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Antonio Luca Alfeo
Also published under:Antonio L. Alfeo
Affiliation
Univ. of Pisa, Italy
Topic
Neural Network,Affective Computing,EEG Signals,Emotion Recognition,Emotion Recognition Task,Frequency Band,Artificial Neural Network,Classification Performance,Convolutional Layers,Convolutional Neural Network,Emotion Categories,Explainable Artificial Intelligence,Latent Space,Machine Learning,Pheromone Trails,Power Spectral Density,Recognition Performance,3D Reconstruction,3D Scene,AI Models,Activation Maps,Additional Metrics,Air Pollution,Amnesty,Amount Of Waste,Approaches In The Literature,Arousal Dimension,Bike-sharing,Biofeedback,Block Diagonal,Brain-computer Interface Applications,Brain-computer Interface System,Call Detail Records,Calling Behavior,Categorical Cross-entropy Loss,Central Place Foragers,Classification Process,Classification Task,Cognitive Domains,Collective Mobilization,Conceptual Domains,Conceptual Information,Conceptual Representations,Continuous Labeling,Daily Routines,Dark Regions,Data Logger,Decision Tree,Dense Layer,Density Patterns,
Biography
Antonio Luca Alfeo was born in Taranto, Italy, in 1987. He received the B.S. and M.S. degrees in computer engineering from the University of Pisa, Italy, and the Ph.D. degree from the International Ph.D. Program in Smart Computing (University of Pisa, University of Florence, and University of Siena), in 2019. In 2018, he was a Visiting Student with the MIT Media Laboratory, where he studied different swarm intelligence solutions to analyze collective behaviors in smart cities with Prof. Alex Sandy Pentland. From 2019 to 2021, he was a Postdoctoral Research Fellow with the Department of Information Engineering, University of Pisa, where he studied different deep learning approaches for the optimization of maintenance processes in the field of Industry 4.0. Since 2022, he has been an Assistant Professor with the Department of Information Engineering and a fellow of the Bioengineering and Robotics Research Center E. Piaggio, University of Pisa. His research interest includes the design of machine learning pipelines to analyze physiological and behavioral data via explainable artificial intelligence and deep representation learning. He is the coauthor of many international scientific contributions in these fields published in peer-reviewed international journals and conference proceedings.