The need for automated decision-making is steadily increasing. The goal is to derive and to implement methods for automated decision support for practical applications in changing uncertain environments. Solving problems in practical applications requires bringing together data management, data science, optimization, and risk management. Decision problems can generally be described by given performance criteria, admissible decisions, constraints, and (data-driven) estimations of the interplay of decisions on performance. However, every application has its own specifics, which should be taken into account to solve a problem effectively.
My research focuses on data-driven decision-making using quantitative methods of operations research and data science applied in the areas of (i) Operations Management and (ii) Database Optimization. I am particularly interested in finding robust solutions for stochastic dynamic environments.
Keywords: (i) Optimal Control of Dynamic Systems, Risk-Sensitive Decision-Making, Dynamic Pricing Competition, Inventory Management, Data-Driven Demand Learning
(ii) Optimal Resource Allocation, Robust Database Configurations, Workload-Driven Decision-Making, Data Placement, Index Selection, Workload Anticipation