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I am a second year PhD student in Brain and Cognitive Sciences at MIT advised by Josh Tenenbaum, working at the intersection of robotics and developmental psychology. Previously, I was a research intern at Facebook AI Research, working with Brenden Lake and Marco Baroni, at Harvard with Sam Gershman, and at the Inria Parietal team with Bertrand Thirion and Gaël Varoquaux. I've studied at École Normale Supérieure Paris-Saclay, École Polytechnique and Universidade de São Paulo. Email / CV / Google Scholar / Github |
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My work focuses on how children's rich theories of the world and sophisticated mental simulations are used to support action. Key areas of interest include planning, tool use, and spatial reasoning. |
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Developmental psychology presents us with a puzzle: though children are remarkably apt at planning their actions, they suf- fer from surprising yet consistent shortcomings. We argue that these patterns of triumph and failure can be broadly captured by the framework of task and motion planning, where plans are hybrid entities consisting of both a structured, symbolic skeleton and a continuous, low-level trajectory. |
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We present a framework for learning constraint-based task and motion planning models using gradient descent. Our model observes expert demonstrations of a task and decomposes them into modes—segments which specify a set of constraints on a trajectory optimization problem. |
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We present a model that starts out with a language of low-level physical constraints and, by observing expert demonstrations, builds up a library of high-level concepts that afford planning and action understanding. |
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We extend the study of systematic compositionality in seq2seq models to settings where the model needs only to recombine well-trained functional words. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized |
 
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Work on human-level learning in Atari-like games, learning theories from gameplay and using them to plan in a model-based manner. |
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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. |
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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. |
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