Research
Self-Adaptive Learning and Meta-Learning
The goal is to create learning algorithms that can modify their mechanisms according to the domain under analysis and the accumulation of meta-knowledge (knowledge about the learning agent's behavior in different environments).
Applications of AI and ML in Astrophysics
Machine learning has already significantly influenced the analysis of astrophysical data. Our goal in this project is to generate new learning algorithms that can aid in the search for data patterns in astrophysics and cosmology.
Automated Scientific Discovery
We aim to find automated ways to extract new physical laws and equations from scientific data, mainly physics and astronomy. The idea is to go beyond search techniques, exploring the space of possible new equations. We plan to automate the discovery process by “learning-to-search” solutions that satisfy physical constraints and display properties typical to known proven equations.