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Emilio Coppa

Sapienza University of Rome

Info


Research interests:
My main research interests are in programming languages, software systems and program analysis. During my PhD, I focused my work on the study of performance scalability of software systems. In the last two years, I have investigated how program analysis techniques, such as symbolic execution, can be exploited in the context of cybersecurity.

Position:
PostDoc at DIAG with Prof. Camil Demetrescu. Member of SEASON Lab.
Member of CIS Sapienza research center for Cyber Intelligence and Information Security.
Member of CyberChallenge.IT organizing team.

Email:
coppa [at] diag.uniroma1.it
Office:
DIAG, Via Ariosto 25, Rome - 1st floor, room B118


Full CV:


Education


Jan 2016 - Present
PostDoc at Sapienza University of Rome
with Prof. Camil Demetrescu (demetres [at] dis.uniroma1.it).


Oct 2012 - Dic 2015
Ph.D. in Computer Science at Sapienza University of Rome.
Advisor: Prof. Irene Finocchi (finocchi [at] di.uniroma1.it).


Apr 2015 - Jul 2015
Visitor at TU Darmstadt.
Prof. Patrick Eugster (peugster [at] cs.purdue.edu).


Oct 2010 - Oct 2012
Master of Science in Engineering in Computer Science (taught in English) at Sapienza University of Rome. GPA 29.57/30. Final grade: 110/110 summa cum laude.
Thesis Advisor: Prof. Camil Demetrescu (demetres [at] dis.uniroma1.it).


Sept 2007 - Oct 2010
Bachelor of Science in Engineering in Computer Science at Sapienza University of Rome. GPA: 26.7/30. Final grade: 110/110.
Thesis advisor: Prof. Camil Demetrescu (demetres [at] dis.uniroma1.it).


Projects


aprof: input-sensitive profiler

aprof - input-sensitive profiling

aprof is a Valgrind tool for performance profiling designed to help developers discover hidden asymptotic inefficiencies in the code. From one or more runs of a program, aprof measures how the performance of individual routines scales as a function of the input size, yielding clues to its growth rate.
Related papers: [CDF-PLDI12] [CDFM-CGO14] [CDF-TSE14] [C-VAL14]


hadoop internals

Hadoop Internals - Diagrams

This project contains several diagrams describing Apache Hadoop internals (2.3.0 or later).


nearestfit: mapreduce progress indicator

NearestFit - predicting MapReduce performance

The NearestFit progress indicator targets accuracy of progress predictions for MapReduce jobs in the presence of data skewness and super-linear computations. This is achieved combining performance profiling, machine learning techniques, and data streaming algorithms.
Related papers: [CF-SOCC15]


Publications


Our latest publications:
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Teaching


  • [2018-2019] Computer System Architecture (Sistemi di Calcolo I): adjunct professor. website.
  • [2017-2018] Computer System Architecture (Sistemi di Calcolo I): adjunct professor. website.
  • [2016-2017] Computer System Architecture (Sistemi di Calcolo I): teaching assistant. website.
  • [2015-2016] Fondamenti di Informatica II: teaching assistant.