[17] 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][PDF]

⇒     Runner-up of Best Student Paper Award 2023, Section on Statistics in Imaging (SI), ASA

[16] 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, EarthArXiv preprint 4931 [EarthArXiv][PDF]

[15] Oesting*, M., and Huser, R. (2022+), Patterns in spatio-temporal extremes, arXiv preprint 2212.11001 [arXiv][PDF]

[14] 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][PDF]

[13] Zhong, P., Brunner, M., Opitz, T., and Huser*, R. (2022+), Spatial modeling and future projection of extreme precipitation extents, arXiv preprint 2212.03028 [arXiv][PDF]

[12] Richards*, J., Huser, R., Bevacqua, E., and Zscheischler, J. (2022+), Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning, arXiv preprint 2212.01796 [arXiv][PDF]

[11] Gong, Y., Zhong, P., Opitz, T., and Huser*, R. (2022+), Partial tail-correlation coefficient applied to extremal-network learning, arXiv preprint 2210.07351 [arXiv][PDF]

[10] Shao, X., Hazra, A., Richards, J., and Huser*, R. (2022+), Flexible modeling of nonstationary extremal dependence using spatially-fused LASSO and ridge penalties, arXiv preprint 2210.05792 [arXiv][PDF]

  [9] Vandeskog*, S. M., Martino, S., and Huser, R. (2022+), An efficient workflow for modelling high-dimensional spatial extremes, arXiv preprint 2210.00760 [arXiv][PDF]

  [8] Sainsbury-Dale*, M., Zammit-Mangion, A., and Huser, R. (2022+), Neural point estimation for fast optimal likelihood-free inference, arXiv preprint 2208.12942 [arXiv][PDF] 

  [7] 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][PDF] 

  [6] Dahal, A., Tanyas, H., van Westen, C., van der Meije, M., Mai, P. M, Huser, R., and Lombardo*, L. (2022+), Space-time landslide hazard modeling via ensemble neural networks, EarthArXiv preprint 3382 [EarthArXiv][PDF]

  [5] Richards*, J., and Huser, R. (2022+), Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks, arXiv preprint 2208.07581 [arXiv][PDF]

  [4] Yadav, R., Huser*, R., Opitz, T., and Lombardo*, L. (2022+), Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions, arXiv preprint 2205.09908 [arXiv][PDF]

  [3] Huser*, R., Stein, M. L., and Zhong, P. (2022+), Vecchia likelihood approximation for accurate and fast inference in intractable spatial extremes models, arXiv preprint 2203.05626 [arXiv][PDF]

  [2] Hazra, A., Huser*, R., and Bolin, D. (2021+), Realistic and fast modeling of spatial extremes over large geographical domains, arXiv preprint 2112.10248 [arXiv][PDF]

  [1] Hazra*, A., Alahmadi, E., and Huser, R. (2021+), Extreme-value analysis: a brief summary, submitted [arXiv][PDF]

[52] Zhang, Z., Arellano-Valle, R. B., Genton, M. G., and Huser*, R. (2022+), Tractable Bayes of skew-elliptical link models for correlated binary data, Biometrics, to appear [journal][PDF preprint]

[51] Zhong, P., Huser*, R., and Opitz, T. (2022+), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics, to appear [journal][PDF preprint]

[50] 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][PDF preprint]

[49] 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][PDF preprint]

[48] de Carvalho*, M., Huser, R., and Rubio, R. (2023), Similarity-based clustering for patterns of extreme values, Stat 12, e560 [journal][PDF preprint]

[47] 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][PDF preprint]

[46] Gong, Y., and Huser*, R. (2022), Flexible modeling of multivariate spatial extremes, Spatial Statistics 52, 100713 [journal][PDF preprint]

[45] Krupskii, P., and Huser*, R. (2022), Modeling spatial tail dependence with Cauchy convolution processes, Electronic Journal of Statistics 16, 6135-6174 [journal][PDF preprint]

[44] Castro-Camilo*, D., Huser, R., and Rue, H. (2022), Practical strategies for generalized extreme value-based regression models for extremes, Environmetrics 33, e2742 [journal][PDF preprint]

[43] Zhang, Z., Huser*, R., Opitz, T., and Wadsworth, J. L. (2022), Modeling spatial extremes using normal mean-variance mixtures, Extremes 25, 175-197 [journal][PDF preprint]

[42] Gong, Y., and Huser*, R. (2022), Asymmetric tail dependence modeling, with application to cryptocurrency market data, Annals of Applied Statistics 16, 1822-1847 [journal][PDF preprint]

[41] 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][PDF preprint]

[40] 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][PDF preprint] 

[39] 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][PDF preprint]

[38] 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][PDF preprint]

[37] Huser*, R., and Wadsworth, J. L. (2022), Advances in statistical modeling of spatial extremes, Wiley Interdisciplinary Reviews (WIREs): Computational Statistics 14, e1537 [journal][PDF preprint]

[36] 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][PDF preprint]

[35] 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][PDF preprint]

[34] 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][PDF preprint]

[33] Yadav, R., Huser*, R., and Opitz, T. (2021), Spatial hierarchical modeling of threshold exceedances using rate mixtures, Environmetrics 32, e2662 [journal][PDF preprint]

[32] 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][PDF preprint]

[31] Huser*, R. (2021), Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes, Extremes 24, 91-104 [journal][PDF preprint]

[30] 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][PDF preprint]

[29] 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][PDF preprint]

[28] Lombardo*, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R. (2020), Space-time landslide predictive modelling, Earth-Science Reviews 209, 103318 [journal][PDF preprint]

[27] 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][PDF preprint]

[26] 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][PDF preprint]

⇒     ENVR Student Paper Award 2017, Section on Statistics and the Environment, ASA

[25] Alam, 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][PDF preprint]

[24] 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][PDF preprint]

⇒     Distinguished Student Paper Award 2018, Eastern North American Region (ENAR) of the International Biometric Society

[23] Castro-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][PDF preprint]

[22] 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][PDF preprint]

[21] Huser, R. and Wadsworth*, J. (2019), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association 114, 434-444 [journal][PDF preprint]

[20] Huser*, R., Dombry, C., Ribatet, M., and Genton, M. G. (2019), Full likelihood inference for max-stable data, Stat 8, e218 [journal][PDF preprint]

[19] 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][PDF preprint]

[18] 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][PDF preprint]

⇒     Highlighted among the 10 most downloaded 2018 papers in Springer's Environmental Sciences Journals (click here)

[17] Hofert*, M., Huser, R., and Prasad, A. (2018), Hierarchical archimax copulas, Journal of Multivariate Analysis 167, 195-211 [journal][PDF preprint]

[16] 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][PDF preprint]

[15] 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][PDF preprint]

[14] 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][PDF preprint]

[13] 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][PDF preprint]

[12] 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][PDF preprint]

[11] 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][PDF preprint]

[10] 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][PDF preprint]

 [9] 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][PDF preprint]

⇒    Award for Best Paper published in JABES during 2016

 [8] Huser*, R., Davison, A. C., and Genton, M. G. (2016), Likelihood estimators for multivariate extremes, Extremes 19, 79-103 [journal][PDF preprint]

 [7] 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][PDF preprint]

 [6] Davison*, A. C., and Huser, R. (2015), Statistics of Extremes, Annual Review of Statistics and its Application 2, 203-235 [journal][PDF preprint]

 [5] 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][PDF preprint]

 [4] Huser, R., and Davison*, A. C. (2014), Space-time modeling of extreme events, Journal of the Royal Statistical Society: Series B 76, 439-461 [journal][PDF preprint]

 [3] Davison*, A. C., Huser, R. and Thibaud, E. (2013), Geostatistics of dependent and asymptotically independent extremes, Mathematical Geosciences 45, 511-529 [journal][PDF preprint]

 [2] Huser, R., and Davison*, A. C. (2013), Composite likelihood estimation for the Brown-Resnick process, Biometrika 100, 511-518 [journal][PDF preprint]

 [1] 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][PDF preprint]

  [4] 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][PDF preprint]

  [3] 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][PDF preprint] 

  [2] 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][PDF preprint]

  [1] 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][PDF preprint]

  [3] Hazra, A., Huser*, R., and Jóhannesson, Á. V. (2021+), 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, to appear [arXiv][PDF]

  [2] 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][PDF preprint]

  [1] 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][PDF preprint]

  [1] 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)