Research
We build AI systems that treat data as a first-class citizen — learning to optimize databases and infrastructure, making data accessible through natural language, and understanding the networks that data lives in.
Text-to-SQL and Data Interfaces
Turning questions in plain language into correct queries over real schemas, and knowing when the answer cannot be trusted.
Machine Learning for Systems
Learned components inside data systems — query optimization, indexing, caching and configuration tuning.
Systems for Machine Learning
The infrastructure machine learning runs on — data pipelines, training and serving efficiency, and the operational side of models in production.
AI Agent Systems
Language-model agents that plan, call tools and act over real data, and the evaluation needed to trust them.
Complex and Social Networks
Inferring structure from information diffusion, modelling cascades, and predicting what spreads.
5 publications
Trustworthy AI and Misinformation
Detecting manipulated content, labelling news early, and making predictions that report their own uncertainty.
4 publications