Yisha Xiang

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.