About me


I hold a DSc in Plant Pathology (Univ. Federal de Pelotas, 2004) with 2-year PostDoc training in risk assessment and prediction (Yang Lab, Iowa State University). Currently, I am Associate Professor at the Universidade Federal de Viçosa (UFV) where I have both teaching and mentoring (masters and doctorate) responsibilities. You can get access to my peer-reviewed publications in my academic profiles: ORCID, publons, Google Scholar, ResearchGate.


I am associate Professor leading a research Laboratory focused on the study of plant disease epidemics. We make use of statistical and mathematical models as well as field, greenhouse and laboratory experimental work.

The computational work is non pathogen-specific and covers a broader range of topics in plant disease epidemiology, including: 1) assessment of disease intensity and crop loss; 2) modeling temporal progress and spatial spread across scales; 3) assessment and prediction of disease risk, and 3) conducting systematic reviews and meta-analysis.

The experimental research is focused on genotyping and phenotyping of fungal pathogen populations. These include assessment of several traits such as: reproduction, pathogenicity, aggressiveness, toxigenic potential and fungicide resistance. Currently, we study two of the most dangerous plant pathogens worlwide: mycotoxigenic Fusarium that cause head blights or ear rots in winter and summer crops and Pyricularia blast of rice, wheat and grasses.


I’ve been involved in scholarly peer-review since early in my career. I’ve served as Associate Editor for a few journals, including Plant Disease. Currently, I am Editor in Chief for Tropical Plant Pathology, the journal of the Brazilian Phytopathological Society.

Open Science

I strongly advocate for an open and reproducible research model and culture that may ultimate contribute to a more accessible, transparent and reliable science. This led me to co-found Open Plant Pathology Initiative together with Adam Sparks. In my Lab, we make use of R language and Tidyverse for all statistical and data science related activities. All data and computational codes produced during our research are made available ahead of peer-review. You can find our code on GitHub. The research outcomes are organized and shared as a research compendium and preprints of manuscripts are archived in Open Science Framework (OSF).


If you see mistakes or want to suggest changes, please create an issue on the source repository.