The Data Science Summer School @Pisa, to be held on Sept 2-6, 2019, will offer a broad multi-disciplinary perspective on Data Mining and Big Data Analytics, Machine Learning and AI, Network Science and Complex Systems, Digital Ethics, Computational Social Science and Applied Data Science.
The School will feature 10 half-day tutorial lectures held by high-profile international scholars, including:
- János Kertész (CEU Budapest), Guido Caldarelli (IMT Lucca) and Albert-László Barabási (Northeastern Univ. Boston, TBC) lecturing on Network science
- Aristides Gionis (Aalto Univ. Helsinki) and Kalina Bontcheva (Univ. Sheffield) lecturing on Social media analytics, information disorder and misinformation
- John Shawe-Taylor (UCL London) lecturing on Machine learning
- Jeroen van den Hoven (TU Delft) and Nello Cristianini (Univ. Bristol) lecturing on Ethical and social impacts of Data Science and Artificial Intelligence
- Stefano Leonardi (Univ. Roma La Sapienza) lecturing on Big data algorithms
- Marlon Dumas (Univ. Tartu) lecturing on Process mining
- Fosca Giannotti (CNR Pisa) and Dino Pedreschi (Univ. Pisa) lecturing on Explainable Artificial Intelligence.
The target audience comprises Master- and PhD-level students, post-doctoral and early-career researchers, and inter-disciplinary researchers from academia and industry. Applications can be submitted online here, prior to the June 16, 2019 deadline.
This is a is joint initiative of the Data Science Ph.D. in Pisa (a consortium of the Scuola Normale Superiore, UniPi, the Sant'Anna School, the IMT School and the CNR) and the Data Science Ph.D. in Rome (La Sapienza University, Rome), co-sponsored by SoBigdata.eu (the European Research Infrastructure “Social Mining and Big Data Analytics Ecosystem”) and EMbeDS (the Department of Excellence for "Economics and Management in the era of Data Science", at the Sant'Anna School).
Interested students in the EMbeDS community are encouraged to contact Cristiana Bettelli (firstname.lastname@example.org) for information on potential support.