$ whoami

Marco Meoni, married, 3 awesome kids, far more computers than I care to mention (1st one, still operational, an Olivetti M24 from 1989).
MSc and Ph.D in Computer Science, Powerbuilder MVP, software engineer, former BigData researcher at CERN, keen on particle physics.
Collection of 3k+ Disney comics, owner of Italian vintage cars, U2 fan, sporty, Valentino Rossi, Zico and Nigel Mansell my myths, Udinese fan.
Love travelling, visited 31 countries and more than half of the States in US, Casalguidi my little peaceful beloved HSH though.

$ $ awk ‘NR > 2004 && NR <= 2020 {print $1}’ conferences.txt

2018 Dataset Popularity Prediction for Caching of CMS Big Data, Journal of Grid Computing
2017 Popularity-based caching of CMS datasets, PARCO, Bologna, Italy
2017 Exploiting Apache Spark platform for CMS computing analytics, ACAT, Seattle, USA
2016 XrootD popularity on Hadoop clusters, CHEP, San Francisco, USA
2011 iGrid: Interactive Grid, IEEE ESCIENCE, Stockholm, Sweden
2011 Beyond batch computing on the WLCG Grid, IEEE/ACM GRID, Lyon, France
2011 Interactivity on the Grid: Limitations and Opportunities, ACM HPDC, San Jose, USA
2010 Exploiting the ALICE HLT for PROOF by scheduling of virtual machines, CHEP, Taipei, Taiwan
2009 A Worldwide Parallel ROOT Facility, Supercomputing, Portland, USA
2009 Interactive Parallel Analysis on the ALICE Grid with the PROOF framework, ICCS, Baton Rouge, USA
2009 Current Status of the CERN Analysis Facility, CHEP, Prague, Czech Republic
2008 Using IHE profiles for an healthcare DataGRID based on AliEn, EMMIT, Tripoli, Libya
2004 AliEn Web Portal, CHEP, Interlaken, Switzerland

$ cat marco.cv | 2>&1 | grep -v “I work w/ computers” | tee out.log

DB-centric Web/Mobile developer (Powerbuilder, Appeon, Vue.JS, Android).
Grid/Cloud computing enthusiast with proficiency in WLCG tools and publications in conferences endorsed by IEEE and ACM.
Hadoop and all around guy.

$ grep ‘Mining Predictive Models for Big Data Placement’ phd.txt

The research program has been supported by the ICT department of the European Organization for Nuclear Research (CERN), Geneva, and the Compact Muon Solenoid (CMS) collaboration at the National Institute of Nuclear Research (INFN), Pisa.
The thesis work has researched on how to apply machine learning and Big Data techniques on a large set of computing logs provided by the CMS experiment, in order to optimize CPU utilization and job wall time of the distributed computing infrastructure.

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