Sito web
www.unive.it/persone/lorenzo.giudice (scheda personale)
Centro temporaneo Progetto Ecosistema dell'Innovazione


Master's Degree in Computer Science: Data Management and Analytics
Ca' Foscari University of Venice (September 2017 - July 2019)
Final mark: 110 with honors 
Thesis: Using Dominant-Set clustering to characterize vessel pathway in port areas.

Bachelor's Degree in Computer Science: Technologies and Information Science
Ca' Foscari University of Venice (September 2014 - March 2017)
Final mark: 110 with honors
Thesis: Migration models to estimate mobility in Venice area.



Best student of the year - Bachelor's Degree Programme in Informatics
(February 2016)
Honor issuer: Ca' Foscari University of Venice.



Tutor activity (as tutor)
Ca' Foscari University of Venice (September 2015 - June 2016)

  • Tutor of Programming course for students pursuing a Bachelor's Degree in Computer Science.
  • F# and C programming languages.

Student Trainee (as Data scientist)
S.A.T.E. System and Advanced Technologies Engineering s.r.l. (June 2018 - October 2018)

  • Apply data analysis and clustering methods on Automatic Identification System (AIS) data in order to help traffic management and collision avoidance in port areas.
  • Data pre-processing.
  • Clustering method application.
  • Cluster analysis.
  • AIS data mining.

Research activity (as Consultant)
Digitalviews srl (September 2019 - October 2019)

  • Application of machine learning techniques for images and video understanding and analysis.
  • Camera calibration.
  • 3D graphical tools implementation.
  • 3D image processing.
  • Machine learning tools implementation.

Research activity (as Consultant)
Ca' Foscari University of Venice (October 2019 - December 2019)

  • Research activity for "Impresa 4.0 e Digital Transformation per le PMI di Venezia e Rovigo del Delta Lagunare".

Research activity (as Consultant)
Ca' Foscari University of Venice (March 2020 - June 2020)

  • Research activity for "Visione Artificiale per il monitoraggio di ambienti pubblici".
    "L'obiettivo specifico di questo progetto è la realizzazione di un'infrastruttura generale che permetta l'utilizzo di un sistema di telecamere IP all'interno ed esterno ad ambienti pubblici (terminal aeroportuali, edifici pubblici, musei, etc.) per abilitare diversi processi di monitoraggio che sfruttino il Machine Learning per estrarre dalla scena informazioni semantiche quantitative e qualitative".

Research activity (as Consultant)
Ca' Foscari University of Venice (July 2020 - June 2021)

  • Research activity for "Metodologie di analisi innovazione digitale – Progettazione e
    implementazione - 2120-0011-1463-2019".

Research activity (as Consultant)
Ca' Foscari University of Venice (July 2021 - January 2023)

  • Research activity for "Integrazione alla piattaforma di sorgenti di Open Data all’interno di un ecosistema
    digitale del turismo per il progetto SMARTLAND - Smart Destinations in the Land of Venice."

Research activity (as Consultant)
Ca' Foscari University of Venice (February 2023 - Present)

  • Research activity for "Sistemi di raccomandazione comportamentale per destinazioni pubbliche e private - Interconnected Nord-Est Innovation Ecosystem (iNEST)".



Reinforcement Learning library
(March 2018 - Present)

  • Performance of solo-agent version and cooperative-agents version of reinforcement learning algorithms has been implemented and compared.

River flow data analysis (Case of study: Livenza, Veneto, Italy)
(May 2018 - June 2018)

  • The main goal concerns the study of Livenza's river behaviour. In 2010, a great flooding damaged lands and, every year, the scenario seems to repeat itself. I analysed data about Livenza's meters above sea level (average level), trying to understand the trend followed in the last years.

Blackjack user assistant
(August 2018 - September 2018)

  • A C++ program (using the OpenCV library) able to process frames from either a video file or webcam and automatically identify the playing cards that are visible during the game. This information is used to infer the probability of winning or losing the game when a new card is drawn.