Michael Mahoney

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar.

Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science.

Research Interests: Mahoney's work centers on the applied mathematics of data, spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science.

Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg, DetKit, Imate, and LeaderBot.

Awards & Honors:

• NeurIPS 2020 Best Paper Award (co-authored work on column subset selection)
• Director, NSF TRIPODS UC Berkeley FODA Institute
• Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK

Grants & Leadership:

• Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis)
• Group Lead, Machine Learning and Analytics, LBNL
• Vice President & Director, Big Data Group, ICSI

Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others.

Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.