Geometry for physical and biological applications. Coordinator: Fioresi

Comprehending the interaction between Geometry and Artificial Intelligence to advance the theoretical study of both disciplines and to promote their use into the context of biological applications.

Research Themes

  • Study of symmetries in physical modelling. Description: We study the mathematical theories of symmetry and quantum symmetry and their applications to theoretical physics.
  • Mathematics of Machine Learning and Geometric Deep Learning. Description: We study the theory of Machine Learning (ML), Deep Learning (DL) and Geometric Deep Learning (GDL) from a mathematical viewpoint. In particular, we aim to understand the deep theoretical links between some recent artificial intelligence algorithms and some areas of pure mathematics such as riemannian geometry, algebraic geometry, Lie theory, sheaf theory and homotopy theory.
  • Geometric based Machine Learning algorithms for the study of biological databases. Description: We develop ML algorithms harnessing the intrinsic geometric structure of biological databases to extract valuable information from them and to solve applied problems that cannot be solved using standard methodologies. We specialize into the study of medical images, DNA data, time series of biological signals, etc.

Lab Members

Rita Fioresi, Associate Professor

Emanuele Latini, Associate Professor (Dept. Mathematics, Unibo)

Giovanni Perini, Full Professor

Ferdinando Zanchetta, Junior Assistant Professor

Michela Lapenna, PhD Student (Department of Physics and Astronomy "Augusto Righi")

Junaid Razzaq,  Dottorando (Department of Physics and Astronomy "Augusto Righi")

Job Openings or Internship Projects 

At the moment, we have no open positions.

Main Publications

  • Grementieri, L., Fioresi, R. “Model Centric Data Manifold: The Data Through the Eyes of the Model” (2022), Siam Journal on Imaging Sciences, 15, 1140-1156.
  • Fioresi, R., Faglioni, F., Sena, P. (2018) “Medical Database for Detecting Neoplastic Lesions in Human Colorectal Cancer with Deep Learning” Biomedical Journal of Scientific & technical research
  • Zanchetta, F. Simonetti, A., Faglioni, G., Malagoli, A., Fioresi, R. (2022). “A geometric Deep Learning approach to blood pressure regression” GeoMedIA 2022 Extended Abstract.