Nando De Feitas
Papers
1
Total Citations
372
H-Index
1
About
Nando de Freitas is a leading figure in machine learning, renowned for his pioneering work in Bayesian optimization, deep learning, and reinforcement learning. His research has fundamentally advanced the ability to solve high-dimensional problems, most notably through his landmark paper "Bayesian Optimization in a Billion Dimensions via Random Embeddings" (2016, 372 citations), which introduced a technique to scale Bayesian optimization to previously intractable dimensions, with profound implications for robotics, automatic algorithm configuration, and intelligent user interfaces. De Freitas has also made seminal contributions to deep reinforcement learning, including work on attention mechanisms and memory-augmented neural networks, and his research on curriculum learning and generative models has shaped modern AI. With over 30,000 citations, his impact is immense; he has received multiple best paper awards and served as a key figure at Google DeepMind, where he led foundational advances in AI. A former professor at the University of Oxford and the University of British Columbia, de Freitas continues to inspire through his work on scalable, data-efficient learning algorithms.
Research Focus
Key Achievements
Top Papers
- 1Bayesian Optimization in a Billion Dimensions via Random Embeddings372 citations · 2016