I am an assistant professor in the Department of Statistics at the University of California, Davis.  I am also a faculty member in the Graduate Group in Epidemiology (GGE) and Graduate Group in Biostatistics.

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My research spans applied and methodological work in Bayesian statistics. I develop Bayesian methods for official statistics, demography, and small area estimation, with applications in National Statistical Offices (NSOs) in low- and middle-income countries. My experience with non-governmental organizations and NSOs provides practical grounding for this research. My theoretical work focuses on developing Bayesian priors for diverse applications and studying their theoretical foundations.

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Representative Submitted Papers​​​​

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• J. Fúquene-Patiño and Brenda Betancourt (2026). Regularized Small Area Estimation with Graph Laplacian Benchmarking priors. Submitted. 

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• J. Fúquene-Patiño (2025).  Fully Bayesian Spectral Clustering and Benchmarking with Uncertainty Quantification for Small Area Estimation.  Submitted. 

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• Sirapat Watakajaturaphon and J. Fúquene-Patiño (2026). On improving the estimates of the sampling variances via Global-Local priors in Small Area Estimation. Submitted. 

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   NEWS (2026)

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• I have posted a new preprint with Brenda Betancourt on arXiv titled: "Regularized Small Area Estimation with Graph Laplacian Benchmarking priors". We propose a family of priors induced by a benchmark-constrained regularization problem. The resulting family includes a Benchmarking Prior that incorporates the benchmarking restrictions without additional regularization across areas, and Single and Multi-View Laplacian Benchmarking Priors that introduce regularization through graph Laplacians constructed from area similarities based on external covariate information. We develop tailored MCMC algorithms based on a reduced parameterization of the constraint space, and apply the framework to estimate Average Household Size (AHS) at subnational level.

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• Invited Seminar Speaker, Instituto Nacional de Estadística y Geografía (INEGI) - National Statistical Office, Mexico — Multidimensional Poverty Seminar Series. September 2026.

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• I have posted a new preprint with Sirapat Watakajaturaphon on arXiv titled: “On improving the estimates of the sampling variances via Global-Local priors in Small Area Estimation”. In this work we propose a new Bayesian approach to improve the posterior estimation of the sampling variances in SAE. Our proposed model incorporates Global-Local (GL) priors to improve the level of shrinkage toward the posterior estimates obtained with the Generalized Variance Function. In this paper, we have theoretical, computational, and practical innovations.​​​

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• I presented my research at the Small Area Estimation Conference (SAE 2026) - Bucharest, Romania, 2026. 

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• ​Our UCD² seed grant, titled  FoodSpecAI with Claire Gormley (University College Dublin, Ireland) and  Stephen F. Brown‬‭ (AI Institute for Food Systems, UC Davis), has been funded. This project will develop Interpretable Artificial Intelligence tools to Analyse Spectroscopy Data. More information here: AI and One Health drive new UCD² funding for UCD–UC Davis research.

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• ​I was awarded Second Place for Best TidBit Session (Junior Faculty Category) at the 2026 Best of Statistical Science Workshop (BOSS 2026), organized by the Department of Statistics at Texas A&M.

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• CFE-CMStatistics 2026 (member of the Scientific Program Committee).

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  NEWS (2025)

 

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• I have posted a new preprint on arXiv titled: “Fully Bayesian Spectral Clustering and Benchmarking with Uncertainty Quantification for Small Area Estimation”. In this work, inspired by machine learning techniques, we propose a new Bayesian model for Small Area Estimation (SAE): "the Fay-Herriot model with Spectral Clustering (FH-SC)".   To enable benchmarking, we leverage the theoretical framework of Posterior Projections for constrained Bayesian inference. We propose a novel measure of uncertainty for the posterior benchmarked estimator: "the Conditional Posterior Mean Square Error (CPMSE)". The proposed methodology is motivated by a real case study involving the estimation of the proportion of households with internet access in the municipalities of Colombia.

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• The paper on: "Measurement Error Models with Global-Local Priors for Small Area Estimation" with Xueying Tang (University of Arizona)​​ has been accepted in Bayesian Analysis. 

   

• I presented my research and organized an invited session: “New machine learning and Bayesian techniques” at CFE-CMStatistics 2025

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• Our paper on building a "Population-Based Statistical Register (PBSR) in a Latin American National Statistical Office" to produce subnational estimates for official statistics has been published in the Journal of Official Statistics. The work addresses data collection challenges due to conflict and displacement and introduces efficient MCMC algorithms for rapid estimation. To our knowledge, this is the first PBSR of its kind in Latin America and the first to generate subnational Bayesian estimates in this context. View paper

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• Civil registration and vital statistics (CRVS) systems should be the primary source of mortality data for governments. Accurate measurement of the completeness of death registration helps inform interventions to improve CRVS systems and to generate reliable mortality indicators.​ The paper I wrote with Tim Adair (University of Melbourne) to produce estimates of the completeness of death registration under a Bayesian framework has been accepted in the Statistical Journal of the International Association for Official Statistics. I hope our method can be implemented in National Statistical Offices. View paper

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• I presented my research at two major statistical conferences this summer: Small Area Estimation Conference (SAE 2025) - Turin, Italy and 2025 International Indian Statistical Association Conference - University of Nebraska-Lincoln.  

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  NEWS (2024)

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• I organized two invited sessions at major international conferences: “New Bayesian alternatives for record linkage and small area estimation” at CFE-CMStatistics 2024, and “Bayesian and Frequentist Innovations for SAE” at Small Area Estimation Conference (SAE 2024). ​​​​​​​

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• CFE-CMStatistics 2024 (member of the Scientific Program Committee)

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• The paper on "Ultra-Sparse Small Area Estimation" with Brenda Betancourt (NORC at the University of chicago) has been accepted in  the Annals of Applied Statistics. The  paper can be found in the following link (Annals of Applied Statistics):​​

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• J. Fúquene-Patiño, and Brenda Betancourt (2024).  Ultra-Sparse Small Area Estimation With Super Heavy Tailed Priors for Internal Migration Flows​​​​​​​​​​. 

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Editorial Services 

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• Associate Editor, Journal of the American Statistical Association, Reviews, (2023 – 2025) - (2026-current).

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• Associate Editor, The American Statistician, Reviews, (2023 – 2025) - (2026-current).

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Selected recent papers

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• X. Tang and J. Fuquene-Patino, (2026). Measurement Error Models with Global-Local Priors for Small Area Estimation. Bayesian Analysis, Forthcoming.

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• J. Fuquene-Patino, Mendoza, A., Cristancho, C. and Ospina, M., (2025). A Bayesian Approach to Produce Subnational Population Estimates Using a Population Base Statistical Register. Journal of Official Statistics, 41(1), pp.172-201.

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• J. Fúquene-Patiño and Adair, T., (2025). A Bayesian approach to estimate the completeness of death registration. Statistical Journal of the International Association for Official Statistics (IAOS), 41(2), pp.421-428.

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• J. Fúquene-Patiño and Betancourt, B., (2025). Ultra-sparse small area estimation with super heavy-tailed priors for internal migration flows. The Annals of Applied Statistics, 19(1), pp.121-146.

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• J. Fúquene-Patiño, C. Cristancho,  M, Ospina and D. Morales (2021).  Fay-Herriot Model-Based Prediction Alternatives for Estimating Households with Emigrated Members. (2021). Journal of Official Statistics. 

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• J. Fúquene-Patiño, M. Steel and D, Rosell (2019). On choosing mixture components via non-local priors.  81, 5, 809-837. Journal of the Royal Statistical Society, Series B .

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Publications (Prior to 2019)

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• J. Fúquene-Patiño  and  Brenda Betancourt and João B. M. Pereira (2018). A weakly informative prior for Bayesian dynamic model selection with applications in fMRI. Journal of Applied Statistics 45 (7), 1173-1192.

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• J. Fúquene-Patiño (2015) A Semi-parametric Bayesian Extreme Value Model using a Dirichlet Process Mixture of Gamma densities. Journal of Applied Statistics, Volume 42, Number 2, 1 February 2015, pp. 267-280 (14).​

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• J. Fúquene-Patiño, M Alvarez and LR Pericchi (2015). A Robust Bayesian dynamic linear model for Latin-American economic time series:“the Mexico and Puerto Rico cases". Latin American Economic Review, Springer, 24 1-17

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• J. Fúquene-Patiño, M. E. Perez, and L. R. Pericchi (2014).  An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models Brazilian Journal Of Probability and Statistics. Vol. 28, No. 2, 288–299.

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• J. Fúquene-Patiño  and  Brenda Betancourt (2011). Heavy tailed priors: an alternative to non-informative priors in the estimation of proportions on small areas. Biometric Brazilian Journal 29 (3).

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• J. Fúquene-Patiño, M. Delgado (2011).  A note on Bayesian Robustness for Count Data. Brazilian Journal Of Probability and Statistics. Vol. 26, No. 3, 279-287.​​​

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• Delgado, M., Portnoy, A., J. Fúquene-Patiño. "Controlling the Oscillations of a Variable Length Pendulum". Biometric Brazilian Journal

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• J. Fúquene-Patiño, J. D. Cook and L.R. Pericchi (2009). Skeptical and optimistic robust priors for clinical trials. Colombian Journal of Statistics, 34 (2), 333-345.

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• J. Fúquene-Patiño (2009) Robust Bayesian priors in clinical trials: An R package for practitioners. Biometric Brazilian Journal 27, 627-643

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• J. Fúquene-Patiño, J. D. Cook and L.R. Pericchi (2009) A Case for Robust Bayesian Priors with Applications to Clinical Trials,  Bayesian Analysis, pp. 817 - 846.

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• J. Fúquene-Patiño  (2005) Dico-ratio estimation with auxiliary information in 3X3 contingency tables. Colombian Journal of Statistics, Volume 28, Number 2,  pp. 141.

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• J. Fúquene-Patiño and Leonardo Bautista (2005) P.P.S. Design with Categorical Variables to Estimate Dicho-Ratios. Colombian Journal of Statistics, Volume 28, Number 1,  pp. 99-114.

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