Yutian Chen
Also known as 陈御天
Yutian Chen is a senior staff research scientist at Google DeepMind. He trained as an electronic engineer at Tsinghua University and earned a machine learning doctorate at the University of California, Irvine, under Max Welling. At DeepMind he contributed to AlphaGo Zero, a Go program that learns from self-play. His research spans Bayesian methods, offline reinforcement learning, generative models and meta-learning. He also co-wrote a 2017 paper on learning to learn, published at the International Conference on Machine Learning.
- Now at
- Google DeepMind
- Works
- 83
- Citations
- 10,333
- h-index
- 18
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
-
2007
Electronic engineering at Tsinghua
He earns a B.E. in electronic engineering from Tsinghua University in 2007.
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2009–2013
A machine learning doctorate in Irvine
He earns an M.S. in 2009 and a machine learning Ph.D. in 2013 at the University of California, Irvine, under Max Welling.
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2013–2015
Scalable Bayesian inference in Cambridge
After his 2013 doctorate, he works as a research associate in the Machine Learning Group at the University of Cambridge with Zoubin Ghahramani on scalable Bayesian inference.
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September 2015
Joining DeepMind as a research scientist
He joins DeepMind as a research scientist in September 2015.
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2017
A learning-to-learn paper at ICML
He co-writes the paper 'Learning to Learn without Gradient Descent by Gradient Descent', published in the proceedings of the 34th International Conference on Machine Learning in 2017.
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October 2017
Work on AlphaGo Zero
In October 2017 DeepMind announces AlphaGo Zero, a Go program that learns from self-play, and he is among its credited authors.
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By 2023
Bayesian methods and offline RL
By 2023 his research spans Bayesian methods, offline reinforcement learning, generative models and meta-learning, with applications in gaming AI, computer vision and text-to-speech.
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As of October 2026
Senior staff research scientist
He is a senior staff research scientist at Google DeepMind.
Sources and credits
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