Michael Mahoney

Biography

Michael W. Mahoney, PhD, is Vice President, Principal Scientist, Research Mentor, and Group Lead for the AI and Big Data group at ICSI. He is also a Professor Adjunct in the Department of Statistics and a member of the RISELab in the Department of Electrical Engineering & Computer Sciences at UC Berkeley, Group Lead for Machine Learning and Analytics at Lawrence Berkeley National Laboratory, and an Amazon Scholar. 

Dr. Mahoney’s research focuses on algorithmic and statistical aspects of modern large-scale data analysis. His recent work focuses 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; computational methods for neural network analysis; physics informed machine learning; and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis.  

Previously, he worked and taught at Yale University, Yahoo Research, and Stanford University. He received his PhD from Yale University with a dissertation in computational statistical mechanics.

Publications

Selected Leadership Activities

• Director, Foundations of Data Analysis Institute, UC Berkeley
• Member, National Advisory Committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI)
• Member, National Research Council Committee on the Analysis of Massive Data, 2011-2013
• Co-Organizer, Simons Institute programs on the foundations of data science, 2013 and 2018

Areas of Expertise

• Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Mathematical Optimization, Operations Research, and Decision Science
• Theoretical Foundations of AI and Machine Learning

ICSI Activity