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Hello! I am a second-year Ph.D. student in the Neural Computation & Machine Learning joint Ph.D. program at Carnegie Mellon University supported by an NSF Graduate Research Fellowship. I am advised by Prof. Michael Tarr. My primary interests lie in understanding how vision can be used to guide motor behavior and using inspiration from biology towards improving computational models. I 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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Jacob Yeung, Andrew F. Luo, Gabriel Sarch, Margaret M. Henderson, Deva Ramanan, Michael J. Tarr Arxiv, 2023 (in submission) Paper We propose a way to study visual motion in the brain by decoupling the modeling of static image representations and motion representations in the human brain. |
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Jacob Yeung, Ji Hyun Bak, Kristofer Bouchard COSYNE, 2023 Paper / Poster 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. |
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Jesse Livezey, Ahyeon Hwang, Jacob Yeung, Kristofer Bouchard International Conference on Image Analysis and Processing, 2022 Paper 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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I have been a teaching assistant for the following course: |
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I have been a tutor for the following courses: |
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Cloned from here!
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