Yisha Xiang
دانشیار · Data-driven decision making under uncertainty
University of Houstonمعرفی
Dr. Yisha Xiang is an Associate Professor in the Department of Industrial Engineering at the University of Houston, part of the Cullen College of Engineering. She holds a PhD from the University of Arkansas and has extensive professional experience in academia and industry, including roles at Texas Tech University, Lamar University, and Sun Yat-Sen University. Her research focuses on data-driven decision-making under uncertainty, statistical machine learning, and applications in manufacturing, healthcare, energy, and infrastructure systems. She leads a lab exploring topics like Bayesian learning for predictive healthcare and reinforcement learning for manufacturing optimization.
Education:
- PhD, Industrial Engineering, University of Arkansas, 2009
- MS, Industrial Engineering, University of Arkansas, 2006
- BS, Industrial Engineering, Nanjing University of Aero. & Astro., China, 2003
Research Interests: Data-driven decision-making under uncertainty, statistical machine learning, maintenance optimization, reliability engineering, and applications in manufacturing, healthcare, and energy systems. Recent work includes Bayesian models for EHR-based clinical prediction and deep reinforcement learning for resource allocation.
Awards:
- NSF CAREER Award (2020)
- Whitacre Engineering Research Award (2021)
- Multiple Best Paper Awards from IISE and Society of Reliability Engineering
Grants: Over $700,000 in funding, including NSF CAREER, DOE Clean Energy, and industry partnerships like Covestro. Projects focus on remanufacturing sustainability, maintenance planning, and reliability analysis.
Advising: Supervised 1 completed PhD and 6 ongoing PhD students, with a focus on nurturing leaders in operations research and engineering. Active in mentoring through REU programs and graduate committees.
Labs & Collaborations: Directs a research lab at the University of Houston, collaborating with industry (e.g., Covestro) and academic partners on projects like NASA turbofan engine remanufacturing and ECG-based cardiac arrhythmia prediction.
