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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Francisco Louzada is a Full Professor at the University of São Paulo, affiliated with the Institute of Mathematical and Computer Sciences. He is the Director of the Center for Mathematics and Statistical Computing Applied to Industry (CeMEAI), Director of Technology Transfer at CEPID-CeMEAI, Coordinator of the Risk Studies Laboratory (CER-USP), Editor of the journal Sankhya A and the book series SpringerBriefs in Statistics - BSA (Springer), member of the Advisory Board of the journals Journal of Applied Statistics and Revista Brasileira de Biometria, and Associate Editor of the journals Brazilian Journal of Probability and Statistics, Communication in Statistics, Quality Tech. Quantitative Management (QTQM), Journal of Statistical and Econometric Methods, and Revista Brasileira de Estatística. Louzada served as President of the Brazilian Statistical Association (ABE) for two terms, Editor of the Fisher Project of Statistical Books series (Blucher), and was a member of the Mahalanobis Award Committee for the 2021 and 2023 awards of the ISI (International Statistical Institute), of which he is an elected member. Francisco Louzada holds a PhD in Statistics from the University of Oxford (1998), a Masters degree in Computer Science and Computational Mathematics from the University of São Paulo (1991), and a Bachelor's degree in Statistics from the Federal University of São Carlos (1988). He works mainly in the following areas of Data Science and Statistics: Survival Analysis and Reliability, Machine Learning, Inference, and Risk Models. (Source: Lattes Curriculum)
| Articles published in Pesquisa FAPESP Magazine about the researcher: |
| Estrategia de combate |
| Combat strategy |
| Wood identification |
| Para identificar maderas |
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| System forecasts hospital demand for personal protective equipment |
| Mathematics can help companies innovating |
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The purpose of this post-doctoral project is to develop different estimation methods in sta- tistical inference. Considering the classical approach, we will introduce a new estimation pro- cedure based on a modification of the maximum likelihood estimators (MLE) that allow us to obtain closed-form estimators. For this new method sufficient and necessary conditions will be studied to obtai…
O tema principal da reunião está centrado na necessidade de se promover e exercitar a interdisciplinaridade entre a Ciência Estatística e outras áreas do conhecimento, incluindo o desenvolvimento metodológico e as aplicações. A programação será elaborada abordando-se temas que atrairão pesquisadores, não somente da área de Estatística, mas também profissionais de áreas correlatas, que uti…
This project willlook at developing new statistical models for survival analysis that exploit the analytical and numerical properties of an overlooked key function: the cumulative hazard function. These analytical properties are very powerful and well understood from the convex analysis point of view but, perhaps surprisingly, they have not been so far exploited for statistical modelling …
(Only some records are available in English at this moment)
This Master's research proposal focuses on pioneering the use of Bayesian Neural Networks (BNNs) for Conditional Average Treatment Effect (CATE) estimation and on improving the use of the Bayesian Additive Regression Tree (BART) model for CATE estimation, leveraging the framework proposed by Hahn et al. (2020). It addresses the notable gap in literature concerning BNN applications in CATE…
Em uma sociedade conectada a produção de dados é massiva e desenfreada. Sistemas de captação de dados surgem a todo momento, buscando atender as necessidades de ações personalizadas, utilizando os dados disponíveis. De marketing a ações de engajamento ambiental, de finanças a divulgação de esporte, os sistemas de captação de dados estão lá, transformando dados em conhecimento. Neste conte…
The purpose of this post-doctoral project is to develop different estimation methods in sta- tistical inference. Considering the classical approach, we will introduce a new estimation pro- cedure based on a modification of the maximum likelihood estimators (MLE) that allow us to obtain closed-form estimators. For this new method sufficient and necessary conditions will be studied to obtai…
(Only some records are available in English at this moment)
| 13 | Completed research grants |
| 13 | Completed scholarships in Brazil |
| 26 | All research grants and scholarships |
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