Germán Abrevaya
I am a Physics PhD student at the University of Buenos Aires, in constant collaboration with Mila and IBM. My research is centered on Scientific Machine Learning (SciML), specifically on how to effectively incorporate prior knowledge into time series machine learning models through differential equations in order to enhance performance, interpretability, and data efficiency.
I've been applying my models to decode and predict human brain dynamics, but these general methods can be applied to many other domains such as wearables, biomarkers, digital twins, climate, finance, and other applications involving complex high dimensional time series data.
I am currently in the last months of my PhD program and open to interesting job opportunities.
Session
In this talk we will show you how to boost your latent SciML model, significantly increasing its performance and data efficiency.
In particular, we will present GOKU-UI, an evolution of the continuous-time generative model GOKU-net, which incorporates attention mechanisms and a novel multiple shooting training strategy in the latent space. On both simulated and empirical brain data, it achieves enhanced performance in reconstruction and forecast tasks while effectively capturing brain dynamics.