[3] Mackey was a graduate student at the University of California, Berkeley, where he earned a PhD in Computer Science (2012) and an MA in Statistics (2011).

[1][4] At Berkeley, his dissertation, advised by Michael I. Jordan, included work on sparse principal components analysis (PCA) for gene expression modeling, low-rank matrix completion for recommender systems, robust matrix factorization for video surveillance, and concentration inequalities for matrices.

[5] After Berkeley, he joined Stanford University, first as a postdoctoral fellow working with Emmanuel Candès and then as an assistant professor of statistics and, by courtesy, computer science.

He used the PRO-ACT database of clinical trial data and Bayesian inference to predict disease prognosis.

[1] He has also developed machine learning models for subseasonal climate and weather forecasting, to more accurately predict temperature and precipitation 2-6 weeks in advance.