| Lester Wayne Mackey II | |
| Birth Place: | Long Island, NY, USA |
| Fields: | Machine learning Computer science Statistics |
| Workplaces: | Microsoft Research Stanford University |
| Alma Mater: | University of California, Berkeley Princeton |
| Thesis Title: | Matrix Factorization and Matrix Concentration |
| Thesis Url: | https://escholarship.org/uc/item/08q2q9vw |
| Thesis Year: | 2012 |
| Doctoral Advisor: | Michael I. Jordan |
| Website: | https://web.stanford.edu/~lmackey |
Lester Mackey is an American computer scientist and statistician. He is a principal researcher at Microsoft Research and an adjunct professor at Stanford University. Mackey develops machine learning methods, models, and theory for large-scale learning tasks driven by applications from climate forecasting, healthcare, and the social good. He was named a 2023 MacArthur Fellow.[1]
Mackey grew up on Long Island.[2] He has said that, as a teenager, the Ross Mathematics Program in number theory introduced him to proof-based mathematics, where he learned about induction and rigorous proof. He got his first taste of academic research at the Research Science Institute. He joined Princeton University as an undergraduate student, where he earned his BSE in Computer Science. There he conducted research with Maria Klawe and David Walker.[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).[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. At Stanford, he created the Statistics for Social Good working group.
In 2016, Mackey joined Microsoft Research as a researcher and was appointed as an adjunct professor at Stanford University. He was made a principal researcher in 2019.
Mackey's early work developed a method to predict progression rates of people with ALS. He used the PRO-ACT database of clinical trial data and Bayesian inference to predict disease prognosis. 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. His models outperform the operational, physics-based dynamical models used by the United States Bureau of Reclamation.