Marcela Alfaro Córdoba

Department of Statistics

UC Santa Cruz

Santa Cruz, California

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.

My Research Journey

Since then, my work has taken me all over the place! I’ve collaborated on projects involving:

Spatial-temporal statistical methods for climate models
Design of experiments applied to biophysics
Functional data analysis applied to spectrophotometry
Reproducibility and open science

What I love most is working with researchers from different fields and helping them tackle their data and statistical challenges.

Teaching & Program Development

These days, I’m focused on two main areas:

Statistical Collaboration: Being the go-to statistician for various research groups.

Education Innovation: Reimagining how we teach statistics and data science. I led the charge to create our new Statistics and Data Science degree program at UCSC, and I’ve been redesigning intro courses and creating new classes on data science methods and data ethics.

Current Big Project: ReproStats Lab

My current major work these days is securing funding for the ReproStats Lab: a space where undergrads and grad students learn by actually reproducing complex statistical studies. I’m passionate about connecting data ethics with reproducibility and training the next generation of scientists to be both smart consumers and responsible producers of statistical work.

My Mission

Everything I do centers around making data science and statistics more open, accessible, reproducible, and responsible.

selected publications

Aridity Trends in Central America: A Spatial Correlation Analysis

Marcela Alfaro-Córdoba, Hugo G. Hidalgo, and Eric J. Alfaro

Atmosphere, 2020

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This paper analyzes temporal trends in aridity, runoff, precipitation, temperature, and potential evapotranspiration across Central America (1970-1999), introducing a spatial correlation methodology to identify extreme trend outliers and assess regional climate change significance.
Ten simple rules to host an inclusive conference

Rocío Joo, Andrea Sánchez-Tapia, Sara Mortara, and 5 more authors

PLOS Computational Biology, 2022

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This editorial provides practical guidelines for organizing inclusive conferences based on lessons learned from the 2021 useR! statistical computing conference, addressing barriers to participation and promoting diversity across multiple dimensions.
Spatio-temporal downscaling emulator for regional climate models

Luis A. Barboza, Sy-Wei Chou Chen, Marcela Alfaro Córdoba, and 2 more authors

Environmetrics, 2023

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This paper describes how to use a spatio-temporal statistical model with varying coefficients as a downscaling emulator for regional climate models, offering a computationally efficient alternative to traditional dynamical downscaling methods.
Central American climate extreme trends: A statistical analysis of CLIMDEX indices

Marcela Alfaro-Córdoba, Natalia P. Mora-Sandí, Hugo G. Hidalgo, and 1 more author

International Journal of Climatology, 2024

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This paper analyzes precipitation and temperature extreme trends in Central America using CLIMDEX indices, employing novel statistical techniques that account for spatial correlation to assess trend significance and detect climate change patterns from 1981-2020.
Building Responsible and Sustainable Open Data Literacy Skills for Early Career Researchers: A Decade of the SoRDS Programme

Sonal Gandhi, Sarah Diggs, Marcela Alfaro Córdoba, and 9 more authors

Data Science Journal, 2026

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This paper documents a decade of the Schools of Research Data Science (SoRDS) programme, a CODATA-RDA initiative that builds foundational data science and open data skills for early-career researchers in low- and middle-income countries through a sustainable Train-the-Trainer model.