Computational Intelligence is one of the areas of Artificial Intelligence that deals with automatic knowledge acquisition. Our group is focused on the theory, application, and development of computational intelligence methods.
Building systems that can automatically acquire knowledge and make intelligent decisions in complex scenarios. We explore both symbolic and sub-symbolic approaches to AI.
Developing and applying learning algorithms for pattern recognition, prediction, and data-driven discovery. Our work includes both supervised and unsupervised methods.
Extracting actionable insights from large-scale datasets and temporal systems. We focus on the complete pipeline from data collection to knowledge extraction.
Leveraging deep neural network architectures for complex representation learning, including applications in vision, natural language, and structured data.
Theory, models, and algorithms for different optimization problems. We study both single- and multi-objective combinatorial optimization using evolutionary and hybrid approaches.
Analyzing complex dynamical systems and chaos theory for understanding temporal patterns and predicting system behavior.
We apply our computational intelligence and optimization methods to solve real-world problems in diverse domains.
Applying computational intelligence to medical diagnostics, clinical decision support, and healthcare data analysis.
Leveraging AI and machine learning for biological data analysis, genomics, proteomics, and molecular research.
Using data-driven and optimization methods for earth sciences, environmental modeling, and geospatial analysis.
The LABIA research group is a team at the Department of Computer Science from the Institute of Computing, Federal University of Bahia, Brazil, created with the purpose of developing high-quality research in several complementary domains.
We study automatic knowledge acquisition; theory, models, and algorithms for different optimization problems; analysis of big data and temporal systems; and intelligent allocation of computing resources.
The group is focused on the investigation and development of methods to address both complex real-world and theoretical problems to which traditional computational approaches cannot be applied very successfully.
We combine expertise from computational intelligence, optimization, and data science to tackle problems that span multiple disciplines.
Our faculty has research experience at institutions across Canada, China, and Spain, fostering a global perspective in our work.
We welcome MSc., PhD, and Postdoc students interested in joining our research. Check our admission program for details.