Emanuel Gouveia

I received my PhD in Physics in 2021, from the University of Minho. I studied properties of the interaction between the top quark and the Higgs boson, analyzing data collected by the ATLAS experiment at CERN.

From 2021 to 2024, I worked as a data scientist at DTx CoLab, in multiple projects under the broad scope of digital transformation, delivering prototypes of data-driven systems to industry partners. During part of this period, I coordinated the Data Science and Machine Learning group.

Since 2024, I teach and research at Universidade Católica Portuguesa, in Braga.

My main research interests are data-efficient machine learning and domain-informed machine learning. In particular, I'm interested in methods for making neural networks more data-efficient by leveraging known dynamics and symmetries of the domain. These include self-supervised learning, equivariant networks, physics-informed neural networks and data augmentation techniques. I'm also interested in applications of such methods to problems in science and industry.