From no coding experience to IEEE published with AI-optimized skin grafts
Working with YRI mentor Dr. Swetha MP, Ryan built an AI framework integrating Multivariate Normal Distribution and polynomial regression with a 3D Generative Adversarial Network to optimize scaffold designs for bioprinted skin grafts. The model trained on synthetic datasets derived from the CO2Wounds-V2 chronic wounds dataset from leprosy patients. It reached an R squared of 0.59 and identified optimized parameters for porosity, pore size, thickness and biomaterial selection.
AI & Biomedical Engineering
Accepted at IEEE ICIEA26
IEEE ICIEA26, 2026
Ryan Vo, YRI Fellow
A 10th grader at St. John's School passionate about healthcare accessibility, with no prior research or programming experience.
Published at an IEEE conference with AI-driven biomedical research after learning Python and building a complete ML pipeline from scratch.
Accepted for presentation and publication at IEEE ICIEA26, paper ID ICIEA26-000288
Optimized 9 scaffold parameters using a 3D GAN architecture
Achieved an R squared score of 0.59 for healing efficiency prediction
Learned Python and built the full ML pipeline from zero experience
Sai Tarekar, Accepted at IEEE ICSCSS 2026