Weijian Zhang

Elmore Family School of ECE

Purdue University

West Lafayette, IN

I am a Ph.D. candidate at Purdue University, advised by Prof. Stanley H. Chan in the Intelligent Imaging Lab.

Research. I work at the intersection of computational imaging and statistical signal processing, on single-photon LiDAR. A single-photon detector goes blind for a moment after every detection, and at high photon flux this dead time systematically distorts the timing histogram that depth estimation depends on. Most systems avoid the problem by attenuating the signal. I would rather model it: my work builds forward models of the photon-registration process under dead time, proves what can and cannot be recovered from those measurements, and turns the resulting theory into simulators fast enough to generate real training data — most recently a Markov-renewal formulation that predicts photon-count statistics in closed form, and a Markov-chain model that runs three orders of magnitude faster than its predecessor.

Previously. I received my M.S. in Electrical and Computer Engineering from UCLA and my B.Eng. in Optoelectronic Information Science and Engineering from the University of Electronic Science and Technology of China. At UESTC I worked with Prof. Zhao Wang on optical coherence tomography system development.

Outside research. Basketball 🏀, weight lifting 🏋️, and — more recently — running 🏃. See the gallery.

CV Google Scholar GitHub Email

I am looking for a Summer 2027 internship in LiDAR, radar, novel sensors, depth estimation, or computational imaging more broadly. My CV has the details, and the fastest way to reach me is email.

News

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Aug 01, 2026	I am on the market for a Summer 2027 internship in LiDAR, depth sensing, and computational imaging. Get in touch.
Jan 15, 2026	Real-Time Markov Modeling for Single-Photon LiDAR accepted to ICASSP 2026 — a 1000× acceleration with convergence guarantees.
Dec 04, 2025	New preprint: Markov-Renewal Single-Photon LiDAR Simulator — the first analytic prediction of photon-count mean and variance under dead time.
May 29, 2025	Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping accepted to ICIP 2025.
Oct 01, 2024	Two papers accepted to MMSP 2024 — a parametric dead-time forward model for photon registrations, and an analysis of the rank-ordered mean estimator.

Selected publications

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Preprint
Markov-Renewal Single-Photon LiDAR Simulator

Weijian Zhang, Prateek Chennuri, Hashan K. Weerasooriya, Bole Ma, and Stanley H. Chan

arXiv preprint arXiv:2512.04924, Dec 2025

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Single-photon LiDAR (SP-LiDAR) simulators face a dilemma: fast but inaccurate Poisson models or accurate but prohibitively slow sequential models. This paper breaks that compromise. We present a simulator that achieves both fidelity and speed by focusing on the critical, yet overlooked, component of simulation: the photon count statistics. Our key contribution is a Markov-renewal process (MRP) formulation that, for the first time, analytically predicts the mean and variance of registered photon counts under dead time. To make this MRP model computationally tractable, we introduce a spectral truncation rule that efficiently computes the complex covariance statistics. By proving the shift-invariance of the process, we extend this per-pixel model to full histogram cube generation via a precomputed lookup table. Our method generates 3D cubes indistinguishable from the sequential gold-standard, yet is orders of magnitude faster.
@article{zhang2025markovrenewal, title = {Markov-Renewal Single-Photon LiDAR Simulator}, author = {Zhang, Weijian and Chennuri, Prateek and Weerasooriya, Hashan K. and Ma, Bole and Chan, Stanley H.}, journal = {arXiv preprint arXiv:2512.04924}, year = {2025}, month = dec, doi = {10.48550/arXiv.2512.04924}, }
ICASSP
Real-Time Markov Modeling for Single-Photon LiDAR: 1000x Acceleration and Convergence Analysis

Weijian Zhang, Hashan K. Weerasooriya, Prateek Chennuri, and Stanley H. Chan

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dec 2026

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Asynchronous single-photon LiDAR (SP-LiDAR) is an important imaging modality for high-quality 3D applications and navigation, but the modeling of the timestamp distributions in the presence of dead time remains a challenge. We present a Markov-chain formulation that accelerates the computation of the steady-state timestamp distribution by three orders of magnitude, together with a convergence analysis that characterizes how quickly the chain reaches its stationary regime.
@inproceedings{zhang2026realtime, title = {Real-Time Markov Modeling for Single-Photon LiDAR: 1000x Acceleration and Convergence Analysis}, author = {Zhang, Weijian and Weerasooriya, Hashan K. and Chennuri, Prateek and Chan, Stanley H.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2026}, }
ICIP
Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping

Weijian Zhang, Hashan K. Weerasooriya, and Stanley H. Chan

In IEEE International Conference on Image Processing (ICIP), Dec 2025

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Efficient simulation of photon registrations in single-photon LiDAR is essential for generating the large-scale training data that learning-based reconstruction demands, but physically faithful sequential simulators are far too slow. We learn a neural mapping from scene and illumination parameters directly to the distorted photon-registration statistics induced by dead time, yielding an ultrafast simulator that remains accurate in the high-flux regime.
@inproceedings{zhang2025ultrafast, title = {Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping}, author = {Zhang, Weijian and Weerasooriya, Hashan K. and Chan, Stanley H.}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, pages = {2772--2777}, year = {2025}, }
MMSP
Parametric Modeling and Estimation of Photon Registrations for 3D Imaging

Weijian Zhang, Hashan K. Weerasooriya, Prateek Chennuri, and Stanley H. Chan

In IEEE International Workshop on Multimedia Signal Processing (MMSP), Dec 2024

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In single-photon LiDAR systems, histogram distortion due to hardware dead time fundamentally limits the precision of depth estimation. We derive a parametric forward model of the photon-registration process under dead time and develop estimators that invert it, recovering depth accurately in flux regimes where conventional histogram-based methods break down.
@inproceedings{zhang2024parametric, title = {Parametric Modeling and Estimation of Photon Registrations for 3D Imaging}, author = {Zhang, Weijian and Weerasooriya, Hashan K. and Chennuri, Prateek and Chan, Stanley H.}, booktitle = {IEEE International Workshop on Multimedia Signal Processing (MMSP)}, year = {2024}, }