Education

University of Liverpool - Department of Computer Science

Doctor of Philosophy - Ph.D. • 2017 — Present

Topic: Deep learning for multi-agent reinforcement learning and decision making
Supervisors: Dr. Frans Oliehoek, Prof. Rahul Savani, Prof. Karl Tuyls

University of Perugia - Department of Mathematics and Computer Science

Master's Degree, Computer Science • 2014 — 2017

Thesis: Learning numeracy - binary arithmetic with Neural Turing Machines
Supervisors: Prof. Valentina Poggioni, Prof. Marco Baioletti
Final mark: 110/110 with honors

University of Perugia - Department of Mathematics and Computer Science

Bachelor's Degree, Computer Science • 2011 — 2014

Thesis: Krylov iterative methods for the geometric mean of two matrices times a vector
Supervisor: Prof. Bruna Iannazzo
Final mark: 110/110 with honors

Publications

Krylov iterative methods for the geometric mean of two matrices times a vector

Main author • Published on Numerical Algorithms 74(2), 561-571, Springer US, 26 January 2017 [.bib]

In this work, we are presenting an efficient way to compute the geometric mean of two positive definite matrices times a vector. For this purpose, we are inspecting the application of methods based on Krylov spaces to compute the square root of a matrix. These methods, using only matrix-vector products, are capable of producing a good approximation of the result with a small computational cost.

Fake Twitter followers detection by denoising autoencoder

Co-author with Valentina Poggioni and Giulia Sorbi • Proceedings of the International Conference on Web Intelligence WI’17, 195-202, ACM, 17 [.bib]

Gaining followers on the Twitter platform has become a rapid way to increase one’s credibility on this social network, that in the last few years has become a launch pad for new trends and to influence people opinions. So, many people have begun to buy fake followers on underground markets appositely created to sold them. Therefore, identifying fake followers profiles is useful to maintain the balance between real influential people on the network and people who simply exploited this mechanism. This work presents a model based on artificial neural networks able to detect fake Twitter profiles. In particular, a denoising autoencoder has been implemented as anomaly detector trained with a semi-supervised learning approach. The model has been tested on a benchmark already used in literature and results are presented.

Teaching

COMP532 Module Demonstrator (Machine Learning and BioInspired Optimization)

University of Liverpool - Department of Computer Science • January 2018 — May 2018

COMP202 Module Demonstrator (Complexity of Algorithms)

University of Liverpool - Department of Computer Science • January 2018 — May 2018

Contacts

Mail

J [dot] Castellini [at] liverpool [dot] ac [dot] uk

Office

n. 213, Department of Computer Science, University of Liverpool • Ashton Building, Ashton Street, Liverpool, United Kingdom, L69 3BX

Phone

I haven't got an English phone number yet, I'm sorry...