Yu Zhang

I am a PhD researcher at the University of Surrey, specializing in skin product formulation optimization. My research focuses on leveraging automated platforms and intelligent optimization methods to accelerate formulation design. By integrating automated formulation systems, machine learning models, and multi-objective optimization, I aim to improve formulation efficiency, reduce experimental costs, and enhance personalized formulation development.

Research Highlights: · Early Decision-Making Algorithm (EDMA): Optimizes formulation selection based on experimental data to reduce the number of required tests. · IVPT/IVRT for Skin Absorption Testing: Enhances drug permeation prediction and validation. · Automated Experimental Scheduling Optimization: Uses genetic algorithms (GA) to optimize resource allocation in robotic formulation systems. · Machine Learning for Formulation Prediction: Investigating Gaussian Process Regression (GPR) and Neural Networks for data-driven formulation optimization.

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