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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] dis.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


  • [BCDD-TR17] Roberto Baldoni, Emilio Coppa, Daniele Cono D’Elia, Camil Demetrescu. Assisting Malware Analysis with Symbolic Execution: a Case Study. Technical report, 2017.
  • [BCDDF-TR16] Roberto Baldoni, Emilio Coppa, Daniele Cono D'Elia, Camil Demetrescu, Irene Finocchi. A Survey of Symbolic Execution Techniques. Technical report, 2016. [ArXiv]
  • [CF-SOCC15] Emilio Coppa and Irene Finocchi. On data skewness, stragglers, and MapReduce progress indicators. Sixth ACM Symposium on Cloud Computing (SoCC'15), pp 139-152, 2015. [DOI] [PDF] [SLIDES]
  • [C-VAL14] Emilio Coppa. An interactive visualization framework for performance analysis. 8th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2014), pp 159-164, 2014. [DOI] [PDF] [SLIDES] [PROJECT SITE]
  • [CDF-TSE14] Emilio Coppa, Camil Demetrescu, and Irene Finocchi. Input-Sensitive Profiling. IEEE Transactions on Software Engineering (IEEE TSE'14), 40(12), pp 1185-1205, 2014.[DOI] [PDF] [PROJECT SITE]
  • [CDFM-CGO14] Emilio Coppa, Camil Demetrescu, Irene Finocchi, and Romolo Marotta. Estimating the Empirical Cost Function of Routines with Dynamic Workloads. 12th IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2014), pp 230-239, 2014. [DOI] [PDF] [SLIDES] [PROJECT SITE]
  • [CDF-PLDI12] Emilio Coppa, Camil Demetrescu, and Irene Finocchi. Input-Sensitive Profiling. 33rd ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI 2012), pp 89-98, 2012. [DOI] [PDF] [SLIDES] [PROJECT SITE]


Teaching