Biography

I’m a PhD candidate at the NYU Center for Data Science, doing cognitive science research advised by Brenden Lake and Todd Gureckis. My research interests center around cognitively-inspired machine learning: how can we draw inspiration from human cognition to advance the design of machine learning methods. My thesis research studies how people represent cognitive goals and tasks and proposes computational models generating similarly rich and human-like goals. I’m excited about how richer goal representations could facilitate exploration and generalization in artificial agents.

In my non-academic life, I enjoy playing ultimate frisbee, making homemade hot sauces, and making friends with all the dogs in Brooklyn.

Interests
  • Cognitive representations of goals and tasks
  • Generating human-like goals for artificial agents
  • Cognitive modeling using neural networks
Education
  • PhD in Data Science, 2019--

    New York University

  • BSc in Computational Sciences, 2015--2019

    Minerva University

Recent Publications

(2024). Spatial relation categorization in infants and deep neural networks. Cognition.

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(2024). Toward Human-AI Alignment in Large-Scale Multi-Player Games.

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(2023). Generating Human-Like Goals by Synthesizing Reward-Producing Programs. Intrinsically Motivated Open-ended Learning @ NeurIPS 2023.

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(2023). Spatial Relation Categorization in Infants and Deep Neural Networks. Cognition (in press).

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(2022). Creativity, Compositionality, and Common Sense in Human Goal Generation. Proceedings of the 44th Annual Meeting of the Cognitive Science Society, CogSci 2022.

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(2021). Examining Infant Relation Categorization Through Deep Neural Networks. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, CogSci 2021.

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(2021). A model of mood as integrated advantage. Psychological Review.

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(2020). Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society, CogSci 2020.

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(2020). Systematically Comparing Neural Network Architectures in Relation Learning. Object-Oriented Learning (OOL): Perception, Representation, and Reasoning Workshop at ICML 2020.

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(2020). Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget . The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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(2019). Contrasting the effects of prospective attention and retrospective decay in representation learning. The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making.

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(2019). Momentum and mood in policy-gradient reinforcement learning. The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making.

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Summer Schools

Brains, Minds, and Machines Summer Course
Attended the 2021 Brains, Minds, and Machines summer course in Woods Hole, MA.
Machine Learning Summer School
Attended the July 2019 MLSS in London, England.