Michael Mahoney,  at UC Berkeley

Michael Mahoney

UC Berkeley

Michael Mahoney is at UC Berkeley. He works on algorithmic and statistical aspects of modern large-scale data analysis. His recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale with a dissertation in computational statistical mechanics, and he has worked and taught at Yale in the mathematics department, at Yahoo Research, and at Stanford in the mathematics department.


Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on Spark and MPI Using Three Case Studies

Explore the trade-offs of performing linear algebra for data analysis and machine learning using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Apache Spark is designed for data analytics on cluster… Read more