João Loula

I am a Master's student in Applied Math and Computer Science at École Normale Supérieure Paris-Saclay. I'm working with Professors Josh Tenenbaum at MIT and Sam Gershman at Harvard on model-based AI for human-like learning in videogames.

I've previously worked at the Inria Parietal team with Bertrand Thirion and Gaël Varoquaux, and at Collège de France with Jonathan Touboul. I've studied at École Polytechnique and Universidade de São Paulo.

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I'm interested in the feedback loop between cognitive science and AI. More specifically I want to understand how people learn flexible models of the world and use them to perform a variety of tasks, and how we can get computers to do the same. Key areas of interest include reinforcement learning, probabilistic programming and program induction.

Human Learning of Video Games
Pedro Tsividis, João Loula, Jake Burga, Thomas Pouncy, Sam Gershman, Josh Tenenbaum
NIPS Workshop on Cognitively Informed Artificial Intelligence (Spotlight Talk), 2017

Work on human-level learning in Atari-like games, learning theories from gameplay and using them to plan in a model-based manner.

Decoding fMRI activity in the time domain improves classification performance
João Loula, Gaël Varoquaux, Bertrand Thirion
NeuroImage, 2017

We show that fMRI decoding can be cast as a regression problem: fitting a design matrix with BOLD activation: event classification is then easily obtained from the predicted design matrices. Our experiments show this approach outperforms state of the art solutions, especially for designs with low inter-stimulus intervals, and the two-step nature of the model brings time-domain interpretability.

Loading and plotting of cortical surface representations in Nilearn
Julia Huntenburg, Alexandre Abraham, João Loula, Franziskus Liem, Kamalaker Dadi, Gaël Varoquaux
Research Ideas and Operations, 2017

We present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.


Reinforcement learning in the brain


Analyzing functional connectivity with Nilearn


Parallelized tracking using Siamese CNNs


Plotting cortical surfaces using Nilearn


Robust face verification


Brownian motion: a primer

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