Excited to share I’m joining @Stanford CS as an Assistant Professor! My lab will focus on AI-driven optimization of data systems. I’m recruiting PhD students for Fall 2027 (list me in your app if interested).
Before starting, I’ll spend the year @OpenAI as a visiting researcher.
In our recent ADRS blog post, we feature LEVI, a low-cost algorithmic discovery framework that makes ADRS cheaper and stronger by pairing lightweight models with frontier models.
🎯 LEVI makes AI-driven algorithm discovery cheaper
[ADRS Blog #19] We feature LEVI, a framework for LLM-based optimization that outperforms prior ADRS frameworks while being ~3–7× cheaper on the main benchmark comparison. Instead of using the biggest models for every step, LEVI
Really excited to share our work on evolving the evolution process itself! By continuously adapting the evolution strategy during the run, EvoX discovers stronger solutions across diverse optimization tasks across ~200 different problems.
Researchers spend hours and hours hand-crafting the strategies behind LLM-driven optimization systems like AlphaEvolve: deciding which ideas to reuse, when to explore vs exploit, and what mutations to try.
🤖But what if AI could evolve its own evolution process?
We introduce
AlphaEvolve is closed-source. We release 🌟SkyDiscover🌟, a flexible, modular open-source framework with two new adaptive algorithms that match or exceed AlphaEvolve on many benchmarks and outperform OpenEvolve, GEPA, and ShinkaEvolve across 200+ optimization tasks.
Our new
