معرفی
Xin Zan is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Iowa's College of Engineering, where he joined in Fall 2024. His research develops advanced data-driven analytics for complex systems in healthcare, manufacturing, and service sectors through novel weakly supervised learning methodologies.
His educational background includes:
- PhD in Industrial and Systems Engineering from the University of Florida (2024)
- MS in Statistics from the University of Florida (2022)
- BS in Industrial Engineering from Shanghai Jiao Tong University, China (2019)
Dr. Zan specializes in statistical and machine learning under weak supervision, knowledge-enhanced deep learning, spatio-temporal modeling, causal inference, and health informatics. His work bridges theoretical advances with practical healthcare applications including sleep disorder diagnostics, infectious disease surveillance, and medical system optimization. He actively develops frameworks for complex system monitoring where labeled data is scarce or imperfect.
His publication portfolio reveals a concentrated focus on healthcare AI applications (60% of recent work), particularly sleep apnea monitoring and infectious disease tracking, while extending methodologies to recommender systems and big data streams. Core technical themes include Bayesian modeling, nonparametric statistics, and causal analysis across temporal and spatial dimensions.
Dr. Zan is recruiting PhD students with engineering, statistics, or mathematics backgrounds and strong programming skills in Python, R, or Matlab. Prospective students should submit CVs, transcripts, and research statements to xin-zan@uiowa.edu for positions in applied statistics, data science, and AI-driven systems engineering.
