Learn more about our research areas

The research and research projects at the Statistical Learning Laboratory (SaLLy) include both methodology and applications. We are particularly interested in high-dimensional modeling, time series analysis and forecasting, spatio-temporal modeling, sports analytics, quantitative and statistical genetics, machine learning, neural networks, artificial intelligence, and general statistical learning methodologies with application to many disciplines, including economics, finance, environment, energy, public policy, business, and industry.

SaLLy is open to establishing collaborations with other research groups locally, nationally, and internationally. Research collaboration includes data analysis support related to master's or PhD thesis and research projects, in all disciplines. Contact us at SaLLy.Laboratory@gmail.com for more information.

Deep Learning &

Creation of machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

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Big Data

Extremely large and complex data sets that require advanced computational and analytical methods to process and extract insights.

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Machine Learning

The use of statistical and mathematical techniques to enable machines to learn from data and improve their ability to perform specific tasks.

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The application of statistical and machine learning techniques to sports data to gain insights into player performance, team strategies, and game outcomes.

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Time Series Forecasting

The application of statistical and machine learning techniques to analyze time-stamped data and make predictions about future values of a variable.

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Spatio-temporal Modeling

The development of mathematical models and algorithms to analyze and predict patterns in data that vary in both space and time.

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Statistical Genetics

The use of statistical methods to analyze genetic data and gain insights into the genetic basis of complex traits and diseases.

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Open calls for postdoctoral studies at SaLLy. The activities can be conducted locally at the Federal University of Bahia or remotely.

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The complete list of publications from SaLLy members can be found in our Google Scholar profile.