Main Interests

Basic problems in learning from data and collective behavior

Methods

Mathematical modelling and analysis tools, AI, behavior

Systems

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

Interested?

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 gonzalo.polavieja@neuro.fchampalimaud.org

Visit also our Champalimaud site

Want to join our lab?

CHECK FOR OPPORTUNITIES

AFFILIATION

Champalimaud Foundation

Champalimaud Centre for the Unknown

Avenida Brasília, 1400-038 Lisbon, Portugal

T (+351) 210 480 200

gonzalo.polavieja(at)research.fchampalimaud.org

FUNDING