William (Chi Kin) Yau

West Lafayette, IN

I am a first-year PhD student at Purdue University’s Electrical and Computer Engineering (ECE) Department. I am honored to be advised by Prof. Stanley Chan. I work in the field of computational imaging, with a particular interest in the intersection between optics, photonics, and deep learning.

Prior to joining Purdue, I finished my Bachelor’s in Physics and Computer Science at UC Berkeley. I was brought into the fantastic field of computational imaging by the mentorship from Prof. Laura Waller and Leyla Kabuli. Prior to this, I did reseach on jet reconstruction algorithms at LBNL, and space plasma physics at SSL.

Outside of research, I enjoy editorial photography and Jazz music.

news

Aug 16, 2025	I joined the Chan Lab at Purdue University headed by Prof. Stanley Chan!

selected publications

Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR

William C. Yau, Weijian Zhang, Hashan Kavinga Weerasooriya, and 1 more author

Oct 2024

Abs DOI Bib

Depth estimation using a single-photon LiDAR is often solved by a matched filter. It is, however, error-prone in the presence of background noise. A commonly used technique to reject background noise is the rank-ordered mean (ROM) filter previously reported by Shin et al. (2015). ROM rejects noisy photon arrival timestamps by selecting only a small range of them around the median statistics within its local neighborhood. Despite the promising performance of ROM, its theoretical performance limit is unknown. In this paper, we theoretically characterize the ROM performance by showing that ROM fails when the reflectivity drops below a threshold predetermined by the depth and signal-to-background ratio, and its accuracy undergoes a phase transition at the cutoff. Based on our theory, we propose an improved signal extraction technique by selecting tight timestamp clusters. Experimental results show that the proposed algorithm improves depth estimation performance over ROM by 3 orders of magnitude at the same signal intensities, and achieves high image fidelity at noise levels as high as 17 times that of signal. The code for this project is made available at https://github.com/yauwilliam69/NCFforLiDAR.git.
@article{yau2024ROManalysis, author = {Yau, William C. and Zhang, Weijian and Weerasooriya, Hashan Kavinga and Chan, Stanley H.}, booktitle = {2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)}, title = {Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR}, year = {2024}, volume = {}, number = {}, pages = {1-6}, keywords = {Reflectivity;Matched filters;Laser radar;Accuracy;Three-dimensional displays;Signal processing algorithms;Background noise;Read only memory;Photonics;Signal to noise ratio;Rank-ordered mean (ROM);depth estimation;single-photon LiDAR 3-D imaging;computational imaging;con-vex optimization;first-photon imaging;LiDAR;low-light imaging;Poisson noise;single-photon detection;time-of-flight imaging}, doi = {10.1109/MMSP61759.2024.10743397}, issn = {2473-3628}, month = oct, dimensions = {true}, }