Andrea Pagnani

Andrea Pagnani is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic career spans theoretical physics, bioinformatics, and complex systems research with strong interdisciplinary applications to biological problems.

Dr. Pagnani's research focuses on the intersection of statistical physics, computational biology, and machine learning. His work particularly emphasizes protein sequence analysis, statistical inference methods, and complex biological systems modeling. He applies theoretical physics approaches to solve challenging problems in bioinformatics, including protein structure prediction, antibody design, and evolutionary modeling. His research bridges the gap between theoretical frameworks and practical biological applications.

His recent publications demonstrate a clear trajectory toward increasingly sophisticated integration of machine learning with traditional statistical physics approaches for biological problems. The research shows progression from fundamental statistical methods to attention mechanisms and explainable AI models for protein design, with consistent emphasis on connecting theoretical frameworks with experimental validation.

His notable scientific achievements include:

• National Scientific Qualification - Second level - 02/A2 - THEORETICAL PHYSICS OF FUNDAMENTAL INTERACTIONS (2013)
• Fellowship at Universite Pierre et Marie Curie (UPMC-Jussieu), Paris (2013)
• Editorial board member for PLOS ONE (2019-present)

Dr. Pagnani actively supervises PhD students working on protein sequence analysis, with current research focusing on unsupervised inference models for protein properties and language models for generative protein design. He leads multiple research projects including EXMOPRODE (Explainable Models for Protein Design, 2023-2025) and MSGEP (Generative statistical models for protein evolution, 2024-2026). His research is supported by various funding mechanisms including EU Horizon 2020 programs, national PRIN grants, and industry collaborations with organizations like HiFiBio and the Cuban Center for Molecular Immunology.

He is a key member of the Statistical Physics and Interdisciplinary Applications research group within the Condensed Matter Physics and Complex Systems Institute (DISAT), where his work combines theoretical physics approaches with cutting-edge computational methods to address complex biological challenges.