Zhengle Wang

I am Zhengle Wang, a Ph.D. student in Computer Science at Purdue University advised by Prof. Chunwei Liu.

My research interests are in database systems and cloud data analytics systems, with a particular focus on workload generation, query optimization, and performance evaluation for modern cloud-native OLAP databases.

Before joining Purdue, I collaborated with researchers and engineers from Renmin University of China, Massachusetts Institute of Technology, and Amazon Web Services on projects related to cloud database benchmarking and system optimization. I also worked on large-scale recommendation and search systems through research and engineering internships in industry.

My current research focuses on realistic execution-aware workload synthesis for cloud OLAP systems, aiming to bridge the gap between academic evaluation and real-world production workloads. More broadly, I am interested in learned system optimization, AI for systems, and agentic infrastructure for data-intensive applications.

Research Themes:

• Cloud Data Systems & AI-era Benchmarking: Realistic workload synthesis, execution-aware query generation, query optimization, and performance evaluation for cloud OLAP systems and AI-driven data workloads.
• Agentic Systems Infrastructure: Infrastructure and evaluation frameworks for agentic systems, including scalable data collection, trajectory management, execution environments, and reproducible evaluation.

Industry Experience

Machine Learning Intern, Meituan, Hotel & Travel Recommendation Team, Beijing, China
Oct 2025 – Dec 2025

Worked on cross-domain and multi-scenario recommendation modeling across hotel, homestay, and ticket-booking services. Explored shared representation learning and scenario-specific adaptation strategies for integrating heterogeneous user behaviors and item features across different travel-related scenarios.

Machine Learning Intern, Poizon Inc., Community Search Team, Beijing, China
Jun 2024 – Mar 2025

Worked on machine learning methods for large-scale content search and recommendation, including relevance-aware ranking, personalized pre-ranking, and model distillation for efficient retrieval/ranking pipelines. This experience exposed me to production-scale ML systems and motivated my broader interest in building reliable, data-driven infrastructure for real-world workloads.