Diederik P. Kingma
e-mail: dpkingma [at] gmail [dot] com
Science, and intelligent behaviour in general, involves learning from data: finding predictive models (theories) that
are consistent with observations. Machine learning is the study of algorithms for automating this learning process with
computer systems. Due to the generality of the problem of learning from data, it is difficult to overstate how impactful
machine learning is: its algorithms and tools are used throughout the sciences and industry. And due to the
exponential growth of computational resources, its potential keeps expanding.
I'm a machine learning researcher, since 2018 at Google. My contributions include the Variational Autoencoder (VAE), the Adam
optimizer, Inverse Autoregressive Flow (IAF), and Glow. More generally my main research interests are at the intersection of deep
learning with topics such as generative models, variational (Bayesian) inference, stochastic optimization, and
identifiability. I obtained a PhD (cum laude) from University of Amsterdam in 2017, and was part of the founding team of
OpenAI in 2015. Before that, I co-founded Advanza which got acquired in 2016.
My formal first name is Diederik, but people also call me Durk, which is a Frisian name that is pronounced like the
English name Dirk.
I live in San Francisco.
- 2018 - Present: Sr. Research Scientist at Google Research (San Francisco). I work on generative models, identifiability, among other topics.
- 2015 - 2018: Part of founding team and Research Scientist at OpenAI (San Francisco). Lead of the Algorithms team, focused on basic research.
- 2013 - 2017: Ph.D. (cum laude) at University of Amsterdam, advised by Max Welling, on the topic of deep learning and generative models. Thesis: Variational Inference and Deep Learning: A New Synthesis. Spent summers of 2014/2015 at DeepMind for collaborations.
- 2010 - 2012: Co-founder and technical lead at Advanza, successful exit in 2016.
- 2009 and 2012: Jr. Research Scientist at New York University, Yann LeCun's lab.
See my Google Scholar profile for a complete list.
Variational Inference and Deep Learning: A New Synthesis
Ph.D. Thesis. Download at Dropbox
or at UvA.
Some research demos that I (co-)developed:
These places are updated more frequently than this website:
Awards and Honors
- 2020: The Adam optimization paper is the world's #1 most cited scientific paper of the past five years, according to Nature Index and Google Scholar.
- 2020: The AI 2000 Most Influential Scholar
Award "in recognition of outstanding and vibrant contributions in
the field of Machine Learning 2009-2019".
- 2019: The
Dutch Datascience Award, from the Royal Holland Society of Sciences and
Humanities, for my contributions in machine learning research.
- 2019: The
ELLIS PhD Award
for "outstanding research achievements during the dissertation phase of
outstanding students working in the field of artificial intelligence and
- 2017: PhD with 'cum laude', highest
distinction in the Netherlands, and first time it was awarded at the CS
department in 30 years.
- 2015: Google's first European
Doctoral Fellowship in Deep Learning.
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