Adaptive AI for Decision Making
Models and algorithms that update decisions as new information arrives.
AI · Business Analytics · Decision Science
Decision Support Under Uncertainty for Business and Service Systems
I am an Assistant Professor at the Institute of Service Science, College of Technology Management, National Tsing Hua University. My work studies how AI, statistical learning, simulation, and uncertainty-aware models can support better decisions in complex, data-rich environments.
Research identity
From Statistical Rigor to Managerial Decisions
Trained as a statistician, I develop adaptive and uncertainty-aware approaches that help decision makers learn from data, evaluate risk, and act when information is incomplete, costly, or evolving. My research builds on Bayesian optimization, adaptive experimental design, machine learning, simulation, and predictive modeling, with growing applications in business analytics and decision support systems.
Research themes
AI and Analytics for Decisions That Unfold Over Time
Uncertainty-Aware Decision Support
Predictive tools that communicate risk, confidence, and trade-offs to decision makers.
Business Analytics Using Simulation
Simulation-based evaluation of marketing, supply chain, and service system decisions.
Experimentation and Resource Allocation
Data collection and experimental design strategies for learning efficiently under constraints.
Teaching
Business Analytics Using Simulation
Upcoming course at NTHU on simulation-based decision making in marketing and supply chain contexts, with cases involving campaign allocation, customer retention, A/B testing, demand forecasting, inventory dynamics, resilience, and capacity allocation.
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Adaptive Design, Uncertainty, and Dynamic Data
My published and ongoing work develops methods for efficient data collection, optimal design, predictive modeling, and adaptive decision-making under uncertainty.
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