About

I’m an Associate Professor of Teaching in the Department of Statistics at UC Santa Cruz. I completed my PhD at NC State, where I specialized in spatial-temporal statistics and climate science applications — basically figuring out how to make sense statistically of weather and climate data across time and space.

Research topics

• Environmental science
• Geography
• Mathematics
• Geology
• Environmental resource management
• Ecology
• Environmental protection
• Statistics
• Climatology

Selected publications

• Building Responsible and Sustainable Open Data Literacy Skills for Early Career Researchers: A Decade of the SoRDS Programme

  Data Science Journal · 2026-01-01

  articleOpen access

  In today’s data-centric research environment, effective data literacy is essential for ensuring data usability, integrity, and reproducibility. The Schools of Research Data Science (SoRDS), launched in 2016 in partnership with the Committee on Data (CODATA) and the Research Data Alliance (RDA), marks a decade of impactful training and capacity-building for early-career researchers (ECRs) from low- and middle-income countries (LMICs) with its tenth anniversary reached in August 2025. This paper examines its distinctive, holistic approach to equipping researchers with core competencies in data science, open science, and research data management (RDM). Unlike traditional programmes focused solely on technical skills, SoRDS integrates principles of data ethics, reproducibility, data stewardship, and interdisciplinary collaboration into its curriculum. Central to its mission is the ‘Train-the-Trainer’ model, which empowers participants to become instructors and regional leaders, creating a sustainable and scalable community of practice. SoRDS not only provides technical training but also fosters a culture of openness and inclusivity, ensuring that the benefits of the data revolution reach underserved research communities. Over the past decade, the schools have been hosted in diverse regions, adapting content to local contexts and creating a strong global network of alumni, mentors, and institutions. Crucially, SoRDS advances an ‘RDM in context’ approach, prioritizing what is practical, relevant, and achievable in low-resource settings. SoRDS tailors its training to the realities of LMICs, making its content more applicable, sustainable, and impactful for these communities. Drawing on a decade of documentation, this paper provides a retrospective synthesis of SoRDS’ development, global expansion, alumni impact, and lessons learned, situating these within the broader training landscape. In particular, it draws comparisons with other training activities, noting the specific niche SoRDS has in this landscape. Finally, it outlines priorities for the programme’s next stage. The primary focus of this paper is to provide a reflective, evidence-based account of SoRDS: its historical development, its unique pedagogical model, its global expansion, and the lessons learned over a decade of implementation in low- and middle-income research environments.

  PublisherOA PDFDOI

• Central American climate extreme trends: A statistical analysis of <scp>CLIMDEX</scp> indices

  International Journal of Climatology · 2024-07-19 · 10 citations

  articleOpen access1st authorCorresponding

  Abstract Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

  PublisherOA PDFDOI

• Foundational Data Science Training for Health Equity Researchers at Minority Serving Institutions: A SoRDS Event

  2023-06-26 · 1 citations

  article

  Contemporary research, particularly when addressing the most significant transdisciplinary research challenges, cannot effectively be done without a range of skills relating to data management, data analysis, and cyberinfrastructure (CI). These data and CI skills are common to all disciplines that conduct data-centric research. Research Data Science acts as a vital component of the scientific process. In a grassroots attempt to address this gap, the CODATA-RDA Schools of Research Data Science (SoRDS) was founded in 2016 to provide instruction on foundational data science and open research concepts to early career researchers in low and middle-income countries. This partnership between international collaborators has since 2016 provided this training to over 1000 early career researchers in 24 events in 10 countries worldwide. The most recent event was held at Georgia Institute of Technology and focused on health equity and included researchers from minority-serving institutions in the southeast United States. This paper covers the background of the SoRDS project along with organization and curriculum details. It also covers the transition of the events from a non-domain-centric curriculum to spotlighting biological and social health equity data and what we learned to make future health-related events more engaging and valuable to the attendees. It also looks toward future events that will serve international students studying health informatics and other data-centric disciplines.

  PublisherDOI

• Spatio‐temporal downscaling emulator for regional climate models

  Environmetrics · 2023-06-12 · 6 citations

  articleOpen accessCorresponding

  Abstract Regional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio‐temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non‐stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.

  PublisherOA PDFDOI

• Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

  Food Chemistry · 2023-08-02 · 35 citations

  article
  PublisherDOI

• Spatio-temporal Downscaling Emulator for Regional Climate Models: a Comparative Study

  arXiv (Cornell University) · 2022-06-08

  preprintOpen access

  Regional Climate Models (RCM) describe the meso scale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from General Circulation Models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of computer time more than statistical climate downscaling. In this paper we describe how to use a spatio-temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using varying coefficients. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non-stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.

  PublisherOA PDFDOI

• Costa Rican wetlands vulnerability index

  Progress in Physical Geography Earth and Environment · 2022 · 22 citations

  • Geography
  • Environmental resource management
  • Environmental protection

  Costa Rica comprises approximately 6% of the world’s biodiversity. Among these lush ecosystems, wetlands are represented in mangrove forests near the sea, along river lowlands, sedimentary and volcanic mountains, and highland páramo landscapes. In 2018, the Ministry of Environment and Energy (MINAE), through the National System of Conservation Areas (SINAC), the United Nations Development Program (UNDP), and the Global Environment Facility (GEF) carried out the new National Wetlands Inventory (NWI) which identified 10,699 wetland polygons. This assessment collected key information such as location, characteristics of the wetland, land use in the vicinity, threats, and other generalities. Based on these valuable results, we propose a wetland Vulnerability Index composed of a Condition Index and a Hazard Index to determine the different vulnerability conditions of each wetland unit. Our findings provide a better comprehension of the status of wetlands in Costa Rica with an environmental geography perspective. Located in a climate change hotspot, Costa Rica’s conservation policies and actions should consider how to manage the most vulnerable wetlands at different scales. This methodology can improve and generate regional and national wetlands inventories as a basis for evidence-based decision making in other latitudes.

  PublisherOA PDFDOI

• Tabular summary for the Costa Rican wetlands vulnerability index

  Zenodo (CERN European Organization for Nuclear Research) · 2022-11-06

  datasetOpen access

  The table summarizes the data inputs used to generate the cartography the "Costa Rican wetlands vulnerability index" paper (DOI: https://doi.org/10.1177/03091333221134189). The table complements the shape for Costa Rican wetlands. It includes information for each one of the 10.669 wetland polygons of Costa Rica, according to the National Wetlands Inventory of Costa Rica generated by UNPD (DOI: http://dx.doi.org/10.13140/RG.2.2.10529.48485) regarding CI, HI, VI, area in hectares, if it is inside/outside/partially within Protected Areas (PA), the type of wetland and the name and unique ID (Form) of each polygon.

  PublisherDOI

• Tabular summary for the Costa Rican wetlands vulnerability index

  Zenodo (CERN European Organization for Nuclear Research) · 2022-11-06

  datasetOpen access

  The table summarizes the data inputs used to generate the cartography the "Costa Rican wetlands vulnerability index" paper (DOI: https://doi.org/10.1177/03091333221134189). The table complements the shape for Costa Rican wetlands. It includes information for each one of the 10.669 wetland polygons of Costa Rica, according to the National Wetlands Inventory of Costa Rica generated by UNPD (DOI: http://dx.doi.org/10.13140/RG.2.2.10529.48485) regarding CI, HI, VI, area in hectares, if it is inside/outside/partially within Protected Areas (PA), the type of wetland and the name and unique ID (Form) of each polygon.

  PublisherDOI

• Ten simple rules to host an inclusive conference

  PLoS Computational Biology · 2022-07-21 · 23 citations

  editorialOpen access

  Conferences are spaces to meet and network within and across academic and technical fields, learn about new advances, and share our work. They can help define career paths and create long-lasting collaborations and opportunities. However, these opportunities are not equal for all. This article introduces 10 simple rules to host an inclusive conference based on the authors' recent experience organizing the 2021 edition of the useR! statistical computing conference, which attracted a broad range of participants from academia, industry, government, and the nonprofit sector. Coming from different backgrounds, career stages, and even continents, we embraced the challenge of organizing a high-quality virtual conference in the context of the Coronavirus Disease 2019 (COVID-19) pandemic and making it a kind, inclusive, and accessible experience for as many people as possible. The rules result from our lessons learned before, during, and after the organization of the conference. They have been written mainly for potential organizers and selection committees of conferences and contain multiple practical tips to help a variety of events become more accessible and inclusive. We see this as a starting point for conversations and efforts towards building more inclusive conferences across the world. * Translated versions of the English abstract and the list of rules are available in 10 languages in S1 Text: Arabic, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Tamil, and Thai.

  PublisherOA PDFDOI

Frequent coauthors

• Néstor Veas-Ayala

  Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud

  6 shared

• Adolfo Quesada‐Román

  6 shared

• Ana‐Maria Staicu

  North Carolina State University

  5 shared

• Matthieu Ménager

  Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale

  5 shared

• Louise Bezuidenhout

  4 shared

• Marcela Hernández-Jiménez

  Universidad Pablo de Olavide

  4 shared

• Daniele Marin

  Duke Medical Center

  3 shared

• Arnab Maity

  North Carolina State University

  3 shared

Education

• Ph.D., spatial-temporal statistics and climate science applications

  NC State