Marco Grassia
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Marco Grassia

Research Fellow · Network Science and Machine Learning

University of Catania

About Me

I am a Research Fellow at the Department of Electrical Electronics and Computer Engineering (DIEEI) of the University of Catania (Italy), where I obtained a Ph.D. in Computer Engineering with a thesis on approaching NP-hard problems on graphs using Geometric Deep Learning.

I work in the NetworkScience Laboratory and my research interests include Network Science and its real-world applications, and also the application of Machine Learning techniques — specifically Geometric Deep Learning — to learn high impact problems, or to solve or reduce the search space of computationally hard ones.

In my free-time I enjoy working on personal projects, practicing sport, playing videogames and photography.

Interests

  • Network Science
  • Artificial Intelligence
  • Geometric Deep Learning
  • Econophysics

Education

  • Ph.D. in Computer Engineering with a thesis on Network Science and Machine Learning, 2022

    University of Catania

  • M.Sc. in Computer Engineering, 2018

    University of Catania

  • B.Sc. in Computer Engineering, 2016

    University of Catania

Experience

 
 
 
 
 

Post-doctoral researcher

University of Catania

May 2022 – Present Catania, Italy
 
 
 
 
 

Adjunct professor (professore a contratto)

University of Catania

Mar 2022 – Present Catania, Italy
Co-docente a contratto di Fondamenti di Informatica (J-Pr) / Foundations of Computer Science, Corso di Laurea in Ingegneria Informatica (L-8), Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), Università degli Studi di Catania
 
 
 
 
 

Research fellow

Consorzio COMETA

Nov 2021 – Jan 2022 Catania, Italy
 
 
 
 
 

External Relations Committee

Italian Chapter of the Complex Systems Society (CSS/Italy)

Sep 2021 – Present
 
 
 
 
 

Ph.D. Student

Department of Electrical Electronics and Computer Engineering (DIEEI), University of Catania

Oct 2018 – Oct 2021 Catania, Italy
Graduated with a thesis on tackling NP-hard problems on graphs using Geometric Deep Learning, where I also proposed a new layer to learn on signed and weighted networks, and a new meta-model to learn on multilayer networks.
 
 
 
 
 

Research Intern

Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI)

Jul 2018 – Sep 2018 Beijing, China

Remote internship at the Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI).

Research on network dismantling approaches that leverage the geometry of the network.

 
 
 
 
 

Machine Learning Intern

Nokia Bell Labs, Data Analytics

Jul 2018 – Sep 2018 Dublin, Ireland
  • Worked on master’s thesis and on paper that was submitted to the International Joint Conference on Artificial Intelligence XXVIII – IJCAI-19
  • “Fine-grained search space classification for hard subset problems”
  • Application of Machine Learning techniques to Network Science
  • Worked in team with Post-Doctoral Researchers and Ph.D. students
  • Python, C++
  • Keras, Auto-sklearn, iGraph, graph-tool
 
 
 
 
 

System Integration and Home Automation Intern

Systemia S.R.L.

Jul 2017 – Oct 2017 Catania, Italy
  • Designed and developed Amazon’s Alexa Custom and Smart Home Skills to provide integration with Vantage Controls devices
  • Studied how to design Vocal User Interfaces (VUIs)
  • Gained familiarity with Amazon Web Services
  • Amazon Web Services IaaS, OpenVPN, Node.JS