Date of Award

6-10-2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Departments

Bioengineering; Computer Science and Engineering; Computer Engineering

First Advisor

Jonathan Zhang

Second Advisor

Michael Schimpf

Abstract

Age Macular Degeneration (AMD) is currently the leading cause of blindness, driven by the degradation of Retinal Pigment Epithelial (RPE) cells. Stem-cell based therapies, particularly those involving induced pluripotent stem cells (iPSCs), typically consists of differentiating stem cells into RPE cells in vitro and transplanting them into the subretinal space. Our goal is to improve the accuracy of differentiation into RPE cells to better promote retinal regeneration. Traditional methods to alleviate symptoms of AMD is through therapeutic medications to slow disease progression, though stem cell therapy has emerged as a promising alternative. However, despite its potential, stem cell therapy presents several challenges, including a higher risk of tumorigenicity, a time intensive process, and limited scalability. Artificial Intelligence (AI) has the ability to act as a “first lab” without incurring cost, materials or time and provide a novel method to assist human directed iPSC differentiation. To evaluate this approach, we fine-tuned existing LLMs including DeepSeek, Grok, and Nvidia before developing our own distillation model. These AI- generated protocols were treated as preliminary and were further validated through wet lab experimentation, with comparisons to an established protocol from Alstem Inc. By integrating AI into the designing process, we aim to enhance accuracy, feasibility, and reproducibility, which holds potential applications in commercial viable regenerative therapies.

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