I am a Ph.D. candidate in Electrical Engineering at the University of Notre Dame, advised by Professor Scott Howard, specializing in optics, computational imaging, image restoration, and inverse problems. My research integrates physics-based optical and sensor modeling with deep generative inference to solve inverse problems such as denoising, deblurring, and super-resolution under real-world degradations, including mixed Poisson–Gaussian noise and photon-limited sensing. I develop systems and algorithms for image reconstruction and enhancement, with evaluation guided by perceptual image quality metrics. My work aims to bridge computational imaging, computer vision, and perception to enable photorealistic synthesis and reliable visual understanding under real-world constraints.
In Fall 2025, I was named a Notre Dame SAI (Scientific Artificial Intelligence) Fellow.
I’m on the job market for research scientist roles in computational imaging and generative modeling starting 2027.
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Research Interests
- Computational Imaging
- Image Restoration & Inverse Problems
- Diffusion Models & Generative Inference
- Physics-Based Optical & Sensor Modeling
- Fluorescence Lifetime Imaging Microscopy (FLIM)
Education
- Ph.D. in Electrical Engineering, University of Notre Dame, 2027 (expected)
- M.S. in Electrical Engineering, University of Notre Dame, 2025
- B.E. in Electrical Engineering (with Honors), Wuhan University, 2021
Experience
Graduate Research Assistant, University of Notre Dame
Notre Dame, IN · Sep 2022 – Present
- Developed probabilistic noise models (mixed Poisson–Gaussian, frequency-domain) for multi-channel imaging systems, improving sensor calibration and noise characterization.
- Designed diffusion-based reconstruction methods that combine learned image priors with physics-based forward models for denoising, deblurring, super-resolution, and photon-limited microscopy deconvolution.
- Built simulation and evaluation pipelines, and collaborated with interdisciplinary teams on biomedical imaging in microscopy and FD-NIRS.
- Working on FD-NIRS with the O’Sullivan Research Group as part of the ARPA-H OMEGA project (Optical, Mechanical, and Electrical Global Assessment of Fetal Hypoxia).
DevOps Engineer Intern, AI Technologies, SAP
Shanghai, China · Feb 2021 – Jun 2021
- Built ML deployment infrastructure on Azure Kubernetes with observability pipelines (Prometheus, Grafana, Jaeger) for performance monitoring.