Ph.D. in Intelligenza Artificiale , 38th cycle (2022-2025)
Ph.D. obtained in 2026
Dissertation:
Theory and Tools for Structure-Aware Machine Learning on Biological Data (Abstract)
Tutors:
Sebastiano VignaNicolo' Antonio Cesa-bianchiPublications
Works published during the Ph.D. View all publications in Porto@Iris
- D'Ascenzo, Davide (2026)
Theory and Tools for Structure-Aware Machine Learning on Biological Data. relatore: VIGNA, SEBASTIANO; CESA-BIANCHI, NICOLO' ANTONIO; , 38. XXXVIII Ciclo, P.: 149
Doctoral Thesis - D'Ascenzo, Davide; Cultrera Di Montesano, Sebastiano (2025)
scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics.
Altro - Danhofer, David A.; D'Ascenzo, Davide; Dubach, Rafael; Poggio, Tomaso (2025)
Position: A Theory of Deep Learning Must Include Compositional Sparsity. In: Forty-second International Conference on Machine Learning Position, Vancouver (CAN), July 13th -19th, 2025, pp. 81199-81210
Contributo in Atti di Convegno (Proceeding) - Boldi, Paolo; D'Ascenzo, Davide; Furia, Flavio; Vigna, Sebastiano (2024)
Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities. In: SOCIAL NETWORK ANALYSIS AND MINING, vol. 14. ISSN 1869-5469
Contributo su Rivista - Boldi, Paolo; D'Ascenzo, Davide; Furia, Flavio; Vigna, Sebastiano (2024)
Score and Rank Semi-Monotonicity for Closeness, Betweenness and Harmonic Centrality. In: The Twelfth International Conference on Complex Networks and their Applications, Menton (FRA), 28 - 30 November, 2023, pp. 102-113. ISSN 1860-949X. ISBN: 9783031534713
Contributo in Atti di Convegno (Proceeding)