Antonio Luca Alfeo

Available Topics

Physiological, Industrial and behavioral data analysis via Deep Representation Learning and Explainable Artificial Intelligence

Master Degree

F. Colak, Development of a Clinical Decision Support System for Thyroid Nodules Based on Explainable Artificial Intelligence, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
C. Daka, Counterfactual rule extraction and generalization for interpretable machine learning, MS in Computer Engineering, Università di Pisa, a.y. 2024-25.
E. De Filomeno, Experimentation of a distributed explainable artificial intelligence approach, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
E. Tinghi, A double quantization approach for autonomous concept learning in sleep staging, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
M. Manni, Enabling Concept-Embedding Models for Sleep Staging through Automatic Prototype-Based Learning of Concepts, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
D. Vigna, Design and evaluation of an ensemble of latent spaces to generate counterfactuals for explainable multiclass emotion recognition, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
D. Laporta, Input-diversified ensemble of counterfactual-based feature importances to approximate the explanation of monolithic machine learning model, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
D. Giaquinta, Design and Experimentation of a Novel Feature Importance Measure and Rule-Extraction Approach Based on Non-Minimal Counterfactuals, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2024-25.
G. Barbieri, Design and testing of a weighted aggregation method for a conterfactual-based feature importance measure and its application to industrial production quality recognition, MS in Computer Engineering, Università di Pisa, a.y. 2024-25.
M. V. Caroti, Strategie di generazione controfattuale per il riconoscimento spiegabile della qualità della produzione, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2023-24.
L. Tonelli, A parametric counterfactual-based feature importance measure for explainable regression of product quality, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2023-24.
T. Nocchi, Design and Experimental Evaluation of a Novel Model-Agnostic Feature Importance Measure for Quality Measures in Industrial Production Processes, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2023-24.
G. Cancello Tortora, Analyzing brain data for robust emotion recognition via conceptual decomposition based on autoencoder, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2022-23.
F. Marabotto, Explainable emotion recognition via a novel loss function based on informed contrastive learning, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2022-23.
L. Turchetti, Sleep stage recognition supported by instances-based explanation via contrastive learning, MS in Artificial Intelligence and Data Engineering, a.y. 2022-23..
P. Calabrese, TITLE TO BE DEFINED, MS in Artificial Intelligence and Data Engineering, Università di Pisa.
M. Martorana, A Novel Feature Importance Measure To Explain The Quality Level Prediction In Smart Manufacturing, MS in Artificial Intelligence and Data Engineering, Università di Pisa, a.y. 2021-22.
N. Mota, Development of explainable machine learning architectures for affective computing (thesis in progress, temporary title), MS in Computer Engineering, a.y. 2021-22.
F. Ritorti, Emotion detection via Explainable Affective Computing for physiological signals (thesis in progress, temporary title), MS in Artificial Intelligence and Data Engineering, a.y. 2021-22.
M. A. Gherardi, Predictive maintenance via multi-modal autoencoder with shared latent representation (thesis in progress, temporary title), MS in Computer Engineering, a.y. 2021-22.
M. Quintavalla, Using electroencephalogram signals to recognize brain activities (thesis in progress, temporary title), MS in Artificial Intelligence and Data Engineering, a.y. 2021-22.
F. Balestri, Explainable Artificial Intelligence for Brain-Computer Interface (thesis in progress, temporary title), MS in Artificial Intelligence and Data Engineering, a.y. 2021-22.
G. Gagliardi, A novel multimodal feature learning architecture for explainable affective computing, MS in Artificial Intelligence and Data Engineering, a.y. 2021-22.
A. Pochiero, An Interpretable Multi Channel Feature Learner based on Deep Autoencoder for Predictive Maintenance, MS in Computer Engineering, a.y. 2020-21.
L. Nannini, Technological troubleshooting based on sentence embeddings via deep Neural Networks, MS in Computer Engineering, a.y. 2020-21.
A. De Rosa, Ottimizzazione e incremento dell’efficienza dei processi aziendali mediante l’uso di strumenti digitali, MS in Ingegneria Gestionale, Politecnico di Torino, a.y. 2019-20.
R. Rocchi, Anomaly Detection based on Deep Autoencoder for manufacturing processes, MS in Computer Engineering, a.y. 2019-20.
G. Marchini, Computational intelligence techniques applied to mobility data in the city science environment, MS in Computer Engineering, a.y. 2018-19.
S. Musetti, Using evolutionary optimization and computational stigmergy to detect purchase hotspots from spatiotemporal credit card transactions, a.y. 2018-19.
M. Monaco, Progettazione e realizzazione di un algoritmo di coordinamento di sciami di droni basato sulla swarm intelligence per il rilevamento e l’inseguimento di target dinamici, CdL Spec. Ing. Informatica per la Gestione d’Azienda, a.a. 2016-17.
S. Egidi, A stigmergy-based framework for mining infrequent activity patterns in urban hotspots using taxi GPS data, MS in Computer Engineering, a.y. 2015-16.
P. Piscione, Development of a simulation environment for the adaptive coordination of UAV swarm performing distributed target search via stigmergy and flocking, MS in Computer Engineering, a.y. 2015-16.

Bachelor Degree [from 2015 to 2025]

A. Aliperti, A. Bullari, A. Chelaru, A. Di Ricco, A. Fabbri, A. Gerratana, A. Gherardi, A. Macri, A. Meini, A. Mencarelli, A. Pochiero, A. Sale, A. Tricoli, A. Vagnoli, C. Bruchi, C. Di Puorto, C. Liu, C.M. Lombardo, C. Masiero, C.V. Stanzione, D. Falcone, D. Giaquinta, D. Morucci, E. Logiudice, E. M. Manoni, E. Manoni, E. Respino, E. Senore, F. Barbetta, F. Cavedoni, F. Frati, F. Hudema, F. Scotto, F. Taverna, F. Tarchi, F. Tommaso, F.Campilongo, E. Tonci, G. Alvaro, G. Baris, G. Capecchi, G. Di Porto, G. Maldarella, G. Marchini, G. Marcuccetti, G. Pilè, G. Rosi, G. Tornabene, G. Turrisi, H. P. Silva, I. Canetta, Y. Perugini, J. Niccolai, K. Giannandrea, K. Tubak, L. Cecchi, L. Cecchini, L. Cocchella, L. Gagliani, L. Landi, L. Mancinelli, L. Mannocci, L. Menchini, L. Monaci, L. Perrone, L. Poggiani, L. Visalli, M. Bertelà, M. Bologna, M. Caroti, M. Carrai, M. Crudo, M. Dessi', M. Fabiani, M. Imbelli Cai, M. Meazzini, M. Pierucci, M. Romani, M. Segreto, M.V. Caroti, N. Bacherotti, N. Fabiano, P. Grimaldi, P. Gronchi, P. Tempesti, R. A. Sacco, R. De Luca, R. Fiorini, R. Mulè, R. Zippo, S. Bonanno, S. Cunsolo, S. D'Avella, S. Lotano, S. Micheloni, S. Musetti, S. Poleggi, S.Marchi, T. Califano, U. Villani, V. Bertei, V. Gately, V. Rispo.

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