Kenji Kawaguchi
Kenji Kawaguchi is an AI researcher whose work studies deep learning theory and optimization. He earned an S.M. in electrical engineering and computer science at MIT in 2016, completed a computer science Ph.D. in 2020, and served as a Harvard postdoctoral fellow. Since 2021, he has been a Presidential Young Professor in the NUS Department of Computer Science. In 2024, he and his coauthors received the NeurIPS Best Paper Award for the Stochastic Taylor Derivative Estimator.
- Works
- 412
- Citations
- 8,679
- h-index
- 41
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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2016
An MIT master's degree
He earns an S.M. in electrical engineering and computer science at MIT in 2016.
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2016
Deep learning without poor local minima
In 2016, he proves that every local minimum of a deep nonlinear network's expected loss is global under an independence assumption.
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2020
A doctorate and a Harvard postdoc
He completes a Ph.D. in computer science at MIT in 2020. He then joins Harvard University as a postdoctoral fellow.
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2020
Ordered SGD
In 2020, he and Haihao Lu introduce Ordered SGD, which biases its gradient estimator toward observations with higher current losses.
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Since 2021
A professorship at NUS
In 2021, he joins the NUS Department of Computer Science as a Presidential Young Professor.
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2024
A NeurIPS Best Paper Award
In 2024, he and his coauthors win the NeurIPS Best Paper Award for the Stochastic Taylor Derivative Estimator.
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As of October 2026
Professor at NUS
As of October 2026, he is a Presidential Young Professor in the Department of Computer Science at NUS.
Sources and credits
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- Kenji Kawaguchi
- Deep Learning without Poor Local Minima
- Kenji Kawaguchi - Harvard University
- Kenji KAWAGUCHI - NUS Computing
- Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
- PhD program and postdoc position in CS at NUS
- NUS Presidential Young Professor Kenji Kawaguchi wins Best Paper Award at NeurIPS 2024
- Information for incoming NUS CS PhD students