Basic problems in learning from data and collective behavior
Mathematical modelling and analysis tools, AI, behavior
Algebras, Zebrafish and human groups, general data structures
We study basic problems in acquiring knowledge from data.
Specifically, we are now dealing with (a) working out relationships between compression and accuracy, (b) building intelligent systems based on algebras (algebraic.ai), (c) using deep nets, RL and other ML systems to build better agent-based models that we compare with principled approaches and behavioral data and (d) similar to (c) but to understand neuronal circuits.
To obtain good datasets of collective behavior, we also build tracking and
behaviour analysis systems, our latest being idtracker.ai
If you are interested in basic problems of learning from data or collective behavior, like maths and coding, work both independently and in a team, like to understand well a problem and also present it nicely to the community, be open about code an data, maybe this is your place.
Contact us at firstname.lastname@example.org
Visit also our Champalimaud site
Champalimaud Centre for the Unknown
Avenida Brasília, 1400-038 Lisbon, Portugal
T (+351) 210 480 200