Bernat Casajuana, an undergrad Biomedical student at the CBBL, attended and presented at MDAI 2026, the 23rd International Conference on Modeling Decisions for Artificial Intelligence, held in Vic from September 7 to 9, 2026.
The meeting focused on the mathematical foundations of decision-making in AI, including the aggregation of information from multiple sources, human decision models, and explainable machine learning—concepts close to how physics-informed neural networks combine data and physical priors in a single model.
Bernat’s talk, “Physics-Informed Neural Networks for Parameter Recovery in the Repressilator Oscillatory Model,” presented work on using PINNs to recover the true kinetic parameters of the Repressilator, a small synthetic gene oscillator, directly from noisy and partially observed simulated data.
Work carried out with Roger Casals-Franch, Adrián López García de Lomana, Pere Martí-Puig, and Jordi Villà-Freixa (IRIS-CC · UVic-UCC · University of Iceland).
Bernat presented his project a few days later, as a poster, at the 3rd Biology for Physics Conference in Barcelona.