CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Bachelor's degree (1998) and a master's degree (2003) in Statistics from the State University of Campinas (1998), a master's degree in Statistics with an emphasis on Biostatistics from the University of Wisconsin-Madison (2003) and a doctorate in Quantitative Genetics and Animal Breeding also from the University of Wisconsin -Madison (2006). Since 2007, he has been a professor at the Department of Statistics at the State University of Campinas, being an Associate Professor level 1 (MS-5.1). He has experience in the area of #8203;#8203;Probability and Statistics, with an emphasis on regression models, with various applications in genetic statistics. She was treasurer of ISBra, the Brazilian chapter of the ISBA International Society for Bayesian Analysis in the 2019-2020 Biennium. As part of her administrative duties at UNICAMP, she is currently the Postgraduate Coordinator, and was Head (2019-2021) and Deputy Head (2013-2015; 2017-2019) of the Department. She has been a member of the Caucus for Women in Statistics of the International Biometric Society (IBS) Conference since 2019. (Source: Lattes Curriculum)
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This Project has application in several problems related to mixed and mixture regression models with applications in different areas. The Project consists of 4 subprojects, involving the following studies: (1) incorporation of functional covariates in Semi-Parametric Regression models for unsupervised learning purposes and with application in two sets of data, one of them associated with …
The literature in plant breeding, such as for grasses like sugarcane and forage, indicates that conducting genetic improvement for multiple phenotypes simultaneously has an advantage over individual and separately modeled phenotypes. However, a common practice is to model multiple phenotypes through a multivariate Gaussian distribution, introducing inferential bias when the variable does …
In this study, the objective is to model the probability of disease in animals using logistic regression models with scalar and functional variables. For this, generalized additive models will be considered. The data to be used in this project are part of a base of Cornell University's Dairy Cattle Biology and Management laboratory.
| 4 / 3 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 1 / 1 | Completed scholarships in Brazil |
| 6 / 5 | All research grants and scholarships |
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