Nando de Freitas
I want to understand intelligence and how minds work. My tools are computer science, statistics, mathematics, and plenty of thinking.
My research is multi-disciplinary:
Machine learning and statistics
AI, reasoning and decision making
Vision, robotics and speech
I am a machine learning professor at Oxford University, a lead research scientist at Google DeepMind, a Fellow of the Canadian Institute For Advanced Research (CIFAR) in the successful Neural Computation and Adaptive Perception program, and a Faculty Fellow of the Alan Turing Institute. I received my PhD from Trinity College, Cambridge University in 2000 on Bayesian methods for neural networks. From 1999 to 2001, I was a postdoctoral fellow at UC Berkeley in the artificial intelligence group of Stuart Russell. I was a professor at the University of British Columbia from 2001 to 2014. I have spun off a few companies, most recently Dark Blue Labs acquired by Google. Among my recent awards are a Google Faculty Research Award, a Distinguished Paper Award at the 2013 International Joint Conference on Artificial intelligence, the 2012 Charles A. McDowell Award for Excellence in Research, and the 2010 Mathematics of Information Technology and Complex Systems (MITACS) Young Researcher Award.
- Action editor for the Journal of Machine Learning Research
- Online courses with videos: Machine Learning and Deep Learning, Monte Carlo
- Google Scholar profile
- Follow me on Twitter
PhD/DPhil applicants: Please follow the instructions on our admissions website.
Bayesian Optimization in High Dimensions via Random Embeddings
Ziyu Wang‚ Masrour Zoghi‚ Frank Hutter‚ David Matheson and Nando de Freitas
In International Joint Conferences on Artificial Intelligence (IJCAI) − Distinguished Paper Award. 2013.
Scott Reed and Nando de Freitas
In International Conference on Learning Representations (ICLR). 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q−Networks
Jakob N. Foerster‚ Yannis M. Assael‚ Nando de Freitas and Shimon Whiteson
No. arXiv:1602.02672. 2016.
- N-Body Methods
- Deep Learning
- Monte Carlo
- Reinforcement Learning
- Computer Vision
- Probabilistic Graphical Models
- Bayesian Optimization