Luca Colomba

Dottorando in Ingegneria Informatica E Dei Sistemi , 36o cycle (2020-2023)
Dipartimento di Automatica e Informatica (DAUIN)

Docente esterno e/o collaboratore didattico
Dipartimento di Automatica e Informatica (DAUIN)


Dottorato di ricerca

Argomento di ricerca

Data Analytics and Big Spatio-Temporal Data


Interessi di ricerca

Machine Learning
Artificial intelligence


Luca Colomba graduated in Computer Engineering at Politecnico di Torino, Italy in 2019 with the thesis "Automatic processing of satellite acquisitions for burnt area detection and damage estimation". From February to April 2020, he was involved in a post-graduate research activity at Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino. He is currently a PhD student in "Data Analytics and Big Spatio-Temporal Data" at the SmartData Center in Politecnico di Torino.
He is interested in the application of data mining and machine learning algorithms to large volumes of data, with a focus on geospatial and temporal information, in order to extract valuable information, perform predictions and identify patterns using Python's data science ecosystem, Apache Spark and Apache Storm frameworks. Data analysis and data mining algorithms are considered both in an offline and online context: spatio-temporal data can be exploited in real-time systems to analyze data streams in many different fields such as natural hazards, disaster prevention, environmental monitoring systems and smart cities.
His main research topics are: Big Data Analytics, Data Mining and Machine Learning.

Premi e riconoscimenti

  • PhD Quality Awards 2023 document (2023)
  • Travel Grant Award to PhD Students for SIGSPATIAL2022 conference, given to volunteers who helped throughout the conference. (2023)
  • 2nd place at Waste Classification Challenge held during International Summer School on Deep Learning 2021 (ISSonDL2021): the challenge consisted in developing and training a Deep Learning model to perform binary classification on images. Images represented different items that needed to be classified either as "Recyclable" or "Organic". To make the challenge more difficult, the organisers randomly added noise to all images in the form of random black rectangles. (2021)



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