Victor Veitch is a research scientist and assistant professor whose 11-year career sits at the intersection of machine learning and statistics, with appointments at Google Cambridge and the University of Chicago. He specializes in causal inference and the development of safe, credible machine learning methods, translating deep theoretical training in mathematics and applied math into practical research impact. His path from theoretical and mathematical physics through a PhD in mathematics and statistics informs a rigorous, formal approach to problems in ML and causal analysis. Victor has published and led research at major institutions including Columbia and Microsoft Research, and brings experience working with network-valued data and large-scale machine learning systems. Based in New York, he combines academic leadership with industry research to push methods that improve the reliability and interpretability of ML-driven decisions.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Mathematics and Statistics, Doctor of Philosophy (Ph.D.), Mathematics and Statistics at University of Toronto
Bachelor’s Degree, Theoretical and Mathematical Physics, Bachelor’s Degree, Theoretical and Mathematical Physics at University of Waterloo
Contributions:120 commits, 11 PRs, 80 pushes in 9 months
causaltensorflow-2tensorflowbert
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