Rate My Professor Zhifeng Bao

Bao's research focuses on data-centric AI, multimodal data lake management, and machine learning for database systems, encompassing areas such as trajectory data queries, spatial-temporal forecasting, query optimization, and differentially private data processing. He has published extensively in premier venues including Proceedings of the ACM on Management of Data, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Intelligent Systems and Technology. Key publications include "Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking" (2026), "Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries" (2023), "Efficient and Effective Algorithms for Generalized Densest Subgraph Discovery" (2023), "A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration" (2021), and "Efficient mining of outlying sequence patterns for analyzing outlierness of sequence data" (2020). His scholarship has accumulated over 6,000 citations. Notable honors include the Chris Wallace Award for Outstanding Research (2021) from the Computing Research and Education Association of Australasia, Google Faculty Research Awards (2015 and 2018), Best Paper Runner-up at ACM KDD 2019, and multiple distinguished service awards such as Senior PC Award at EDBT 2024 and Associate Editor Awards for ACM SIGMOD and PVLDB.