremes, arXiv preprint 2212.11001 [arXiv]Guerrero, M. B., Ombao, H., and Huser*, R. (2022+), Club Exco: clustering brain extreme communities from multi-channel EEG data, arXiv preprint 2212.04338 [arXiv]Redondo*, P. V., Huser, R., and Ombao, H. (2022+), Functional-coefficient models for multivariate time series in designed experiments: with applications to brain signals, arXiv preprint 2208.00292 [arXiv]Richards*, J., and Huser, R. (2022+), Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks, arXiv preprint 2208.07581 [arXiv]Books: de Carvalho, M., Huser, R., Naveau, P., and Reich, B. (2025+), Handbook of Statistics of Extremes, Chapman & Hall/CRC Press, Boca Raton, FL, in preparation (forthcoming for 2025) Journal papers: Fischer*, E., Bador, M., Huser, R., Kendon, E. J., Robinson, A., and Sippel, S. (2024+), Record-breaking extremes in a warming climate, Nature Reviews Earth & Environment, conditionally accepted Zhong, P., Brunner, M., Opitz, T., and Huser*, R. (2024+), Spatial modeling and future projection of extreme precipitation extents, Journal of the American Statistical Association, to appear [journal]Sainsbury-Dale*, M., Richards, J., Zammit-Mangion, A., and Huser, R. (2024+), Neural Bayes estimators for irregular spatial data using graph neural networks, Journal of Computational and Graphical Statistics, to appear [journal]SBSS Student Paper Award 2024, Section on Bayesian Statistical Science (SBSS), ASAHazra, A., Huser*, R., and Bolin, D. (2024+), Efficient modeling of spatial extremes over large geographical domains, Journal of Computational and Graphical Statistics, to appear [journal]Shao, X., Hazra, A., Richards, J., and Huser*, R. (2025), Flexible modeling of nonstationary extremal dependence using spatially-fused LASSO and ridge penalties, Technometrics 67, 97-111 [journal]Huser*, R., Opitz, T., and Wadsworth, J. L. (2025), Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes, Environmental Data Science 4, e3, 1-16 [journal]Kim, M., Genton, M. G., Huser, R., and Castruccio*, S. (2025), A neural network-based adaptive cut-off approach to normality testing for dependent data, Statistics and Computing 35, 22 [journal]Zammit-Mangion*, A., Sainsbury-Dale, M., and Huser, R. (2025), Neural methods for amortized inference, Annual Review of Statistics and Its Application 12, to appear [journal]Richards*, J., Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2024), Neural Bayes estimators for censored inference with peaks-over-threshold models, Journal of Machine Learning Research 25, 1-49 [journal]Vandeskog*, S. M., Huser, R., Bruland, O., and Martino, S. (2024), Fast spatial simulation of extreme high-resolution radar precipitation data using integrated nested Laplace approximations, Journal of the Royal Statistical Society: Series C, qlae074 [journal]Dahal*, A., Huser, R., and Lombardo, L. (2024), At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition, Journal of Geophysical Research: Machine Learning and Computation 1, e2024JH000164 [journal]Huser*, R., Stein, M. L., and Zhong, P. (2024), Vecchia likelihood approximation for accurate and fast inference with intractable spatial max-stable models, Journal of Computational and Graphical Statistics 33, 978-990 [journal]Gong, Y., Zhong, P., Opitz, T., and Huser*, R. (2024), Partial tail-correlation coefficient applied to extremal-network learning, Technometrics 66, 331-346 [journal]Vandeskog*, S. M., Martino, S., and Huser, R. (2024), An efficient workflow for modelling high-dimensional spatial extremes, Statistics and Computing 34, 137 [journal]Krupskii*, P., Huser, R. (2024), Max-convolution processes with random shape indicator kernels, Journal of Multivariate Analysis 2023, 105340 [journal]Cisneros, D., Hazra, A., and Huser*, R. (2024), Spatial wildfire risk modeling using a tree-based multivariate generalized Pareto mixture model, Journal of Agricultural, Biological, and Environmental Statistics 29, 320-345 [journal]Award for Best Paper published in JABES during 2024Zhong, P., Huser*, R., and Opitz, T. (2024), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics 30, 96-109 [journal]Dahal, A., Tanyas, H., van Westen, C., van der Meije, M., Mai, P. M, Huser, R., and Lombardo*, L. (2024), Space-time landslide hazard modeling via ensemble neural networks, Natural Hazards and Earth System Sciences 24, 823-845 [journal]Sainsbury-Dale*, M., Zammit-Mangion, A., and Huser, R. (2024), Likelihood-free parameter estimation with neural Bayes estimators, The American Statistician 78, 1-14 [journal]Cisneros, D., Richards*, J., Dahal, A., Lombardo, L., and Huser, R. (2024), Deep graphical regression models for jointly moderate and extreme Australian wildfires, Spatial Statistics 59, 100811 [journal]Yadav, R., Huser*, R., Opitz, T., and Lombardo, L. (2023), Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions, Journal of the Royal Statistical Society: Series C 72, 1139-1161 [journal]Ahmed*, M., Tanyas, H., Huser, R., Dahal, A., Titti, G., Borgatti, L., Francioni, L., and Lombardo L. (2023), Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system, International Journal of Applied Earth Observation and Geoinformation 125, 103593 [journal]Richards*, J., Huser, R., Bevacqua, E., and Zscheischler, J. (2023), Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning, Artificial Intelligence for the Earth Systems 2, e220095 [journal]Dahal*, A., Castro Cruz, D. A., Tanyas, H., Fadel, I., Mai, P. M., van der Meijde, M., van Westen, C., Huser, R., and Lombardo, L. (2023), From ground motion simulations to landslide occurrence prediction, Geomorphology 441, 108898 [journal]Zhang, Z., Arellano-Valle, R. B., Genton, M. G., and Huser*, R. (2023), Tractable Bayes of skew-elliptical link models for correlated binary data, Biometrics 79, 1788-1800 [journal]Zhang*, Z., Krainski, E., Zhong, P., Rue, H., and Huser, R. (2023), Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes 26, 339-351 [journal]Cisneros, D., Gong, Y., Yadav, R., Hazra*, A., and Huser, R. (2023), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes 26, 301-330 [journal]de Carvalho*, M., Huser, R., and Rubio, R. (2023), Similarity-based clustering for patterns of extreme values, Stat 12, e560 [journal]Guerrero, M. B., Huser*, R., and Ombao, H. (2023), Conex-Connect: Learning patterns in extremal brain connectivity from multi-channel EEG data, Annals of Applied Statistics 17, 178-198 [journal]Gong, Y., and Huser*, R. (2022), Flexible modeling of multivariate spatial extremes, Spatial Statistics 52, 100713 [journal]Krupskii, P., and Huser*, R. (2022), Modeling spatial tail dependence with Cauchy convolution processes, Electronic Journal of Statistics 16, 6135-6174 [journal]Castro-Camilo*, D., Huser, R., and Rue, H. (2022), Practical strategies for generalized extreme value-based regression models for extremes, Environmetrics 33, e2742 [journal]Zhang, Z., Huser*, R., Opitz, T., and Wadsworth, J. L. (2022), Modeling spatial extremes using normal mean-variance mixtures, Extremes 25, 175-197 [journal]Gong, Y., and Huser*, R. (2022), Asymmetric tail dependence modeling, with application to cryptocurrency market data, Annals of Applied Statistics 16, 1822-1847 [journal]Opitz*, T., Bakka, H., Huser, R., and Lombardo, L. (2022), High-resolution Bayesian mapping of landslide hazard with unobserved trigger event, Annals of Applied Statistics 16, 1653-1675 [journal]Jóhannesson, Á. V., Siegert, S., Huser*, R., Bakka, H., and Hrafnkelsson, B. (2022), Approximate Bayesian inference for analysis of spatio-temporal flood frequency data, Annals of Applied Statistics 16, 905-935 [journal]Yadav, R., Huser*, R., and Opitz, T. (2022), A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data, Spatial Statistics 51, 100672 [journal]Zhong, P., Huser*, R., and Opitz, T. (2022), Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude, Annals of Applied Statistics 16, 272-299 [journal]Huser*, R., and Wadsworth, J. L. (2022), Advances in statistical modeling of spatial extremes, Wiley Interdisciplinary Reviews (WIREs): Computational Statistics 14, e1537 [journal]Lombardo*, L., Tanyas, H., Huser, R., Guzzetti, F., and Castro-Camilo, D. (2021), Landslide size matters: a new data-driven, spatial prototype, Engineering Geology 293, 106288 [journal]Hazra, A., and Huser*, R. (2021), Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model, Annals of Applied Statistics 15, 572-596 [journal]Hrafnkelsson*, B., Siegert, S., Huser, R., Bakka, H., and Jóhannesson, Á. V. (2021), Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models, Bayesian Analysis 16, 611-638 [journal]Yadav, R., Huser*, R., and Opitz, T. (2021), Spatial hierarchical modeling of threshold exceedances using rate mixtures, Environmetrics 32, e2662 [journal]Bopp*, G., Shaby, B., and Huser, R. (2021), A hierarchical max-infinitely divisible spatial model for extreme precipitation, Journal of the American Statistical Association 116, 93-106 [journal]Huser*, R. (2021), Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes, Extremes 24, 91-104 [journal]Huser*, R., Opitz, T., and Thibaud, E. (2021), Max-infinitely divisible models and inference for spatial extremes, Scandinavian Journal of Statistics 48, 321-348 [journal]Khandavilli*, M., Yalamanchi, K. K., Huser, R., and Sarathy, M. (2020), Effects of fuel composition variability on high temperature combustion properties: A statistical analysis, Applications in Energy and Combustion Science 1-4, 100012 [journal]Lombardo*, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R. (2020), Space-time landslide predictive modelling, Earth-Science Reviews 209, 103318 [journal]Castro Camilo*, D., and Huser, R. (2020), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association 115, 1037-1054 [journal]Vettori, S., Huser*, R., Segers, J., and Genton, M. G. (2020), Bayesian model averaging over tree-based dependence structures for multivariate extremes, Journal of Computational and Graphical Statistics 29, 174-190 [journal]ENVR Student Paper Award 2017, Section on Statistics and the Environment (ENVR), ASAAlam, T., Alazmi, M., Naser, R., Huser, F., Momin, A. A., Astro, V., Hong, S., Walkiewicz, K. W., Canlas, C. G., Huser, R., Ali, A., Merzaban, J., Adamo, A., Jaremko, M., Jaremko, Ł., Bajic, V. B., Gao, X., and Arold, S. T. (2020), Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs, Bioinformatics 36, 1121-1128 [journal]Vettori, S., Huser*, R., and Genton, M. G. (2019), Bayesian modeling of air pollution extremes using nested multivariate max-stable processes, Biometrics 75, 831-841 [journal]Distinguished Student Paper Award 2018, Eastern North American Region (ENAR) of the International Biometric SocietyCastro-Camilo*, D., Huser, R., and Rue, H. (2019), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics 24, 517-534 [journal]Lombardo*, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research: Earth Surface 124, 1958-1980 [journal]Huser, R. and Wadsworth*, J. (2019), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association 114, 434-444 [journal]Huser*, R., Dombry, C., Ribatet, M., and Genton, M. G. (2019), Full likelihood inference for max-stable data, Stat 8, e218 [journal]Opitz, T., Huser*, R., Bakka, H., and Rue, H. (2018), INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles, Extremes 21, 441-462 [journal]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 32, 2179-2198 [journal]Highlighted among the 10 most downloaded 2018 papers in Springer's Environmental Sciences Journals (click here)Hofert*, M., Huser, R., and Prasad, A. (2018), Hierarchical archimax copulas, Journal of Multivariate Analysis 167, 195-211 [journal]Krupskii*, P., Huser, R., and Genton, M. G. (2018), Factor copula models for replicated spatial data, Journal of the American Statistical Association 113, 467-479 [journal]Vettori*, S., Huser, R., and Genton, M. G. (2018), A comparison of dependence function estimators in multivariate extremes, Statistics and Computing 28, 525-538 [journal]Lombardo*, L., Saia, S., Schillaci, C., Mai, P. M., and Huser, R. (2018), Modeling soil organic carbon with Quantile Regression: Dissecting predictors’ effects on carbon stocks, Geoderma 318, 148-159 [journal]Huser*, R., Opitz, T., and Thibaud, E. (2017), Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures, Spatial Statistics 21, 166-186 [journal]Castro Camilo, D., Lombardo*, L., Mai, P. M., Jie, D., and Huser, R. (2017), Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model, Environmental Modelling and Software 97, 145-156 [journal]Castruccio*, S., Huser, R., and Genton, M. G. (2016), High-order composite likelihood inference for max-stable distributions and processes, Journal of Computational and Graphical Statistics 25, 1212-1229 [journal]Naveau*, P., Huser, R., Ribereau, P., and Hannart, A. (2016), Modeling jointly low, moderate and heavy rainfall intensities without a threshold selection, Water Resources Research 52, 2753-2769 [journal]Huser*, R., and Genton, M. G. (2016), Non-stationary dependence structures for spatial extremes, Journal of Agricultural, Biological and Environmental Statistics 21, 470-491 [journal]Award for Best Paper published in JABES during 2016Huser*, R., Davison, A. C., and Genton, M. G. (2016), Likelihood estimators for multivariate extremes, Extremes 19, 79-103 [journal]Ben Taieb*, S., Huser, R., Hyndman, R. J., and Genton, M. G. (2016), Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression, IEEE Transactions on Smart Grid 7, 2448-2455 [journal]Davison*, A. C., and Huser, R. (2015), Statistics of extremes, Annual Review of Statistics and its Application 2, 203-235 [journal]Genton*, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y., and Vettori, S. (2015), Visuanimation in statistics, Stat 4, 81-96 [journal]Huser, R., and Davison*, A. C. (2014), Space-time modelling of extreme events, Journal of the Royal Statistical Society: Series B 76, 439-461 [journal]Davison*, A. C., Huser, R. and Thibaud, E. (2013), Geostatistics of dependent and asymptotically independent extremes, Mathematical Geosciences 45, 511-529 [journal]Huser, R., and Davison*, A. C. (2013), Composite likelihood estimation for the Brown-Resnick process, Biometrika 100, 511-518 [journal]Anderes*, E., Huser, R., Nychka, D., and Coram, M. (2013) Nonstationary positive definite tapering on the plane, Journal of Computational and Graphical Statistics 22, 848-865 [journal]Contributions to papers with discussion:Huser*, R., and Zammit-Mangion, A. (2024+), R. Huser and A. Zammit-Mangion's contribution to the Discussion of `the Discussion Meeting on Analysis of citizen science data' by J. Koh and T. Opitz, Journal of the Royal Statistical Society: Series A, to appear Huser, R. (2024), Seconder of the vote of thanks to Healy et al. and contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland', Journal of the Royal Statistical Society: Series C, qlae078 [journal]Hazra*, A., and Huser, R. (2021), Discussion of "Multilevel linear models, Gibbs samplers and multigrid decompositions" by Giacomo Zanella and Gareth Roberts, Bayesian Analysis 16, 1309-1391 [journal]Huser*, R., and Cisneros, D. (2020), Discussion of "Graphical models for extremes" by Sebastian Engelke and Adrien S. Hitz, Journal of the Royal Statistical Society: Series B 82, 871-932 [journal]Huser*, R., de Carvalho, M., and Lombardo, L. (2019), Discussion on the meeting on `Data visualization', Journal of the Royal Statistical Society: Series A 182, 419-441 [journal]Bakka, H., Castro Camilo, D., Franco-Villoria, M., Freni-Sterrantino, A., Huser, R., Opitz, T., and Rue*, H. (2018), Discussion of "Using stacking to average Bayesian predictive distributions" by Yao et. al, Bayesian Analysis 13, 917-1003 [journal]Book chapters:Redondo, P. V., Guerrero, M., Ombao*, H., and Huser, R. (2024+), Statistics of extremes for neuroscience, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL, tentatively accepted Yadav, R., Lombardo*, L. and Huser, R. (2024+), Statistics of extremes for natural hazards: landslides and earthquakes, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL, tentatively accepted Richards, J. and Huser*, R. (2024+), Extreme quantile regression with deep learning, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL, tentatively accepted Hazra, A., Huser*, R., and Jóhannesson, Á. V. (2023), Bayesian latent Gaussian models for high-dimensional spatial extremes, In Statistical Modeling Using Bayesian Latent Gaussian Models – With Applications in Geophysics and Environmental Sciences, editor B. Hrafnkelsson, Springer, 219-251 [book]Lombardo*, L., Opitz, T., and Huser, R. (2019), Numerical recipes for landslide spatial prediction by using R-INLA: A step-by-step tutorial, In Spatial Modeling in GIS and R for Earth and Environmental Sciences, editors H. R. Pourghasemi and C. Gokceoglu, Elsevier, 55-83 [book]Davison*, A. C., Huser, R., and Thibaud, E. (2019), Spatial extremes, In Handbook of Environmental and Ecological Statistics, editors A. E. Gelfand, M. Fuentes, J. A. Hoeting and R. L. Smith, CRC Press, 711-744 [book]PhD Thesis:Huser*, R. (2013), Statistical Modeling and Inference for Spatio-Temporal Extremes, Ph.D. thesis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland [PhD thesis][PDF]Lambert Award 2015 (Prize to recognize the work of young statisticians up to age 35)EPFL Doctoral Award 2014 (2 laureates among 403 Ph.D. theses defended)
