Brendan O'Donoghue

I lead a research team at Google DeepMind in London, where I've worked since 2015. My research sits at the intersection of generative AI, reinforcement learning, and optimization. Lately I've been focused on diffusion models for language: I lead the Google DeepMind text diffusion research team, whose work has resulted in releases including DiffusionGemma—Google's open-weights diffusion language model, which generates text up to 4× faster than comparable autoregressive models, announced by Sundar Pichai and Demis Hassabis—and its experimental precursor, Gemini Diffusion. Previously I worked on reinforcement learning, especially Bayesian approaches to the exploration/exploitation trade-off, which resulted in algorithms like K-learning and the Uncertainty Bellman Equation.

Outside DeepMind I'm probably best known for my work in optimization, particularly SCS, the Splitting Conic Solver—an open-source solver for large-scale convex optimization that I wrote and still maintain, which gets downloaded a few million times a month.

Before DeepMind I led a machine learning team at Quantcast in San Francisco and London, working on large-scale ad targeting and real-time bidding. I did my Ph.D. at Stanford University with Stephen Boyd, on designing control policies via convex optimization. Before that, I read information and computer engineering at Cambridge.