Daniele Di Pompeo

Daniele Di Pompeo

Post-doc researcher

University of L'Aquila

Biography

Daniele Di Pompeo is a Post-doc researcher at Univesrsity of L’Aquila. He has got the PhD in ICT on 2019, and his research interests are mainly realated to model-based performance analysis. In 2020 he has elected as “Expert of the subject” in Software Quality Engineering. He is member of the Software Engineering and Architecture Laboratory research group.

Interests
  • Performance analysis
  • Model-Driven Engineering
  • Search-Based Software Engineering
Education
  • PhD in Information and Communication Technology, 2019

    University of L'Aquila

  • MEng in Computer and System Engineering, 2015

    University of L'Aquila

  • BSc in Computer and System Engineering, 2012

    University of L'Aquila

Experience

 
 
 
 
 
Thesis co-supervisor - Vincenzo De Petris
University of L’Aquila
Mar 2021 – Mar 2021 Italy
Co-supervisor for Vincenzo De Petris Bachelor degree thesis in computer science.
 
 
 
 
 
Post-Doc
Center of Excellence EMERGE
Nov 2020 – Present Italy
 
 
 
 
 
Expert of the Subject in Software Quality Engineering
University of L’Aquila
Oct 2020 – Present Italy

Responsibilities include:

  • Supporting students during the SQE course
  • Member of the Exam Committee
 
 
 
 
 
Post-Doc
Center of Excellence DEWS
Nov 2018 – Oct 2020 Italy
 
 
 
 
 
Thesis co-supervisor - Marisa Fallone
Nov 2018 – Nov 2018 Italy
Co-supervisor for Marisa Fallone Master degree thesis in computer and automated engineering.
 
 
 
 
 
Thesis co-supervisor - Stefano Di Francesco
Nov 2018 – Nov 2018 Italy
Co-supervisor for Stefano Di Francesco Bachelor degree thesis in Computer Science.

Organizing Committee

Publication Chair
Web Chair

Program Committee

Artifacts-Evaluation Program Committee
Artifacts-track Committee
Demo Evaluation Committee
Artifact Evaluation Committee
Demo Evaluation Committee

Projects

Emerge

Emerge

Augmentation networks for automotive/rail

MegaMart 2 Excel Project

MegaMart 2 Excel Project

MegaM@Rt will create a framework incorporating methods and tools for continuous development and validation leveraging the advantages in scalable model-based methods to provide benefits in significantly improved productivity, quality and predictability of large and complex industrial systems.

Contact