Mathematical engineer, particularly interested in graph theory, Markov chains, stochastic processes and randomised optmisation algorithms.
Currently working on privacy issues in recommender systems.
Also interested in high performance parallel computing and big data analysis.
Design, convergence rate and complexity analysis of decentralised algorithms on graphs.
Convex optimisation, with application to discrete problems. Numerical methods for approximate solution of optimisation problems.
Monte Carlo Markov chains techniques for data mining and feature selection, applied to medical diagnostic and artificial olfaction.
Probabilistic matrix factorisation applied to recommender systems, with focus on privacy issues.
Event-based simulators design for wireless network analysis.
Statistical inference, Bayesian modelling and exploratory data analysis, with focus on big data.
My research is currently focused on recommender systems and probabilistic matrix factorisation.
Design of decentralised algorithms applied to channel/code selection in wireless network or local topology discovery in cellular networks and convex optimisation applied to throughput fairness of 802.11 mesh networks.
Research on small cell networks (femtocells) with particular interest in scrambling code selection.
I graduated with great distinction (110/110 summa cum laude). Thesis title: Monte Carlo Markov Chain methods for the approximate solutions of feature selection problems.
Organisation of Hamilton Institute seminars, available here.
Alessandro Checco — firstname.lastname@example.org — +353 85 1648160