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. efore that, I co-founded Advanza which got acquired in 2016.