Danilo Marcondes Filho

marcondes.danilo@gmail.com

Professor Danilo has academic experience in Industrial Statistics, focusing on Statistical process control (SPC). He works at developing theoretical aproaches based on statistical techniques in a SPC context with processes that generate data with different characteristics, such as: overdispersed/zero-inflated data; high dimension multivariate data; multivariate time series data.

Eduardo de Oliveira Horta

eduardo.horta@ufrgs.br

Eduardo Horta is an Adjoint Professor and Early Career Researcher in the Department of Statistics at the Federal University of Rio Grande do Sul, Brazil. He holds a PhD in Economics from the same University (2015), having spent a season as Visitor Graduate Student (Sandwich Doctorate) at Queen Mary University of London (2013). His research interests include functional time series, stochastic processes, financial econometrics, nonparametric methods and quantile regression.

Fabio Mariano Bayer

bayer@ufsm.br

Professor Fábio Bayer researches statistical computing methods, his main interest fields of research are: bootstrap, classical inference, dynamical models, regression models and statistical signal processing.

Flávio Augusto Ziegelmann

flavioaz@mat.ufrgs.br

Professor Flávio Augusto Ziegelmann holds a degree in Statistics from Universidade Federal do Rio Grande do Sul (UFRGS, 1994), Master’s Degree in Statistics from Universidade de São Paulo (USP, 1996) and a PhD in Statistics from University of Kent at Canterbury (UKC – UK, 2002). He is also I am the president of the Brazilian Statistical Society, vice-director of the Institute of Mathematics and Statistics at UFRGS, as well as the coordinator of the graduation programme in Statistics and permanent member of the graduation programme in Economics at UFRGS.

Gabriela Bettella Cybis

gcybis@yahoo.com.br

Professor Gabriela Betella Cybis holds a bachelor’s degree in Biology (Universidade Federal do Rio Grande do Sul, 2007), M.S in Mathematics, with emphasis in probability and statistical mathematics (Universidade Federal do Rio Grande do Sul, 2009), and a PhD in Biomathematica (University of California, Los Angeles, 2010). She works with Bayesian phylogenetics methods, and has interest in developing statistical methods for genetics, evolutionary biology and epidemiology, focusing on computational statistics. Also, she has been working with non-parametric methods for Inference in Clustering.

Guilherme Pumi

guipumi@gmail.com

Professor Guilherme Pumi holds a degree in Mathematics from the Universidade Regional Integrada do Alto Uruguai e das Missões (2002), M.S. in Mathematics from the Federal University of Rio Grande do Sul (2006), M.S. in Statistics from the University of California at Davis (2009) and Ph.D. in Mathematics from the Universidade Federal do Rio Grande do Sul (2012) and was a Postdoctoral fellow at the Universidade Federal do Rio Grande do Sul. He is currently Adjoint Professor at the Universidade Federal do Rio Grade do Sul. Guilherme has experience on Mathematical Statistics, acting on the following subjects: asymptotic theory, bounded time series modeling, long-range dependence, parametric and semiparametric estimation of time series, chaotic processes and copulas.

Hudson da Silva Torrent

hudson.torrent@ufrgs.br

Professor Hudson Torrent has experience working with Econometrics and Time Series, mainly researching the following subjects: Nonparametric frontier estimation, nonparametric regression, semiparametric regression and Time Series forecasting.

Marcio Valk

marciovalk@gmail.com

Professor Marcio Valk holds a bachelor’s degree in Mathematics from Universidade Federal do Rio Grande do Sul (2004). A M.S in Mathematics, with emphasis in Probability and Statistics, from Universidade Federal do Rio Grande do Sul (2007). He also holds a PhD in Statistics from Universidade Estadual de Campinas (2011). He is an Adjoint Professor in the Department of Statistics at the Federal University of Rio Grande do Sul. Professor Marcio has research projects in the area of Hierarchical Clustering Inference and U-Statistics Methods for Clustering Time Series.