Jacob Yeung

Hello! I am a 1^st year Ph.D. student in Neural Computation at Carnegie Mellon University supported by an NSF Graduate Research Fellowship.

My primary interests lie in understanding how vision can be used to guide motor behavior and using inspiration from biology towards improving computational models. To this end, my recent research explores subspace identification in neural and behavioral data, and self-supervised learning for images.

I recently graduated with a B.S. in Electrical Engineering and Computer Science (EECS) from UC Berkeley. My research was advised by Dr. Kristofer Bouchard and Dr. Ji Hyun Bak at the Lawrence Berkeley National Laboratory. I also worked with Medhini Narasimhan and Prof. Trevor Darrell at Berkeley AI Research (BAIR).

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We introduce external Dynamics Components Analysis (eDCA), a linear dimensionality reduction method that finds subspaces of past neural data that have maximal mutual information with subspaces of future behavior data.

We introduce a dataset with known hierarchical and compositional structure and show deep unsupervised models poorly learn the latent generative structures.

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Last updated: Sep 13, 2023