We are the Guestrin Lab at Stanford University, Department of Computer science. We focus on practical and impactful research in machine learning and artificial intelligence, creating tools and systems that solve real-world problems and make AI more trustworthy.
A significant focus of our research has been building and releasing ML systems that work in the real-world, with the aim of gaining massive adoption, impacting industry and fundamentally influencing the design and architecture of such systems. Here are some key projects we have co-created:
- XGBoost: scalable, portable and distributed gradient boosting library.
- LIME: explaining the predictions of any machine learning classifier.
- TextGrad: self-optimization of prompts and outputs of LLM programs.
- AlpacaFarm: small and cheap (<600$) instruction-following large-language model.
- Apache TVM: end-to-end deep learning compiler stack for CPUs, GPUs and specialized accelerators.
See here for a longer list of projects.
Browse our repositories, join issues, discussions and contribute!!
Thank you for your interest in our work!