sapienza logo

Emilio Coppa

Sapienza University of Rome


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.

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.

coppa [at]
DIAG, Via Ariosto 25, Rome - 1st floor, room B118

Full CV:


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

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

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

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]

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]


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]


Our latest publications:
  • [{{ }}] {{ a.given }} {{ }}, and. {{ p.title }}. {{ p['container-title'] }} ({{ p['collection-title'].replace('\'', '20') }}), {{ p.issued['date-parts'][0][0] }}. [DOI] [DOI] [PDF] [SLIDES] [PROJECT SITE] [BIBTEX]


  • [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.