Personal Webpage of Francesco Ballerin
PhD fellow at the University of Bergen
I am currently on the job market. My PhD is expected to conclude at the end of September 2026. If you have an open position for which I may be a suitable candidate, please do not hesitate to contact me at francesco@ballerin.it.
Work Experience
PhD Candidate with Teaching Duties
University of Bergen
2022 - Current
- Research topics: Geometric Deep Learning, Differential Geometry, Computer Vision. More details available in the Research tab.
- Teaching duties: Real Analysis, Fourier Analysis, Measure and Integration Theory, Stochastic Processes.
Junior Research Assistant
FBK — Fondazione Bruno Kessler
2017 - 2019
- Predictive models for agricultural yields, quality estimation, and pesticide management from meteorological data and spectrophotometry analysis.
- Development of LoRa-Network solutions for machine learning in an IoT environment.
Education
PhD in Mathematics
University of Bergen
2022 - Current
Thesis: Geometrical Methods for Data AnalysisResearch in geometric deep learning and equivariant methods for data on manifolds and curved spaces. Covers topics including equivariant neural networks on the sphere, PDE-based data analysis, and sub-Riemannian geometry. More details are available in the Research tab.
Master's Degree in Mathematical Analysis
University of Bergen
2020 - 2022
Avg. grade: A | Thesis: Sub-Riemannian geometry and its applications to image processingAfter an introduction on sub-Riemannian geometry, focusing on the examples of the Lie group \(SE(2)\) and the projective tangent bundle \(PT\mathbb{R}^2\), applications to the field of image processing are discussed. In particular we study a model of geometry of vision for image restoration due to Petitot, Citti and Sarti and further developments by Boscain, Duplaix, Gauthier and Rossi on hypoelliptic operators. New tools and techniques based on such work are developed and discussed.
Bachelor's Degree in Mathematics
University of Trento
2017 - 2020
Grade: 107/110 | Thesis: Active Contour Models for Image Segmentation and Motion TrackingTraditional image segmentation methods based on global and local thresholds and the detection of intensity discontinuities have been widely used for numerous applications, but they intrinsically have multiple limitations. Two PDE-based active contours approaches, inspired by the fundamental snake equation presented by Kass, Witkin and Terzopoulos, are discussed. An implementation is then produced, as well as an extension of the algorithm to realize active tracking on video files.
Schools & Training
NORA Summer School
NORA AI
June 2024
School covering advanced topics in machine learning with a focus on geometric deep learning, symmetry-aware models, and their applications to structured data. Included both theoretical foundations and hands-on work with modern ML methods.
Geilo Winter School
SINTEF
January 2024
School focusing on graph-based data analysis and machine learning. Covered spectral graph theory, graph signal processing, and graph neural networks.
Skills
Programming
Python, C++, C, Java, Kotlin, JavaScript, PHP, MATLAB, MySQL
Frameworks & Libraries
PyTorch, PyTorch Geometric, NetworkX, Pandas
Tools
Git, Jupyter, LaTeX, HTML, CSS
Languages
| Italian | Mother tongue |
| English | C2 |
| Norwegian | B2 |
| German | A2 |
Certificates, Awards & Grants
| 2023 | Meltzer Universitetsstiftelse — Research grant |
| 2022 | Meltzer Universitetsstiftelse — Research grant |
| 2018 | 1st place — Vertical Innovation Hackathon, Bolzano |
| 2017 | Best Software Programming — RoboCup Junior World Championship, Nagoya |