Jing Yi

Jing Yi is an active biostatistician and vision science researcher, holding a Ph.D. in Statistics from the University of California, Berkeley (2006), where her dissertation on fluorescent DNA quantification was supervised by Terence Speed. Her current work bridges statistical methodology with ophthalmological applications.

Her educational background includes:

• Ph.D. in Statistics, University of California, Berkeley, 2006

Research focuses on color vision mechanisms, adaptive testing paradigms, and machine learning-driven diagnostic tools. She pioneers tablet-based vision assessment methods like AIM (Angular Indication Measurement) and FInD (Foraging Interactive D-prime), emphasizing self-administered, accessible clinical evaluations. Her work addresses fundamental perceptual phenomena (e.g., neon color spreading, chromatic-luminance interactions) while developing practical solutions for color vision deficiency screening and visual acuity measurement.

Analysis of her 15 most recent publications (2022–2025) reveals strong interdisciplinary integration of psychophysics, optics, and AI. Key trends include adaptive algorithms for rapid color vision classification, quantification of refractive errors via novel acuity methods, and validation of digital diagnostic tools against traditional clinical standards. Her research consistently targets real-world applicability in optometry and ophthalmology.

No scientific awards are documented in the provided materials.

While student advising, grant funding, and laboratory affiliations remain unspecified in available sources, her prolific publication record indicates active collaboration with vision science and medical technology teams. Future work appears directed toward refining machine learning classifiers for color vision phenotyping and expanding tablet-based diagnostic accessibility.