Mathematical engineer, particularly interested in graph theory, Markov chains, stochastic processes and randomised optmisation algorithms. Currently working on crowdsourcing, human computation and privacy issues in recommender systems. Also interested in high performance parallel computing and big data analysis.

Education

Feb 2015 Ph.D. in Mathematics
Hamilton Institute
Design of decentralised algorithms applied to channel/code selection and convex optimisation for throughput fairness of 802.11 networks
2010 M.Sc. in Mathematical Engineering
University of Roma "Tor Vergata"
110/110 with great distinction. Thesis on Monte Carlo Markov Chain methods for the approximate solutions of feature selection problems
2009 Erasmus Scholarship
Universiteit Gent, Department of Telecommunications
Queuing Behaviour of Statistical Multiplexer with Spacing
2007 B.Sc. in Mathematical Engineering
University of Roma "Tor Vergata"
110/110 with great distinction. Thesis on Wavelet analysis for recognition of form document images with complicated background

Research Experience

2017 - present Information School, University of Sheffield, Dr. Gianluca Demartini
Research Associate on the H2020-funded project FashionBrain on Crowsourcing and recommender systems
2016 Information School, University of Sheffield, Dr. Gianluca Demartini
Research Associate on the EPSRC-funded project BetterCrowd on Crowsourcing and recommender systems
2016 Science Foundation Ireland and Trinity College Dublin, Prof. Doug Leith
Recipient of Technology Innovation Development Award (TIDA) 2016 on Privacy issues in recommender systems and probabilistic matrix factorisation
2015 Statistics and Computer Science Department, Trinity College Dublin, Prof. Doug Leith
Postdoctoral Researcher on Privacy issues in recommender systems and probabilistic matrix factorisation

Selected Publications

Google Scholar

Pairwise, Magnitude, or Stars: What's the Best Way for Crowds to Rate?
A. Checco and G. Demartini
arXiv preprint arXiv:1609.00683 2016
[1] [pdf]
BLC: Private Matrix Factorization Recommenders via Automatic Group Learning
A. Checco, G. Bianchi, and D. Leith
ACM Transactions on ACM Transactions on Privacy and Security (TOPS) 2017
[2] [pdf]
Learning-Based Constraint Satisfaction With Sensing Restrictions
A. Checco and D. Leith
IEEE Journal of Selected Topics in Signal Processing 2013
[3] [pdf]
Fast, Responsive Decentralised Graph Colouring
A. Checco and D. Leith
arXiv preprint arXiv:1405.6987 2014
[4] [pdf]
Fair Virtualisation of 802.11 Networks
A. Checco and D. Leith
IEEE/ACM Transactions on Networking 2013
[5] [pdf]
Proportional Fairness in 802.11 Wireless LANs
A. Checco and D. Leith
IEEE Communications Letters 2011
[6] [pdf]
Channel Bonding in Short-Range WLANs
B. Bellalta, A. Faridi, J. Barcelo, A. Checco, and P. Chatzimisios
European Wireless 2014
[7] [pdf]
Throughput Analysis in CSMA/CA Networks using Continuous Time Markov Networks: A Tutorial
B. Bellalta, A. Zocca, C. Cano, A. Checco, J. Barcelo, and A. Vinel
arXiv preprint arXiv:1404.0180 2014
[8] [pdf]
Using Crowd sourcing for Local Topology Discovery in Wireless Networks
A. Checco, C. Lancia, and D. Leith
arXiv preprint arXiv:1401.1551 2014
[9] [pdf]
Self-configuration of Scrambling codes for WCDMA Small Cell Networks
A. Checco, R. Razavi, D. Leith, and H. Claussen
IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC) 2012
[10] [pdf]
On the interactions between multiple overlapping WLANs using channel bonding
B. Bellalta, A. Checco, A. Zocca, and J. Barcelo
IEEE Transactions on Vehicular Technology 2016
[11]

Industry Experience

2011 - 2012 Bell Laboratories Ireland, Intern
  • Decentralised algorithms design for scrambling code selection in femtocell networks

Skills

Languages

Bash, C, C++, CSS, Matlab, JavaScript, Fortran, HTML, LaTeX, Mathematica, Python, R

Frameworks

Spark, Cloudera, Pandas, NumPy, SciPy, SimPy, scikit-learn

Algorithm design

Design, convergence rate and complexity analysis of decentralised algorithms on graphs

Convex optimisation

Convex optimisation, with application to discrete problems. Numerical methods for approximate solution of optimisation problems

Data Mining

Monte Carlo Markov chains techniques for data mining and feature selection

Privacy in recommender systems

Probabilistic matrix factorisation applied to recommender systems, with focus on privacy issues

Simulators

Event-based simulators design for wireless network analysis

Statistical inference

Bayesian modelling and exploratory data analysis, with focus on big data

Recent Blog Posts

Modus Operandi of Crowd Workers: The Invisible Role of Microtask Work Environments March 20, 2017
How to Best Serve Micro-tasks to the Crowd when there Is Class Imbalance March 19, 2017
Pairwise, Magnitude, or Stars: What's the Best Way for Crowds to Rate? March 18, 2017

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Last updated on 2017-03-20