The need for automated decision-making is steadily increasing. The goal is to derive methods and models for automated decision support for practical applications in uncertain and changing environments. Solving problems in practical applications requires bringing together data management, analytics, optimization, and risk management.
Our research group Data-Driven Decision Support focuses on data-driven decision-making using quantitative methods of operations research and data science applied in the areas of (i) Revenue Management and (ii) Database Optimization. Our research has been published in renowned OR Journals (EJOR, JEDC, COR, IJPE, IJPR, DGAA, JRPM), distinguished data science conferences (KDD, IJCAI, RECSYS, SDM), and leading computer science venues (VLDB, ICDE, EDBT, DAPD, CIKM, SSDBM).
Keywords: (i) Optimal Control of Dynamic Systems, Risk-Sensitive Decision-Making, Dynamic Pricing Competition, Inventory Management, Data-Driven Demand Learning, Causal Inference
(ii) Optimal Resource Allocation, Robust Database Configurations, Workload-Driven Decision-Making, Data Placement, Index Selection, Workload Prediction/Anticipation