Brandon Y. Feng

I am a Postdoctoral Associate at MIT CSAIL, working with Prof. William T. Freeman, and a Visiting Scientist at Harvard-Smithsonian Center for Astrophysics, working with Dr. Cecilia Garraffo.
I develop machine learning algorithms to extract information seemingly invisible and beyond human perception. By pushing the boundaries of computational imaging and computer vision, my work aims to uncover hidden knowledge and accelerate scientific progress by expanding human vision from intuitive to algorithmic.
I completed my Ph.D. in Computer Science at the University of Maryland, advised by Prof. Amitabh Varshney and closely working with Prof. Christopher A. Metzler and Prof. Jia-Bin Huang.

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Exoplanet Imaging via Differentiable Rendering

Exoplanet Imaging via Differentiable Rendering

Brandon Y. Feng, Rodrigo Ferrer-Chávez, Aviad Levis, Jason J. Wang, Katherine L. Bouman, William T. Freeman
IEEE TCI 2024

Seeing new light from exoplanets.

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NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

Brandon Y. Feng*, Haiyun Guo*, Mingyang Xie, Vivek Boominathan, Manoj K. Sharma, Ashok Veeraraghavan, Christopher A. Metzler
Science Advances 2023 Science.org Headline

Neural signal representations enable breakthroughs in correcting for severe time-varying wavefront aberrations caused by scattering media.

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3D Motion Magnification: Visualizing Subtle Motions with Time-Varying Neural Fields

3D Motion Magnification: Visualizing Subtle Motions with Time-Varying Neural Fields

Brandon Y. Feng*, Hadi AlZayer*, Michael Rubinstein, William T. Freeman, Jia-Bin Huang
ICCV 2023

3D motion magnification allows us to magnify subtle motions in seeamingly static scenes while supporting rendering from novel views.

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FPM-INR: Fourier Ptychographic Microscopy Image Stack Reconstruction Using Implicit Neural Representations

FPM-INR: Fourier Ptychographic Microscopy Image Stack Reconstruction Using Implicit Neural Representations

Haowen Zhou*, Brandon Y. Feng*, Haiyun Guo, Siyu Lin, Mingshu Liang, Christopher A. Metzler, Changhuei Yang
Optica 2023

Physics-based neural signal representations accelerate real-time 3D refocusing in Fourier ptychographic microscopy and overcome barriers to clinical diagnosis.

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Seeing the World Through Your Eyes

Seeing the World Through Your Eyes

Hadi AlZayer*, Kevin Zhang*, Brandon Y. Feng, Christopher A. Metzler, Jia-Bin Huang
CVPR 2024 Oral Presentation Acc. Rate: 0.78%

The only true voyage of discovery would be not to visit strange lands, but to possess other eyes, to behold the universe through the eyes of another.

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VIINTER: View Interpolation with Implicit Neural Representations of Images

VIINTER: View Interpolation with Implicit Neural Representations of Images

Brandon Y. Feng, Susmija Jabbireddy, Amitabh Varshney
SIGGRAPH Asia 2022

View interpolation without 3D reconstruction or correspondence.

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PRIF: Primary Ray-based Implicit Function

PRIF: Primary Ray-based Implicit Function

Brandon Y. Feng, Yinda Zhang, Danhang Tang, Ruofei Du, Amitabh Varshney
ECCV 2022

Efficient implicit 3D shape representation.

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SIGNET: Efficient Neural Representations for Light Fields

SIGNET: Efficient Neural Representations for Light Fields

Brandon Y. Feng, Amitabh Varshney
ICCV 2021 Oral Presentation Acc. Rate: 3.36%

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

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