WeiHsiang Lin

Reinforcement learning in healthy aging and schizophrenia patients • Designed and led experiments to explore reinforcement learning in aging individuals and schizophrenia patients, uncovering significant behavioral insights in the clinical sector. • Applied Q-learning algorithms to analyze data, identifying unique behavioral properties across different subject populations, for enhancing the quality of behavioral assessments. • Fostered international collaboration, ensuring project success through effective

• Conducted human fMRI studies to investigate neural mechanisms behind decision-making, including comprehensive troubleshooting to ensure data integrity. • Processed complex fMRI datasets with High-Performance Computing (HPC) cluster, enhancing analytical efficiency. • Achieved an accuracy of over 85% in predicting behavioral patterns from neural data by using machine learning (SVR) and statistical modeling (GLM).

• Designed custom experimental compartments using AutoDesk and collaborating with local manufacturers achieving a 50% cost reduction over international imports. • Optimized hardware and camera settings to improve the quality of animal behavior and cellular images. • Developed a precise algorithm for tracking animal movements and analyzing cell images, boosting analytical accuracy.

Directed and supervised a team of five in weapons management and maintenance for a military camp of over 100 soldiers.