Eduardo Aguilar Torres
"Beatriu de Pinós" Postdoc fellow under the supervision of Prof. Petia Radev at UB and an Assistant professor at UCN. His main interest is in the research and application of DL algorithms for visual food analysis. He aims to contribute to improving the quality of life of people through the generation of technological solutions based on ML and CV.
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Profile summary
Eduardo Aguilar Torres is a Beatriu de Pinós Postdoc fellow at Universitat de Barcelona and an Assistant Professor in the Department of Computer and Systems Engineering at UCN. He is currently leading a project on visual food analysis for monitoring the Chilean diet.
He holds a Ph.D. in Mathematics and Computer Science from the University of Barcelona under the tutelage of Prof. Petia Radeva. He is a Civil Engineer in Computing and Informatics and a Master's in Computer Engineering from the Universidad Católica del Norte (UCN).
His undergraduate studies were concluded with Academic Distinction, his master's degree with Highest Academic Distinction, and his doctoral degree with Cum Laude. He has developed 22 papers within the field of machine learning and computer vision: 11 journal articles, 10 papers published in conferences, and 1 book article. In particular, his main interest is in the research and application of deep learning algorithms and uncertainty modeling to analyze the semantic content present in images accurately and robustly. As for the journal articles, 7 of them have been published in high-impact scientific journals (WOS Q1 and Q2).
Regarding the conference papers, these have been mainly published in international conferences of the discipline with conference proceedings indexed in SCOPUS. It should be noted that twice his publications in international congresses were awarded as best paper. He has also collaborated in several R&D projects as a member of the research team and as principal investigator. The most relevant projects in which he has participated have been European projects led by Prof. Petia Radeva, directly related to his research topics. As principal investigator, he has led a project dealing with object detection in the framework of mining occupational safety. His main interest is in the research and application of Deep Learning algorithms for visual food analysis. He aims to contribute to improving the quality of life of people through the generation of technological solutions based on Machine Learning and Computer Vision.
Latest publications
Eduardo Aguilar Torres has 24 publications on Google Scholar.
Only the last 20 publications are shown here.
Access Eduardo Aguilar Torres Google Scholar Profile
Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images
2024 | Roberto Morales, Angela Martinez-Arroyo, Eduardo Aguilar | Sensors 24 (7), 2034, 2024
Bayesian DivideMix++ for Enhanced Learning with Noisy Labels
2024 | Bhalaji Nagarajan, Ricardo Marques, Eduardo Aguilar, Petia Radeva | Neural Networks 172, 106122, 2024
Exploring multi-food detection using deep learning-based algorithms
2023 | Roberto Morales, Juan Quispe, Eduardo Aguilar | 2023 IEEE 13th International Conference on Pattern Recognition Systems …, 2023
Continual Evidential Deep Learning for Out-of-Distribution Detection
2023 | Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, Joost Van de Weijer | Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
Deep ensemble-based hard sample mining for food recognition
2023 | Bhalaji Nagarajan, Marc Bolaños, Eduardo Aguilar, Petia Radeva | Journal of Visual Communication and Image Representation 95, 103905, 2023
Localización simultánea y mapeo para control de un robot móvil autónomo usando escaneo de nube de puntos LiDAR y métodos de aprendizaje de máquina
2023 | Ricardo Urvina Córdova, Eduardo Aguilar Torres, Alvaro Prado Romo | Ingeniare. Revista chilena de ingeniería 31, 0-0, 2023
Hacia una detección precisa de cascos de seguridad en tiempo real a través de un método basado en el aprendizaje profundo
2023 | Roger Max Calle Quispe, Maya Aghaei Gavari, Eduardo Aguilar Torres | Ingeniare. Revista chilena de ingeniería 31, 0-0, 2023
Bayesian deep learning for semantic segmentation of food images
2022 | Eduardo Aguilar, Bhalaji Nagarajan, Beatriz Remeseiro, Petia Radeva | Computers and Electrical Engineering 103, 108380, 2022
Uncertainty-aware selecting for an ensemble of deep food recognition models
2022 | Eduardo Aguilar, Bhalaji Nagarajan, Petia Radeva | Computers in Biology and Medicine 146, 105645, 2022
Can deep learning models recognize chilean diet
2022 | Bastián Muñoz, Ignacio Chirino, Eduardo Aguilar | IEEE Latin America Transactions 20 (9), 2131-2138, 2022
Uncertainty-aware data augmentation for food recognition
2021 | Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khantun, Marc Bolaños, Petia Radeva | 2020 25th International Conference on Pattern Recognition (ICPR), 4017-4024, 2021
Nutritional monitoring in older people prevention services
2021 | Bhalaji Nagarajan, Rupali Khatun, Marc Bolaños, Eduardo Aguilar, Leonardo Angelini, Mira El Kamali, Elena Mugellini, Omar Abou Khaled, Noemi Boqué, Lucia Tarro, Petia Radeva | Digital Health Technology for Better Aging: A Multidisciplinary Approach, 77-102, 2021
S2ML-TL framework for multi-label food recognition
2021 | Bhalaji Nagarajan, Eduardo Aguilar, Petia Radeva | International Conference on Pattern Recognition, 629-646, 2021
Uncertainty-aware integration of local and flat classifiers for food recognition
2020 | Eduardo Aguilar, Petia Radeva | Pattern Recognition Letters 136, 237-243, 2020
Uncertainty modeling and deep learning applied to food image analysis
2020 | Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khatun, Marc Bolaños, Petia Radeva | International Joint Conference on Biomedical Engineering Systems and …, 2020
Deep Learning and Uncertainty Modeling in Visual Food Analysis
2020 | Eduardo Aguilar | Universitat de Barcelona, 2020
Regularized uncertainty-based multi-task learning model for food analysis
2019 | Eduardo Aguilar, Marc Bolaños, Petia Radeva | Journal of Visual Communication and Image Representation 60, 360-370, 2019
Class-conditional data augmentation applied to image classification
2019 | Eduardo Aguilar, Petia Radeva | Computer Analysis of Images and Patterns: 18th International Conference …, 2019
Food recognition by integrating local and flat classifiers
2019 | Eduardo Aguilar, Petia Radeva | Iberian Conference on Pattern Recognition and Image Analysis, 65-74, 2019
Grab, pay, and eat: Semantic food detection for smart restaurants
2018 | Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, Petia Radeva | IEEE Transactions on Multimedia 20 (12), 3266-3275, 2018
