معرفی
Hui Yang is a Professor of Industrial and Manufacturing Engineering and Biomedical Engineering at Pennsylvania State University, holding the Gary and Sheila Bello Chair Professor title. He is affiliated with multiple institutions including the Penn State Cancer Institute, Clinical and Translational Science Institute, and Institute for Computational and Data Sciences. Currently serving as PI and Site Director of the NSF Center for Health Organization Transformation (CHOT), his career includes leadership roles in professional societies such as IISE Data Analytics and Information Systems Society (President 2017-2018) and INFORMS Quality, Statistics and Reliability (QSR) society (President 2015-2016).
As Associate Editor for journals like IISE Transactions, IEEE JBHI, and IEEE Transactions on Automation Science, he maintains strong editorial influence. His research integrates nonlinear stochastic dynamics with sensor-based system informatics to advance both smart manufacturing and healthcare engineering. Recent work explores digital twin technologies, blockchain applications, and AI-driven disease modeling for conditions like Alzheimer's and cardiovascular disease.
Key scientific contributions include developing character-level linguistic biomarkers for early dementia detection, self-organizing network representations of cardiac systems, and privacy-preserving neural networks for Industry 4.0 environments. His research group has received significant external funding from NSF, DOE, and NIST to address challenges in heterogeneous manufacturing networks, adaptive failure prognosis, and spatiotemporal optimization.
- Fulbright Award in Science, Technology and Innovation (2022)
- IISE Fellow (2021)
- NSF CAREER Award (2015)
Through his Virtual Learning Factory and SCOUT spatiotemporal framework, Yang bridges manufacturing analytics with health informatics, creating cross-domain methodologies for system diagnostics/prognostics, process optimization, and smart health monitoring. His Cross Recurrence Analysis Toolbox provides open-source methods for nonlinear time series analysis.
