Robust priority pricing
Modeling customer joining behavior under ambiguous valuation-delay sensitivity and designing pricing policies that remain stable and interpretable under distributional uncertainty.
PhD Candidate in Operations Research · Technical University of Munich
AI, optimization and analytics for data-driven decision-making.
I develop robust and flexible analytical frameworks that combine operations research, machine learning and uncertainty-aware decision models, with applications in healthcare, service systems, pricing and industrial analytics.
About
Bridging theory, data and operational impact.
I am a PhD candidate in Operations Research at the Technical University of Munich, supervised by Prof. Dr. Jingui Xie. My dissertation develops robust and flexible frameworks for healthcare management and broader service-system decision-making.
My work combines predictive and prescriptive analytics, robust satisficing, queueing networks, stochastic programming and machine learning. I have also worked on industrial data mining and computer vision for lithium-ion battery production at BMW Group.
Research
Selected research themes
Healthcare scheduling
Data-driven robust scheduling of elective patients, integrating uncertainty-aware models with operational constraints in healthcare service systems.
Service-system flexibility
Studying how server flexibility and pooling configurations affect stochastic service-system performance, scalability and operational trade-offs.
Projects
Applied work
BMW Group · Battery production analytics
Built data-driven KPI and analytics tools for lithium-ion battery production, supporting operational decision-making with statistical learning and Bayesian quality estimation.
Computer vision for product-parameter identification
Contributed to a computer vision tool for identifying important product parameters in battery production, combining discretization techniques, data augmentation and probabilistic modeling.
TUM Data Innovation Lab & Lidl Analytics
Supervised practical machine learning solutions for uncertainty quantification in regression.
Bosch Home Comfort Group
Supervised data-driven price optimization for sales departments, linking analytics to practical commercial decision-making.
Publications
Publications and working papers
Impact of Server Flexibility on Pooling Configuration in Stochastic Service Systems.
Y. Chen, J. Xie, N. Yang, G. Zhang, T. Zhu. Production and Operations Management, forthcoming, 2026.
A KPI System for Small Sample Sizes Based on the Bayesian Estimation of Cpk in the Production of Lithium-Ion Batteries.
N. Yang, T. Kornas, R. Daub. Procedia CIRP, 2021.
Robust Priority Pricing with Ambiguous Valuation–Delay Sensitivity Heterogeneity.
Working paper.
Data-Driven Robust Scheduling of Elective Patients.
Working paper; presented at INFORMS Healthcare Conference, Toronto, Canada, July 2023.
Skills
Technical toolkit
Programming
Python, R, SAS, MATLAB, C, SQL, Mathematica
Optimization
Robust optimization, queueing models, stochastic programming
AI & Analytics
Statistical learning, uncertainty quantification, generative AI
Communication
Academic supervision, project management, data storytelling
Let’s connect.
I am interested in applied AI, data science, optimization and analytics roles where rigorous modeling can create measurable operational impact.