Lester Wayne Mackey II
| |
|---|---|
| Born | |
| Alma mater | University of California, Berkeley Princeton |
| Scientific career | |
| Fields | Machine learning Computer science Statistics |
| Institutions | Microsoft Research Stanford University |
| Thesis | Matrix Factorization and Matrix Concentration (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.[2] He got his first taste of academic research at the Research Science Institute.[2] 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).[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. At Stanford, he created the Statistics for Social Good working group.[1]
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.[1]
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.[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.[1] His models outperform the operational, physics-based dynamical models used by the United States Bureau of Reclamation.[1]
{{cite web}}: CS1 maint: numeric names: authors list (link)
| International |
|
|---|---|
| National |
|
| Academics |
|
