The Statistical Learning Laboratory (SaLLy) is a hub for statistical and data science research, training, and consultancy. It brings together the research group of Professor Paulo Canas Rodrigues, including some of the main collaborators and students. Please visit our website at www.SaLLy.ufba.br and get involved with our projects.
Some of the research from the SaLLy group can be found in our Google Scholar profile.
Paulo Canas Rodrigues
Time Series and Singular Spectrum Analysis
Time series analysis and forecasting is one of the major research topics in statistics and widely used in many areas of application. Singular spectrum analysis (SSA) is a widely used non-parametric method for model fit and model forecasting. Under this topic, we are develop new methodologies, generalize and adapt current methods, and perform real data applications, for model fit and model forecasting.
This represents one of the core research topics at SaLLy. The main collaborators for this topic are Rahim, Olawale, Mohammad and Valdério. The students actively involved are Carlo, Patrick and Jonatha.
Quantitative and Statistical Genetics
The study and understanding of genotype-by-environment interactions and of QTL-by-environment interaction are of key importance in agronomy and plant sciences. Under this topic we develop, generalize and adapt statistical models (e.g. AMMI and GGE) to better model the data collected from multi-environment trials. We are also interested in genotype-to-phenotype crop growth models and QTL detection.
This represents one of the core research topics at SaLLy. The main collaborators for this topic are Jakub and Vanda. The students actively involved are Tatiana, Inês, Everton and Isaac.
Neural Networks for Time Series Analysis
Artificial neural networks are of key importance to learn and model complex and non-linear relationships. Under this topic we are interested in applying neural networks for time series forecasting and time series clustering, and to develop hybrid algorithms.
This represents one of the core research topics at SaLLy. The main collaborator for this topic is Winita. The students actively involved are Javier, Patrick, Rafael, Renan, Jonatha and Camila.
Statistics in Sports
Sport analytics are of great importance to study the behavior of athletes and teams and its proper use can provide competitive advantages. In this topic we are interested in using supervised and unsupervised statistical learning techniques to better understand and model data from team sports such as basketball.
The student actively involved in this project is João.
Detrended Fluctuation Analysis
Detrended fluctuation analysis (DFA) is very useful to quantify the long-range auto-correlation in non-stationary time series. In this topic we are interested in adapting and developing new methodology related to DFA for long memory processes. One of the main interests in terms of application is related to the study of EEG (electroencephalogram) signals.
The student actively involved in this project is Victor.
High Dimensional Data Analysis
High dimensional data analysis has become increasingly frequent and important in diverse fields of sciences, engineering, and humanities, ranging from genomics and health sciences to economics, finance and machine learning. Analysis of high-dimensional data calls for new statistical methodologies and theories. Under this topic, we are developing new methodologies, generalize and adapt current methods, for regression, classification and clustering with high dimensional features.
The main collaborator for this topic is Mohammad. The student actively involved in this project is Marla.
Activities at SaLLy