Yiling Li

I am a first-year Ph.D. student in Electrical & Computer Engineering at Purdue University and a member of the Intelligent Imaging Lab (i2Lab) advised by Prof. Stanley Chan.

My research focuses on advancing light-driven technologies at the intersection of nanophotonics and computational imaging. I am particularly interested in how nanoscale structures, material systems, and algorithmic models can be co-designed to control light through metasurface engineering and holographic display architectures for next-generation optoelectronic devices and display systems.

I received my B.S.E. in Electrical & Computer Engineering from Princeton University with minors in Statistics & Machine Learning, Robotics, and Visual Arts. My undergraduate senior thesis on differentiable forward models for holographic display systems was advised by Prof. Felix Heide at Princeton Computational Imaging Lab.

Beyond research, I enjoy tinkering with electronics, creating art, and learning dance choreography.

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Research

Differentiable Forward Models for Holographic Display Systems

This study develops camera-in-the-loop (CITL) calibration and learned differentiable forward models to compensate for real-world optical imperfections in holographic display systems. By modeling hardware nonidealities such as aberrations, illumination variation, and detector noise, we improve hologram reconstruction quality without requiring physical hardware during optimization.

Plasmon-enhanced ultralow threshold solid-state triple fusion upconversion

We substantially improve the performance of upconversion films through excitation with surface plasmons that propagate along a planar silver-film interface, leading to an absorption enhancement that reduces the intensity threshold and enhances the external quantum efficiency.

Plasmon enhancement for ultralow threshold triplet fusion upconversion with applications in white organic light emitting diodes

This project addresses solid state disadvantages of triplet fusion upconversion by exciting an upconversion film using the concentrated electric field of planar surface plasmon polaritons, demonstrating reductions in optical power threshold.

Learnable optical model for hologram correction

We investigate camera-in-the-loop (CITL) computer-generated holography (CGH) for 3D nanoscale printing in photosensitive resin by training neural networks to compensate for optical aberrations in a 4f metasurface–spatial light modulator optical system. We further correct for speckle, pixel cross-talk, and zeroth-order light to improve agreement between simulated and experimental holograms.

Ultralow threshold triplet fusion upconversion enabled by surface plasmon field enhancement

While solid-state upconversion systems promise applications in solar cells, photoredox catalysis, and bioimaging; most require tremendous optical power to achieve maximum upconversion efficiency. In this work, we lower this required optical power by using the concentrated electric field of surface plasmons generated from an organic light emitting diode.

Plasmon enhanced light-mediated chemical processes

In this study, we optimize the dielectric and acceptor layers of organic light-emitting diodes (OLEDs) to increase the efficiency of triplet-triplet annihilation photon upconversion (TTA-UC), a process that converts two low-energy photons into one higher-energy photon.

Last updated: July 27, 2026 | Design and source code from Leonid Keselman's fork of Jon Barron's website