Lab of Data & AI-Intensive Systems

principled, grounded in theory and quantifying how far we can improve or go wrong; adaptive, learning from execution rather than predetermining everything; and declarative, so users say what they want, not how to get it. As a long-term goal, we aim to build a reliable AI-driven research assistant for reducing obvious/manual tasks so we can focus on genuinely interesting problems. Our work can be viewed by domain, by system layer, by methodology, or by the dimensions of heterogeneity we tackle. Click any item to see what it means and the papers behind it. Core database technology (mainly OLAP): adaptive/declarative query processing, hardware-aware query processing, physical operator design, and query optimization. AI inside the database, learned components that replace heuristics and manual labor inside data systems: query optimization and NL-to-SQL translation. Data systems for AI, the infrastructure AI workloads run on: LLM inference serving, vector query processing, semantic operators and engines over unstructured data, and datalakes. We look for students who are curious about how data systems actually work — and restless about how they should work in the AI era. Graduates, and undergraduate interns who want 3-6 months of research experience are also welcome. A strong background in any one of systems, algorithms, theory, mathematics, or machine learning is a great starting point. Beyond background, we look for students who enjoy the process of solving hard problems with the grit to stay on them, speak up with their own ideas while staying open to different ones, enjoy growing and keep asking for one more step, and just start without worrying too much. As an early member of a new lab you will work directly with the professor, own your research agenda, and aim for top d