Victor Veitch

I am a research scientist working on machine learning and statistical inference.

I'm currently a Distinguished Postdoctoral Researcher in the department of statistics at Columbia University, where I work with the groups of David Blei and Peter Orbanz. I completed my Ph.D. in statistics at the University of Toronto, where I was advised by Daniel Roy. In a previous life, I worked on quantum computing at the University of Waterloo.

My recent interests revolve around adapting machine learning methods for causal inference. Much of my work focuses on learning from network data. Other recent interests include stochastic optimization, privacy, and probabilistic symmetries.

I like collaborations; reach out if you've got a cool problem you'd like to chat about!


  • New preprint on how to design neural networks to get very accurate estimates of causal effects
  • New preprint showing how to do causal inference with text data by exploiting deep natural language models
  • New preprint using network embedding methods to adjust for unobserved confounding in causal inference
  • Our paper generalizing empirical risk minimization to relational (network) data was accepted as an AISTATS oral

Curriculum Vitae