| Carlos E. Thomaz |  |
| Grupo de
Processamento de Sinais e Imagens Departamento de Engenharia Eletrica Av. Humberto de Alencar Castelo Branco, 3972 - Assuncao Sao Bernardo do Campo - Sao Paulo - Brazil - CEP 09850-901 Office: K5-01 Tel: +55 (0)11 4353 2910 (ext. 2183) Fax: +55 (0)11 4109 5994 Email: my initials @fei.edu.br
[How to reach FEI in Sao Bernardo do Campo, Map of the campus] |
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Short Biography: Carlos E. Thomaz is Professor of Statistical Pattern Recognition at the Department of Electrical Engineering, University College FEI, Sao Paulo, Brazil. He is head of the Image Processing Lab at FEI. Carlos Thomaz received, in 1993, his B.Sc. degree in Electronic Engineering from Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil. After working for six years in industry, he obtained the M.Sc. degree in Electrical Engineering from PUC-Rio in 1999. In October 2000, he joined the Department of Computing at Imperial College London, UK, where he obtained the Ph.D. degree in Statistical Pattern Recognition in 2004. His main research interests are in Pattern Recognition, Cognitive Perception and Machine Learning and over these years he has published more than 150 journal and conference articles on these topics. Professor Thomaz was awarded, in 2012, a University of Nottingham Brazil Visiting Fellowship to work during three months in the Sir Peter Mansfield Magnetic Resonance Centre, UK. He is an associate editor of IEEE Latin America Transactions, member of the editorial board of PLOS ONE and has served as a member of organising and program committees at a number of national and international conferences. From 2015 to 2018, he was awarded a Newton Advanced Fellowship from the Royal Society, UK. In 2020, he received the Fernandes Figueira Award from the National Academy of Medicine (Brazil) for his work on human visual perception of neonatal pain developed in collaboration with researchers of the Federal University of Sao Paulo (UNIFESP). Some recent mentoring results of his research activities have become a finalist project in the 2nd edition of H-INNOVA Health Innovation Award (2020/2021) and in the 8th edition of Semana Municipal de Ciencia, Tecnologia e Inovacao de Santo Andre (2022), as well as the 3rd place project (PhD & Researchers Category ) in the 5th edition of H-INNOVA (2023/2024).
Research Opportunities
Our research activities are mainly related to high-dimensional data analysis. We essentially investigate, propose and implement statistical and machine learning methods to extract, visualize, understand and classify patterns on signal and information (visual and cognitive) processing. Such multivariate data signal and information might be an image, like a human face, a non-invasive measurement of activity in the human brain, like electroencephalography, a human visual perception of an object of interest, like eye-tracking, or even an automotive judder like the vehicle longitudinal oscillation during its clutch system engagement. For further details, please see our Research, Publications, or Image Processing Lab homepages, or contact me directly.
[Teaching, Research, Publications, FEI Face Database]
Recent works
R. N. Orsi and C. E. Thomaz. Analise do olhar humano: Estudos experimentais de rastreamento ocular para explicar padroes visuais em tarefas cognitivas. In proceedings of the 24th Simposio Brasileiro de Computacao Aplicada a Saude, SBCAS 2024, Concurso de Teses e Dissertacoes (CTD), pp. 91-96, Goiania, Goias, Brazil, June 25th-28th 2024.
C. J. Andrioli, L. F. Buzuti, and C. E. Thomaz. Analysis of the calibration of magnetic resonance equipment through the application of Deep Learning and phantom images, 54o. Jornada Paulista de Radiologia (JPR), Sao Paulo, Sao Paulo, Brazil, 2nd-5th May 2024 (Honorable Mention - Best Poster Award).
L. P. Carlini, G. A. S. Coutrin, L. A. Ferreira, J. C. A. Soares, G. V. T. Silva, T. M. Heiderich, R. C. X. Balda, M. C. M. Barros, R. Guinsburg, and C. E. Thomaz. Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks, Artificial Intelligence in Medicine, vol. 147, 10pp., January 2024.
E. S. Silva, M. C. M. Barros, J. B. L. Borten, L. P. Carlini, R. C. X. Balda, R. N. Orsi, T. M. Heiderich, C. E. Thomaz, and R. Guinsburg. Pediatricians’ focus of sight at pain assessment during a neonatal heel puncture, Revista Paulista de Pediatria, vol. 42, 8pp., 2024.
J. B. L. Borten, M. C. M. Barros, E. S. Silva, L. P. Carlini, R. C. X. Balda, R. N. Orsi, T. M. Heiderich, A. Sanudo, C. E. Thomaz, and R. Guinsburg. Looking through providers’ eyes: pain in the Neonatal Intensive Care Unit, American Journal of Perinatology, vol. 41, 7pp., 2024.
Last updated: dezembro 16, 2024. Copyright of these papers, reproduced here for timely dissemination of research information, is with the respective publishers.
