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Postdoc in Machine LearningPost Doc
Cultural Heritage Technologies
Marco Fiorucci is a Post Doc Researcher in Machine Learning at the Centre for Cultural Heritage Technology (CCHT) of the Istituto Italiano di Tecnologia (IIT). He is working mainly in the fields of Machine Learning, with particular emphasis on unsupervised representation learning. He is a computer scientist with a solid broad background, spanning from Physics and Machine Learning to Graph Theory and Statistics, who has successfully worked on several different interdisciplinary projects both in academia and in industry. He received the Master degree (summa cum laude) and his PhD in Computer Science from Ca’ Foscari University of Venice respectively in 2015 and 2019. His PhD thesis proposes a robust and principled graph summarization method its relevance in the context of Structural Pattern Recognition. He held visiting research position at the University of Alicante and at VTT (Finland). More recently, he has shifted his attention to the analysis of EO data (multispectral and hyperspectral data) for the detection of sub-surface archaeological sites, of looting activities and of hyperspectral data for pigment identification in painting. Current research on the topic includes the development of algorithms for the automated identification of cultural heritage objects using EO data, including semi-supervised methods for the detection of anomalies in time series and ML methods to directly process LiDAR point clouds. In addition to research, Marco is one of the co-founder and co-organizer of DataBeersVenezia and one of the communication managers of the CCHT.
Davoudi H., Fiorucci M., Traviglia A.
Ancient Document Layout Analysis: Autoencoders meet Sparse Coding
25th International Conference on Pattern Recognition
Conference Paper Conference
Fiorucci M., Davoudi H., Traviglia A.
Semi-supervised classification of ancient coins using graph neural networks
Conference on Computer Applications and Quantitative Methods in Archaeology (CAA)
Abstract Report Conference
Reittu H., Leskela L., Raty T., Fiorucci M.
Analysis of large sparse graphs using regular decomposition of graph distance matrices
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 3784-3792
L'intelligenza artificiale a servizio dell'arte
Graph Summarization Using Regular Partition and Its Use in Graph Search
Department of Mathematics and Systems Analysis, Aalto University
Structural Big Data and Regular Partitions
Department of Computer Science and Artificial Intelligence, University of Alicante
Traviglia A., Fiorucci M.
Graph Convolutional Neural Networks for Cultural Heritage: Applications in RS recognition, numismatics and epigraphy
Machine Learning in Archaeology, Rome
Pelosin F., Fiorucci M., and Pelillo M.
Graph Summarization Using Regular Partitions
The 8th International Conference on Network Analysis, Moscow
Traviglia A., Artesani A., Fiorucci M., Ljubenovic M.
European Physical Journal Plus
Colleagues of Cultural Heritage Technologies