| 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] |
|
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 this work 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).
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
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, vol. 42, 8pp., Revista Paulista de Pediatria, 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, C. E. Thomaz, and R. Guinsburg. Looking through providers’ eyes: pain in the Neonatal Intensive Care Unit, American Journal of Perinatology, December 2023.
P. H. S. Domingues, T. M. Heiderich, M. C. M. Barros, R. Guinsburg, and C. E. Thomaz. Neonatal Face Segmentation with and without Clinical Devices using SAM. In proceedings of the 36th SIBGRAPI, Conference on Graphics, Patterns and Images, Workshop of Works in Progress (WiP), pp. 101-104, Rio Grande, Rio Grande do Sul, Brazil, November 6th-9th 2023.
L. A. Ferreira, L. P. Carlini, G. A. S. Coutrin, T. M. Heiderich, M. C. M. Barros, R. Guinsburg, and C. E. Thomaz. Revisiting N-CNN for Clinical Practice. In proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, Workshop on Ambient Intelligence for Healtcare & Computational Affective Intelligence for Computer Assisted Interventions, PRedictive Intelligence in MEdicine (PRIME), LNCS 14277, pp. 231-240, Vancouver, Canada, October 8th 2023.
R. G. M. Junior, R. N. Orsi, M. C. M. Barros, R. Guinsburg and C. E. Thomaz. Pupillary activity in areas of interest from visual stimuli for neonatal pain assessment. In Workshop on Ambient Intelligence for Healtcare & Computational Affective Intelligence for Computer Assisted Interventions, 4pp., Vancouver, Canada, October 8th 2023.
F. G. Tamanaka and C. E. Thomaz. Sobel filter and linear classification for deepfake analysis of faces. In proceedings of the 20th Encontro Nacional de Inteligencia Artificial e Computacional, ENIAC 2023, pp. 15-27, Belo Horizonte, Minas Gerais, Brazil, September 25th-29th 2023.
L. F. Buzuti and C. E. Thomaz. Frechet AutoEncoder Distance: A new approach for evaluation of Generative Adversarial Networks, Computer Vision and Image Understanding, 11pp., July 2023.
Last updated: fevereiro 20, 2024. Copyright of these papers, reproduced here for timely dissemination of research information, is with the respective publishers.
