I am a PhD candidate in the Department of Electrical Engineering at Caltech, advised by Prof. Changhuei Yang. My current research interests include:

(a) Computational microscopy with model-based methods;
(b) Physics-based neural network for microscopic image reconstruction;
(c) Vision-based learning-assisted methods with imaging system co-designs for digital pathology.

My aspiration is to become an engineer-scientist who advances scientific discovery through innovative engineering designs.


News
12/03/2024 - I am awarded Schmidt Academy for Software Engineering Graduate Research Fellowship. The support will start on Jan. 1st, 2025 for contributions to the algorithm and software development towards computational microscopy, specifically Fourier Ptychographic Microscopy. [Schmidt Academy GRA Fellows]
08/06/2024 - Our new paper titled "Investigating 3D microbial community dynamics of the rhizosphere using quantitative phase and fluorescence microscopy" is online at PNAS . [Science.org News]
05/24/2024 - I am awarded SPIE Optics and Photonics Scholarship . [Webpage] [PDF] [News]
03/06/2024 - The new paper "AI-guided histopathology predicts brain metastasis in lung cancer patients" has been featured on the Caltech homepage. News
03/04/2024 - My new paper on "AI-guided histopathology predicts brain metastasis in lung cancer patients" is online today. Check my publication list here !
02/14/2024 - I passed my candidacy exam!
11/21/2023 - My personal homepage is online!
06/30/2021 - I joined Caltech Biophotonics Lab!


Selected Publications
Full publication list here

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     (BibTex)     [Project Page]     {Data}
Fourier ptychographic microscope images the biological samples with high-resolution and large field-of-view simultaneously. However, this microscope faces challenges with long image stack reconstruction time and huge data volumes. We designed physics-based neural signal representations to tackle these challenges and showed potential in facilitating remote diagnosis, digital pathology, and efficient clinical data packaging.


Teaching
Caltech EE151 - Electromagnetic Engineering (Head TA for Spring 2024)

Foundations of circuit theory-electric fields, magnetic fields, transmission lines, and Maxwell's equations, with engineering applications.

Caltech EE151 - Electromagnetic Engineering (Head TA for Spring 2023)

Foundations of circuit theory-electric fields, magnetic fields, transmission lines, and Maxwell's equations, with engineering applications.