Michael Mahoney - Research

Overview

The main focus of my work is on algorithmic and statistical aspects of modern large-scale data analysis. There is a focus on foundational/theoretical questions, but this theory is strongly tethered to implementational questions and a diverse range of very practical applications.

Theory:

• Randomized linear algebra and randomized numerical linear algebra.

• Stochastic optimization for convex and non-convex problems.

• Implicit regularization for scalable approximation/optimization algorithms.

• Local graph partitioning and approximation algorithms.

Implementations:

• of a range of core linear algebra, graph, and stochastic optimization algorithms

• on single machine, distributed data system, and supercomputer environments

Applications:

• Internet and social media analysis.

• Community structure, clustering, and information dynamics in large social and information networks.

• Genetics, medical imaging, astronomy/astrophysics, climate science, and a range of other scientific applications.

My dissertation was in computational statistical mechanics (the centerpiece was the development and analysis of the TIP5P model of liquid water). Prior to graduate school I worked in both computational and experimental biophysics on proteins and protein-nucleic acid interactions. After graduate school, I switched to theoretical computer science, where I did a lot of work on randomized algorithms for large matrix and graph problems. I also worked at Yahoo Research for several years, where I worked on large-scale web analytics, query log analysis, social media analysis, and social network analysis.

Software

See the full publication list for code to reproduce results on any one paper.

• Hessian Flow. For more details, see the arXiv paper.

• Alchemist project. For more details, see the RISE project page on Alchemist or the KDD 2018 paper or the CUG 2018 paper.

• Distributed Second-order Convex Optimization. For more details, see the arXiv paper.

• GPU-accelerated Sub-sampled Newton's Method. For more details, see the arXiv paper.

• Second-Order Optimization for Non-Convex Machine Learning. For more details, see the arXiv paper.

• Local Graph Clustering. For more details, see the PIEEE paper.

• Performance of linear algebra in Spark. For more details, see the arXiv paper, or the talk at the 2016 Dato Data Science Summit, or the blog post by Alex Gittens.

• LSRN: the randomized least-squares solver for parallel environments. For more details, see the LSRN paper.

Funding

• NSF Research Grant (with P. Drineas, M. Gu, and I. Ipsen), "Randomization as a Resource for Rapid Prototyping," 2018-2021, $450K.

• NSF Research Grant, "Combining Stochastics and Numerics for Improved Scalable Matrix Computations," 2018-2021, $500K.

• ONR Research Grant (with A. Shrivastava and R. Baraniuk), "Randomized Numerical Linear Algebra for Large-scale Learning and Inference," 2018-2022, $400K.

• NSF Research Grant (with B. Yu, F. Perez, R. Karp, and M. Jordan), "Berkeley Institute on Foundations of Data Analysis," 2017-2020, $1.5M.

• NSF Research Grant (with K. Ramchandran and S. Avestimehr), "Foundations of Coding for Modern Distributed Computing," 2017-2021, $350K.

• DOE Research Grant, "Scalable Inference for Adversarial Network Data," 2016-2018, $90K.

• DARPA Research Grant, D3M program, "Robust, Efficient, and Local Machine Learning Primitives," 2017-2021, $1.35M.

• Academic Research Gift: Adobe, Inc., "Terabyte-scale Regression Diagnostic Methods for Interactive and Exploratory Analytics," 2016-2018, $50K.

• ARO Research Grant, "Local Algorithms for Large Informatics Graphs," 2016-2019, $375K.

• Cray Research Grant, "Implementing and Evaluating Matrix Algorithms in Spark on High Performance Computing Platforms for Science Applications," 2015-2019, $1.0M.

• UCB Internal Research Grant, via BDD, "Improving the scaling of deep learning networks by characterizing and exploiting soft convexity," 2016-2019, $200K.

• NSF Research Grant, via Purdue CSoI, (with D. Gleich) "Quantifying the information content of a graph via information in graph diffusions," 2015-2019, $325K.

• NSF Travel Grant, "Streaming Algorithms for Fundamental Computations in Numerical Linear Algebra," (with J. Demmel, O. Schwartz, and S. Toldeo) 2015-2019, $40K.