CV

Ph.D. Candidate

University of California, Los Angeles (UCLA)

Electrical and Computer Engineering

• Expected graduation — Dec. 2026. GPA — 3.975/4.0.
• Advisor — Prof. Aydogan Ozcan, Bio- and Nano-photonics Lab.

B.Eng.

Zhejiang University, Chu Kochen Honors College

Optical Science and Engineering

• GPA — 3.95/4.0 (Rank — 3/130).
• Thesis: Super-Resolution Imaging (advisor: Cuifang Kuang).

Research Scientist Intern

Meta Reality Labs

Optical Engineer Intern

Daheng Optics Incorporation

• Developed a diffractive phase microscope leveraging zero-order and first-order interference to extract phase profiles of samples, enabling accurate phase reconstruction of ~10 um plastic micro-beads.
• Built a Time-Domain Optical Coherence Tomography (TD-OCT) system to extract depth information, achieving micron-scale depth detection through ~150-200 um of overlaid tape.

Optical Generative Model

UCLA

Graduate Student Researcher

• Co-designed an end-to-end differentiable framework for a hybrid optical-digital system, distilling a diffusion model (DDPM) into a digital encoder and a holographic optical decoder for high-fidelity image synthesis.
• Engineered and characterized a custom holographic display platform enabling ultra-fast, low-latency optical image generation, achieving FID scores of 131.08 (MNIST) and 18 dB PSNR on painting image synthesis, with < 1 ns optical processing speed.

Diffractive Optics-based Computational Imaging and Display

UCLA

Graduate Student Researcher

• Designed and experimentally validated diffractive optical systems for computational imaging by coupling differentiable light-propagation physics with custom deep learning architectures.
• Trained a digital-twin neural network to predict and correct phase aberrations, reducing simulation-to-experiment mismatch and enhancing holographic image fidelity.
• Developed a hardware-in-the-loop (HIL) training framework using Reinforcement Learning (RL), accelerating convergence by 4x and enhancing system robustness by ~50% against fabrication errors and misalignments.
• Collaborated with Lawrence Livermore National Laboratory to co-design DOEs for imaging through occlusions, advancing robust optical sensing and computational photography in complex environments.

Spectral Kernel Machines for Intelligent Spectral Vision

UCLA

Graduate Student Researcher

• Led ML algorithm development for Spectral Kernel Machines, enabling ~2 us inference latency and two orders of magnitude improvements in speed and energy efficiency over conventional hyperspectral pipelines.
• Applied methods to spectral machine vision, spanning segmentation, materials, and chemical analysis, delivering up to 98.6% accuracy, 100% blind-test classification, and sub-nanometer thickness discrimination.
• Collaborated with UC Berkeley teams to integrate algorithms with real hardware; the resulting system was reported in Science.

Super-Resolution Virtual Staining for Mass Spectrometry

UCLA

Graduate Student Researcher

• Developed a Brownian-bridge diffusion model (BBDM) to perform image-to-image translation, achieving 10x super-resolution for virtual histology on label-free mass spectrometry data.
• Designed an optimized noise-sampling strategy to stabilize the training of the latent diffusion model, reducing output variance by 15% and ensuring consistent, high-fidelity results.
• Outperformed SOTA conditional GAN baselines across key metrics (SSIM, LPIPS, PSNR), establishing a new benchmark for virtual staining performance.

Materials Detection Using Terahertz Spectral Signal and Deep Learning

UCLA

Graduate Student Researcher

• Engineered a transformer-based spectral-spatial model for semantic segmentation of chemical components from raw terahertz spectral data, achieving 90% pixel-level accuracy and enabling robust material recognition.
• Developed a physics-aware post-processing pipeline (morphological filtering + spatial consistency) to suppress noise and boost image-level classification to >95%.
• Worked in a cross-lab collaboration spanning terahertz hardware, spectroscopy, and deep learning to align model outputs with real physical measurements.

Dissertation Year Award Fellowship (Fall 2026)

UCLA

Emil Wolf Outstanding Student Paper Competition

Optica, Frontiers in Optics + Laser Science (FiO LS)

Best Early Career Researcher Presentation (Silver)

SPIE Optics + Photonics

First-Class Scholarship for Outstanding Merits (2018-2020)

Zhejiang University

Awarded to students ranking in the top 3% out of ~6,000 peers across the university.