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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Antonio R. S. Parmezan received a Ph.D. degree in Computer Science and Computational Mathematics from the Institute of Mathematics and Computer Science at the University of São Paulo - USP -, São Carlos, Brazil (2022). From December 2018 to May 2019, he was a visiting Ph.D. student with the Department of Computer Science at the University of Helsinki - UH -, Helsinki, Finland. Antonio holds an M.Sc. degree in Computer Science and Computational Mathematics from USP (2016) and a B.Sc. in Computer Science from Western Paraná State University - UNIOESTE -, Foz do Iguaçu, Brazil (2012). He is currently a postdoctoral researcher at USP and a scientific collaborator at two research centers: Laboratory of Computational Intelligence - LABIC - at USP and Laboratory of Bioinformatics - LABI - at UNIOESTE. His research interests include data mining, machine learning, data science, time series analysis, data stream processing, feature selection, metalearning, and machine vision. Antonio has also contributed as a referee to national and international scientific events and reputable journals, such as Machine Learning and Data Mining Knowledge Discovery. (Source: Lattes Curriculum)
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Many challenges faced in data mining and knowledge discovery are related to data classification. This task has been explored in various applications, such as microorganism identification based on image analysis of cultures grown in Petri dishes. Studies on this topic have demonstrated considerable predictive performance, with error rates below 15% for image collections containing more tha…
Technological advances have driven the development of increasingly larger and more complex image databases, thus increasing the demand for effective image retrieval methods that meet users' needs. Over the past two decades, researchers have extensively studied content-based image retrieval for various applications, except for microbial culture images. This project aims to develop a conten…
In this project, we will explore the use of state-of-the-art methods to speed-up training and testing times of deep learning models. We are also interested in developing novel techniques for training these models with a limited amount of data. For speed-up, we will explore custom hardware such as Graphical Processing Units (GPUs) for training and embedded hardware for testing. We will als…
Deep learning is an artificial neural network-based machine learning technique that has been successfully employed in many applications, including image analysis, speech recognition, and text comprehension. It consists of multi-level layers, many capable of concurrently running processes with high-level features defined and automatically extracted from low-level ones. A major challenge in…
In the last decade we have seen a huge increase of interest for time series methods in Data Mining and Knowledge Discovery. Such interest has culminated in the proposal of literally hundreds of methods for tasks such as classification, cluste- ring, anomaly detection, motif detection, among others. Empirical research has demonstrated that time series similarity-based methods provide very …
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