Research and Development (R&D)
This section is dedicated to the main research topics developed in the GIA.
Machine Learning
Research topic that studies and develops algorithms capable of solving problems through pattern recognition using the theory of computational learning. These algorithms are capable of representing knowledge through inductive learning.
Data Mining
Research line that studies algorithms capable of extracting patterns from data to aid in decision-making and knowledge discovery. Many of the algorithms studied by this line have a statistical or intelligent bias.
Natural Language Processing
Research topic that combines artificial intelligence and linguistics for the processing and analysis of natural languages. In the research group, there are studies related to text classification, topic modeling, named entity recognition and linking, information extraction, language models, text segmentation, sentiment analysis, creation and annotation of text corpora, natural language inference, among others.
Computer Vision
Research topic that encompasses the processing and extraction of relevant visual properties from digital images. The research involves feature extraction, pattern and object recognition, image classification, optical character recognition, content-based image retrieval, semantic segmentation, among others.
Data Visualization and Visual Analytics
Research line that aims to formulate visual and process-based metaphors for knowledge discovery from abstract data. In GIA, there are ongoing research projects in the proposition of visual representations, multidimensional projections (including dimensionality reduction techniques), as well as visualization-based analytical processes, exploratory data visualization, user interaction techniques, among others.
Database and Big Data
Research line that aims to study data models, architectures, and NoSQL database languages, data provenance in cloud computing environments, architecture and analysis of massive unstructured databases, metadata management in big data, and NoSQL Databases in Bioinformatics.
Bioinspired Computing
Area of Computer Science that employs models inspired by biology and nature. In the group, research in this area is related to evolutionary computation and genetic algorithms.