Scalable Intelligence
We build new AI systems that scale robustly across data, parameters, and compute.
We develop scalable and self-improving AI systems and methodologies towards the goal of AGI, leveraging techniques in machine learning, systems, natural language processing, and beyond.
Prospective Students and Postdocs:
- For students interested in working in the lab, please fill out the following form.
- If you are a prospective PhD student: please indicate your interest in working with Prof. Mirhoseini in the Stanford CS PhD online application form, as well as in your research statement. We admit new PhD students each year. Please do not contact us directly regarding PhD applications until admission decisions are out, as we will not be able to reply to emails from individual applicants.
- If you are a postdoc candidate: we currently have a joint postdoc position available. For information on how to apply, please visit here.
Research Themes
Agentic Optimization and Self-Improvement
We create agentic frameworks that enable machines to learn from their worldly interactions and experiences, enabling advanced reasoning and planning.
Automated Verifiers and Benchmarks
We design methods and frameworks for automated evaluation and benchmarking.
AI for Systems and Systems for AI
We design efficient systems for AI training and serving, with a particular interest in using AI itself as the optimization engine.
