Research – Davide Bacciu Homepage

Post-Doc

Memorization in Recurrent Neural Networks (2017-2020), Antonio Carta, Computer Science Department, Università di Pisa
A Tensor Framework for Learning in Structured Domains (2017-2020), Daniele Castellana, Computer Science Department, Università di Pisa
Deep Learning Safety under Non-Stationarity Assumptions (2017-2020), Francesco Crecchi, Computer Science Department, Università di Pisa
Deep Learning on Graphs with Applications to the Life Sciences (2017-2020), Marco Podda, Computer Science Department, Università di Pisa

(2019-2022), Dario Balboni, Data Science PhD, Scuola Normale Superiore
(2019-2022), Andrea Cossu, Data Science PhD, Scuola Normale Superiore
2020-2023), Valerio De Caro, Computer Science Department, Università di Pisa
(2018-2021), Federico Errica, Computer Science Department, Università di Pisa
(2017-2020), Elisa Ferrari, Data Science PhD, Scuola Normale Superiore
(2020-2023), Alessio Gravina, Computer Science Department, Università di Pisa
(2019-2022), Francesco Landolfi, Computer Science Department, Università di Pisa
(2019-2022), Giacomo Lanciano, Data Science PhD, Scuola Normale Superiore
(2018-2021), Francesca Lizzi, Data Science PhD, Scuola Normale Superiore
(2020-2023), Danilo Numeroso, Computer Science Department, Università di Pisa
2020-2023), Michele Resta, Computer Science Department, Università di Pisa
(2019-2022), Francesco Sansone, Computer Science Department, Università di Pisa
(2019-2022), Asma Sattar, Computer Science Department, Università di Pisa
(2019-2022), Andrea Valenti, Computer Science Department, Università di Pisa

(Mar 2021-May 2021) Elisa Ferrari: Causal analysis of COVID-19 data
(Mar 2021-May 2021) Ruggiero Santo: Deep learning for lung ultrasound processing in COVID-19
(Jul 2020-Oct 2020) Alessio Gravina: Deep graph networks for COVID-19 drug repurposing
(May 2020-ongoing) Davide Italo Serramazza: Multimodal data processing for video remixing
(Apr 2018-Apr 2020) Antonio Bruno: Design and development of learning models for non-isomorph structured transductions
(Jul 2019-Dec 2019) Gioele Bertoncini: Explorative analysis of wearable sensor data(
May 2019-Oct 2019) Vlad Pandelea: Machine learning for structured data and probabilistic processes
(Apr 2019-Oct 2019) Andrea Valenti: Deep learning for music generation
(Nov 2017-Mar 2018) Antonio Bruno: Deep Learning for non-isomorph structured transductions
(Aug-Nov 2017) Daniele Castellana: Design and development of generative models for learning non-isomorph transductions
(May-Nov 2017) Marco Podda: Analysis of clonal evolution data by machine learning
(Apr-Oct 2016) Francesco Crecchi: Analysis of biomedical signals from mobile devices by Machine Learning
(Sept 2015-Mar 2016) Francesco Brundu: Analysis of Machine-Learning techniques and models for industrial process Big Data