The group
People
The group brings together researchers with backgrounds in mathematics, statistics, computer science and engineering, working closely with clinicians, biologists and industrial experts.
Group leader
Laura Azzimonti
Senior Researcher and Lecturer, IDSIA USI-SUPSI
Group Leader, SIB Swiss Institute of Bioinformatics
Laura Azzimonti leads the Knowledge-Informed AI group and the IDSIA research area AI in Health and Life Sciences. Her research develops machine learning methods that integrate data with prior knowledge: physics-informed machine learning, causal AI and probabilistic graphical models, and federated learning. She is principal investigator, or SUPSI principal investigator, of projects funded by Horizon Europe and Innosuisse and of direct research mandates with hospitals and companies. She holds a PhD in Mathematical Models and Methods in Engineering from Politecnico di Milano and has worked at IDSIA since 2015.
Team
-
Daniele Malpetti
Researcher and Lecturer
Coordinates the federated learning research line. Works on federated learning methods and software for biomedical data and on interpretable machine learning for transcriptomics, including the use of single-cell knowledge in the analysis of bulk RNA-seq data.
-
Lea Multerer
Researcher
Works on physics-informed machine learning and mechanistic modelling, in particular physics-informed neural networks, reduced models and surrogate models of glucose and insulin dynamics in type 2 diabetes.
-
Clara Galimberti
Researcher
Works on physics-informed machine learning and system identification, in particular physics-informed neural networks, physics-informed meta-learning and physics-informed flow matching.
-
Giovanni Angelotti
PhD student
Works on causal AI for treatment decisions in intensive care, including target trial emulation and the estimation of individual treatment effects from routinely collected clinical data.
-
Manuel Acquistapace
Master's student
Works on physics-informed machine learning and physics-informed meta-learning. His master's thesis is on physics-informed flow matching.
-
Christian Berchtold
Master's student
Works with Daniele Malpetti on federated learning for clinical applications. He has also worked on diffusion models for biomedical images.
Former members
-
Sandra Mitrović, Researcher (2020–2025)
-
Marta Lenatti, Visiting PhD student (2025)
-
Giada Triulzi, Visiting master's student (2026)
Join us
We welcome students and researchers interested in machine learning grounded in scientific and domain knowledge.
- Master's and bachelor's theses for students at SUPSI, USI and partner universities, often on data and questions from our clinical and industrial collaborations.
- PhD and postdoctoral positions, announced on this page and on the IDSIA website.
- Internships and research visits for master's and PhD students.
There are no open calls at the moment. We also consider unsolicited applications from motivated candidates, particularly when they can be linked to a fellowship or a joint project proposal.
How to apply. Send an email to Laura Azzimonti with a CV, a transcript of records, a short statement of your research interests and the names of two referees. For theses, indicate your study programme and when you plan to start.
Contact
Laura Azzimonti, group leader
Polo universitario Lugano, Campus Est
Via la Santa 1
6962 Lugano-Viganello, Switzerland