His research focuses on scaling up all of the major practical methods of machine learning (ML) to massive datasets. Alex began working on this problem at NASA in 1993 (long before the current fashionable talk of big data). His large-scale algorithms helped enable the top scientific breakthrough of 2003 and have won a number of research awards. Alex served on the National Academy of Sciences Committee on the Analysis of Massive Data and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies. Alexander has degrees in applied mathematics and computer science from UC Berkeley and a PhD in computer science from Carnegie Mellon.... Read More.