Durk Kingma

Co-Founder

Netherlands17 yrs 1 mo experience

AI ML PractitionerAI Enabled

Key Highlights

Pioneered the Variational Auto-Encoder (VAE) concept.
First to receive 'cum laude' in Machine Learning at University of Amsterdam.
Led research on cutting-edge generative models at Google.

Stackforce AI infers this person is a leading expert in AI Research and Machine Learning.

Contact

dpkingma@gmail.com LinkedIn

Skills

Core Skills

Machine LearningArtificial Intelligence

Other Skills

AlgorithmsBayesian InferenceData MiningDeep LearningDiffusion ModelsEntrepreneurshipGenerative ModelingGenerative ModelsLarge Language ModelsNatural Language ProcessingPattern RecognitionPythonResearchSemi-Supervised LearningStatistical Data Analysis

About

I'm a computer scientist and machine learning researcher, mostly working on generative models. My research contributions include the Variational Auto-Encoder (VAE) and the Adam stochastic optimization method. I obtained a PhD from University of Amsterdam.

Experience

Anthropic

ML Research

Oct 2024 – Present · 1 yr 5 mos · Netherlands

Google

2 roles

Research Scientist, Google Brain / DeepMind

Jul 2018 – Oct 2024 · 6 yrs 3 mos · San Francisco, California, United States · On-site

Leading various research projects on generative models, including diffusion models (images) and large language models (text). Started at Google Brain, which merged with DeepMind in 2023.

Generative ModelsDiffusion ModelsLarge Language ModelsMachine LearningArtificial Intelligence

Doctoral Fellow in Deep Learning

Jun 2015 – May 2016 · 11 mos

Received Google's first European Doctoral Fellowship in Deep Learning.
https://research.googleblog.com/2015/06/announcing-2015-google-european.html
Research collaborations with Google Deepmind, London, in summers of 2014 and 2015.

Openai

Research Scientist and Team Lead

Dec 2015 – Jul 2018 · 2 yrs 7 mos · San Francisco

Member of the founding team of OpenAI and lead of the Algorithms team, focused on basic research. Our team developed new algorithms and methods, mostly for generative models.

AlgorithmsGenerative ModelsMachine LearningArtificial Intelligence

Google deepmind

Ph.D. Research

Aug 2014 – Sep 2015 · 1 yr 1 mo · London

Summers of 2014 and 2015: worked at Google Deepmind, collaborating with Danilo J. Rezende, Shakir Mohamed, Karol Gregor and Daan Wierstra on generative modeling and semi-supervised learning using variational autoencoders.

Generative ModelingSemi-Supervised LearningMachine LearningArtificial Intelligence

University of amsterdam

Ph.D. in Machine Learning (Cum Laude)

Jan 2013 – Jan 2017 · 4 yrs · Amsterdam Area, Netherlands

Research in the field of Artificial Intelligence and Machine Learning. Thesis title: "Variational Inference and Deep Learning: A New Synthesis". Ph.D. Commitee: Max Welling (advisor), Yoshua Bengio, David Blei, Aapo Hyvarinen and others. Most of my work was on combining Variational (Bayesian) Inference with Deep Learning, but also spend time on other aspects of machine learning such as stochastic optimization. Some of my high-impact contributions are the variational autoencoder (VAE) and the stochastic optimization method Adam. I was the first to receive 'cum laude' (the highest honor in the Netherlands) in our university department since 1985.

Variational InferenceDeep LearningStochastic OptimizationMachine LearningArtificial Intelligence

Courant institute of mathematical sciences

2 roles

Research Scholar

Jan 2012 – Jan 2013 · 1 yr · Greater New York City Area

Research at Yann LeCun's lab into Machine Learning and stochastic optimization.
Teaching Assistant in Yann LeCun's graduate course in Machine Learning: designing and grading of student assignments and exam, and tutoring students at weekly office hours.