Summary
Chenmu Wang is a data analyst with eight years of experience applying industrial engineering, operations research, and statistics to improve learning systems and supply-chain operations. At Penn State she developed real-time Matlab interfaces and EEG-driven regression models that raised individualized learning performance from 79 to 95, demonstrating a rare blend of experimental design and signal-processing skills. Her background includes hands-on supply-chain optimization—demand forecasting, MRP solutions, and labor-balancing models—that delivered measurable cost and efficiency gains across manufacturing and logistics settings. Trained with a Master’s in Industrial Engineering and a Ph.D. background in bioinformatics/genomics on GitHub, she brings both quantitative rigor and domain breadth to cross-disciplinary problems. Colleagues value her ability to turn fractional factorial experiment insights into actionable system changes that boost engagement and reduce defects. Based in State College, PA, she is motivated by using optimization and data-driven design to make adaptive systems more effective.
8 years of coding experience
Master's degree, Industrial Engineering, 3.78, Master's degree, Industrial Engineering, 3.78 at Penn State University
Bachelor of Engineering (B.Eng.), Automation Engineer Technology/Technician, 3.5, Bachelor of Engineering (B.Eng.), Automation Engineer Technology/Technician, 3.5 at Beijing Institute of Technology
English, Chinese