I study how optical measurements shape what machines can infer, and how to make that inference reliable with limited, noisy data. I connect measurement design, reconstruction and direct inference to understand what makes perception robust.
- Now: Time-domain diffuse optical tomography for 3D brain imaging; acquisition-aware single-pixel sensing on an optical bench; exploratory question answering from measurements with a frozen vision–language model.
- Long term: help robots interpret the physical world from raw measurements and physical models.
- Open to: research collaborations and internships in computational imaging, image-free sensing and machine perception.
 STSF-TPLS |
 remote-gpu-trainer |
 SHSI |
 LDGI |
Animations are conceptual illustrations. View still images.
Papers and more code
- The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing, arXiv 2026 · paper · code
- Generalized sparse Hadamard single-pixel Imaging, Optics Express 2025 · paper · code
- Loop differential ghost imaging based on line pattern, Optics Communications 2025 · paper · code
- All nine papers, with BibTeX: hanyuyuan6.github.io/#publications
- Comparison-of-SPI-DLs: eight deep networks and correlation ghost imaging, benchmarked on one shared Hadamard acquisition
- TGIs-Entry: reference implementations of traditional, differential and normalized ghost imaging