The Statistical Learning Laboratory (SaLLy) brings together my research group, collaborators and students. SaLLy focuses on the development and application of statistical learning methodologies to transform data into information.

The projects at SaLLy include both methodological and applied research. We are particularly interested in high-dimensional statistical models, time series analysis and forecasting, quantitative genetics and general statistical learning methodologies with application to many disciplines.

Some of the research from the SaLLy group can be found in our Google Scholar profile.

People at SaLLy

Head and Principal Investigator

Collaborators and Affiliate Members

Vanda Lourenço (Portugal)

Olawale Awe (Nigeria/Brazil)

Winita Sulandari (Indonesia)

Jakub Paderewski (Poland)

Valderio Reisen

(Brazil/France)

Students

Tatiana Assis

PhD Candidate 

(ESAQ/USP)

Carlo Solci

PhD Candidate 

(UFES)

Inês Gomes Mota

PhD Candidate 

(Portugal)

Javier Linkolk

PhD Candidate (UV/Chile)

Everton Costa

MSc student
(UFBA)

Victor Barreto

BSc student 

(UFBA)

Rafael Araújo

BSc student 

(UFBA)

Patrick Messala

BSc student 

(UFBA)

João Vitor Silva

BSc student 

(UFBA)

Renan Bispo

BSc student 

(UFBA)

Jonatha Pimentel

BSc student 

(UFBA)

Isaac de Oliveira

BSc student 

(UFBA)

Marla Lorrani
BSc student
(UFBA)

Camila Braz

BSc student 

(UFBA)

Kim Leone Silva

BSc student 

(UFBA)

Research at SaLLy

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.

From Mahmoudvand et al. (2017)

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

Contact

If you need to contact us, please send and email to Sally.Laboratory@gmail.com