About Me
I obtained my PhD in Computer Science at Politecnico di Milano. During my PhD I acquired a solid experience in image processing, data analysis, Artificial Intelligence (AI), and Computer Vision (CV). I have 4+ years of experience in Python scripting and software development, excellent knowledge of data science libraries, Git, and Docker,, and 2+ years of Java development and relational databases. I applied my expertise to cultural heritage and remote sensing data.
I am Italian, and I am willing to relocate. I love playing tennis, climbing, and skiing in my free time.
I speak Italian and English proficiently. I can understand and speak Spanish. I am learning German.
I am most skilled in: Python, PyTorch, Tensorflow, Pandas, Docker, Swift, Git, MacOS, Unix.
I am also competent in: OpenCV, Java, NodeJS, working with Windows and Flask.
Education
Politecnico di Milano
Ph.D. in Computer Science
November 2019 - March 2023
Department of Electronics Information and Bioengineering (DEIB)
Researching on Deep Learning (DL) architectures and Computer Vision (CV) techniques for the analysis of Cultural Heritage data to support complex iconography studies and the enrichment of public online collections. Currently working on ArtDL.
Thesis title: “Analysis of Cultural Heritage Data for Complex Iconography Studies”
Research focus: Artificial Intelligence, Computer Vision, Image Classification, Object Detection, Data Analysis
Politecnico di Milano
MSc in Computer Science and Engineering
October 2016 - December 2018
MSc track on Ambient Intelligence and Data Engineering
Master thesis on the feasibility of Deep Learning (DL) architectures for landform extraction with the final aim to enrich Volunteered Geographic Information (VGI) systems, such as OpenStreetMap (OSM).
Thesis title: “Learning to find mountains”
Politecnico di Milano
BSc in Computer Science and Engineering
October 2013 - September 2016
Experience
Department of Electronics Information and Bioengineering (DEIB)
Researched on Deep Learning architectures and Computer Vision techniques for cultural heritage and remote sensing data. Worked with state-of-the-art image classification, object detection and instance segmentation models.
- Led the ArtDL project, obtained state-of-the-art results on iconography identification through Deep Learning and Computer Vision techniques with potential use in interactive museums applications and improvement of online collections.
- Actively took part in H2020 projects for the processing and analysis of remote sensing data, drastically reducing manual photointerpretation time and human errors in sensitive use cases (e.g., illegal landfills identification, drone search and rescue).
- Co-authored 10+ publications in international journals and conferences in the Deep Learning and Computer Vision field.
- Co-supervised 10+ MSc students with thesis focused on Deep Learning, Computer Vision, and Machine Learning. More than 50% of the thesis led to an international publication.
- Assisted and tutored BSc and MSc courses for multiple academic years (2019 to 2023): Web Technologies (5 cfu, 300+ students), Data Bases 2 (5 cfu, 300+ students).
Department of Electronics Information and Bioengineering (DEIB)
Taught and tutored multiple BSc and MSc courses (Web Technologies, Databases 2) with 300+ students. Prepared Java projects and technical documentations, and corrected exams and assignments.
Projects
ArtDL
https://artdl.orgPython, PyTorch, Tensorflow, Jupyter Notebook, Numpy, Pandas, Scikit-learn, OpenCV, Pillow
Created and released a public data set of 43k paintings, implemented and evaluated Deep Learning (DL) models for Christian iconography localization. Acquired expertise in data preparation and analysis, Artificial Intelligence, Computer Vision and data science Python libraries.
Python, Jupyter Notebook, Docker, Pandas, Plotly, Numpy
Contributed to the development of an innovative open-source framework assisting Machine Learning (ML) applications in the stages of data preparation, model evaluation, and black-box error diagnosis. ODIN is entirely developed following Python and unit testing best practices.
Publications
C. Fasana, S. Pasini, F. Milani, P. Fraternali Weakly Supervised Object Detection for Remote Sensing Images: A Survey (2022) MDPI - Remote Sensing
F. Milani, N. O. Pinciroli Vago, P. Fraternali Proposals Generation for Weakly Supervised Object Detection in Artwork Images (2022) MDPI - Journal of Imaging
P. Fraternali, F. Milani, R. N. Torres and N. Zangrando Black-box Error Diagnosis in Deep Neural Networks for Computer Vision: a Survey of Tools (2022) Springer - Neural Computing and Applications
R. N. Torres, F. Milani and P. Fraternali ODIN: Pluggable Meta-annotations and Metrics for the Diagnosis of Classification and Localization (2021, October) in LOD 2021: Machine Learning, Optimization, and Data Science
N. O. Pinciroli Vago, F. Milani, P. Fraternali and R. da Silva Torres Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis (2021) MDPI - Journal of Imaging
F. Milani and P. Fraternali A Data Set and a Convolutional Model for Iconography Classification in Paintings (2021) ACM - Journal on Computing and Cultural Heritage
R. N. Torres, F. Milani and P. Fraternali Algorithms for mountain peaks discovery: a comparison (2019, April) In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 667-674)
R. N. Torres, P. Fraternali, F. Milani and D. Frajberg Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods (2019) Applied Geomatics, 1-22
R. N. Torres, P. Fraternali, F. Milani and D. Frajberg A Deep Learning model for identifying mountain summits in Digital Elevation Model data (2018, September) In 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 212-217)