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Brandon Y. Feng

I am a Ph.D. Candidate in Computer Science at the University of Maryland advised by Prof. Amitabh Varshney. My research focus lies at the intersection of graphics, vision, and imaging. I develop machine learning algorithms for image and 3D data processing, with applications ranging from mixed reality to natural science. My goal is to enhance our ability to represent and perceive information about our world, facilitating new creation with positive societal impact.

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paper (tba)

Efficient implicit shape representation.

Preprint, 2022

Training GAN with blurry images from the same scene distorted by changing turbulence. Recovers a sharp image without estimating the blur kernels or acquiring a large labelled dataset.


Interesting findings from evaluating AlphaFold2 on protein-protein docking, highlighting areas for future development using deep learning methods.

Brandon Y. Feng, Amitabh Varshney
project page | paper | video | code

Remarkable compression rates by representing light fields as neural network weights. Simple and compact formulation also supports angular interpolation to generate novel viewpoints.

Zhenyi He, Keru Wang, Brandon Y. Feng, Ruofei Du, Ken Perlin
project page | paper | video

Visualizing "who's looking at who" from static profile images as people always turn off their videos. Synthesized eye gazes are obtained by leveraging a general-purpose neural network.


Training neural networks for 360° monocular depth and normal estimation. Proposed a novel approach of combining depth and normal as a double quaternion during loss computation.


Neural networks with multimodal input disproportionately rely on certain modality while ignoring the rest. Developed a new strategy to overcome such bias and adapt to missing modalities.

This page is modified based on Jon Barron's website