Luca Colomba

Ph.D. candidate in Ingegneria Informatica E Dei Sistemi , 36th cycle (2020-2023)
Department of Control and Computer Engineering (DAUIN)

Research Assistant
Department of Control and Computer Engineering (DAUIN)

Docente esterno e/o collaboratore didattico
Ateneo (ATENEO)

Docente esterno e/o collaboratore didattico
Department of Control and Computer Engineering (DAUIN)



Research topic

Data Analytics and Big Spatio-Temporal Data


Research interests

Life sciences
Software engineering and Mobile computing


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, including Apache Spark framework. Moreover, he published several research papers in the field of remote sensing, Earth Observation and deep learning, focusing on emergency managament and land cover classification.
His main research topics are: Big Data Analytics, Data Mining and Machine Learning.

Awards and Honors

  • PhD Quality Awards 2023 document (2023)
  • Volunteer at ECML/PKDD2023 conference (Torino, 18-22 September 2023) (2023)
  • 2nd year PhD Quality Award (2nd prize) (2024)
  • 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)



Master of Science

MostraNascondi A.A. passati


Research groups


Latest publications View all publications in Porto@Iris