Jacob Yeung

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

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.

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Research
edca formulation photo Discovery of Linked Neural and Behavioral Subspaces with External Dynamic Components Analysis
Jacob Yeung, Ji Hyun Bak, Kristofer Bouchard
Society for Neuroscience, 2022
description / pdf / 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.

animal reid photo Animal Re-ID & Census from Camera Traps
Jacob Yeung, Medhini Narasimhan, Trevor Darrell, Colorado Reed, Andrew Dunn, Inaoyom Imong
Google AI for Social Good Workshop, 2022
description / pdf / presentation

We generate pseudo-labels and identify subsets of images for annotation using self-supervised learning.

hangul fonts photo Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations
Jesse Livezey, Ahyeon Hwang, Jacob Yeung, Kristofer Bouchard
International Conference on Image Analysis and Processing, 2022
description / pdf

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

convolution pruning photo Convolutional Neural Network Pruning
Andy Dong*, Oscar Xu*, Jacob Yeung*
description / pdf

We present a novel convolutional filter pruning technique based on the Tucker decomposition that utilizes spatial information from convolutional outputs.

deepchrome 2 photo DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments
Saurav Kadavath*, Sam Paradis*, Jacob Yeung*
description / pdf / presentation

We present a novel visualization technique for finding relationships among histone modifications for gene expression and find the signals learned by DeepChrome are only as informative as the average histone modification counts.

Teaching

I have been a teaching assistant for the following course:

I have been a tutor for the following courses:

Experience
berkeley logo University of California, Berkeley
Aug 2018 - Dec 2022

B.S. in Electrical Engineering and Computer Science
lbnl logo Lawrence Berkeley National Laboratory
Aug 2020 - Present

Research Intern
Advisor: Dr. Kristofer Bouchard and Dr. Ji Hyun Bak

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Last updated: Jan 7, 2023