James McInerney
James McInerney
Researcher in Machine Learning

About Me

Senior Research Scientist at Netflix, California

My research is on machine learning and probabilistic models


  • Senior Research Scientist, Spotify, New York

  • Assistant Adjunct Professor, Columbia University

  • Postdoctoral researcher, Columbia University and Princeton University

  • PhD in machine learning for spatiotemporal modeling

  • MSc, Imperial College London

  • BA, Oxford University


Research Interests

My research interests are in latent variable models, variational inference, and causality. I have developed a number of Bayesian machine learning techniques presented at the top machine learning conferences to discover hidden structure in text, recommendation, & mobility data. Below is a summary of projects I have lead.


causal recommendation

In recent years, the causal challenges present in recommender systems have been increasingly recognized. My work looks at how to train models with data that are confounded by the recommender and other biases.


scalable Variational inference

Variational inference and variational autoencoders are promising methods for performing approximate Bayesian inference on large data sets. My research is about how to perform inference on streaming data and how to deal with the non-convexity of the variational objective.


spatio-temporal probabilistic modeling

Spatio-temporal data require new models and decision-making techniques to deal with non-exchangeability. My research proposes methods in the areas of anomaly detection, reinforcement learning, and overcoming data sparsity in temporal patterns.



with my wife in Arizona

with my wife in Arizona


It all started when…

My background is in maths and computer science. I become interested in artificial intelligence between my undergraduate and masters, particularly to the possibilities of having machine learning agents take the cognitive load of processing increasing amounts of data.

Studying artificial intelligence at Imperial College London opened my eyes to neural networks and Bayesian inference. I followed this passion with a PhD in machine learning for spatio-temporal data and studied probabilistic models of time series, discrete data, and variational inference. This took me to David Blei's lab at Princeton who introduced me to causal analysis and latent variable recommendation models.

I now combine all these elements in my job as a research scientist at Netflix where I continue to publish and work with other scientists to develop new machine learning methods.


I maintain two blogs:

  1. D-Speculation blog about ML and probabilistic models

  2. Personal blog of random thoughts about life, music, and science.