Michael Mahoney's new home page

Basic info

I am at ICSI and the Department of Statistics at UC Berkeley, and I am also in the RISELab (in the past AMPLab) in the Department of Computer Science.

You can reach me via electronic mail: mmahoney is the username, and stat dot berkeley dot edu follows the at symbol. (Of course, my gmail address is still valid.) Also, some other (somewhat outdated) information can be found at my old web page at Stanford and my older web page at Yale.

Students, postdocs, etc.

Most of my work focuses on the theory and practice of what is now called big data, although I was doing it back when it was just massive, and prior to that when it was just large. On the theory side, we develop algorithms and statistical methods for matrix, graph, regression, optimization, and related problems. On the practice side, we provide implementations (e.g., on single machine, distributed data system, and supercomputer environments), and we also apply the methods to a range of problems in internet and social media analysis, social networks analysis, as well as genetics, mass spec imaging, astronomy, climate, and a range of other scientific applications.

I have been fortunate enough to work with a number of great students and postdocs over the years.

  • Zhewei Yao (student, UC Berkeley)
  • Feynman Liang (student, UC Berkeley)
  • Amir Gholaminejad (postdoc, UC Berkeley)
  • Kai Rothauge (postdoc, UC Berkeley)
  • Shusen Wang (postdoc, UC Berkeley)
  • Peng Xu (student, Stanford)
  • Aditya Devarakonda (UC Berkeley)
  • Julian Shun (postdoc, UC Berkeley, now faculty at MIT)
  • Kimon Fountoulakis (postdoc, UC Berkeley, now faculty at Waterloo)
  • Fred Roosta (postdoc, UC Berkeley, now faculty at Queensland)
  • Alex Gittens (postdoc, UC Berkeley; now faculty at RPI)
  • Di Wang (UC Berkeley PhD, 2017; now at Georgia Tech)
  • Jiyan Yang (Stanford PhD, 2016; now at Facebook)
  • Aaron Adcock (Stanford PhD, 2014; now at Facebook)
  • Xiangrui Meng (Stanford PhD, 2014; at LinkedIn, then Databricks)
  • Lorenzo Orecchia (intern, Yahoo; now faculty at Boston)
  • Jure Leskovec (intern, Yahoo; now faculty at Stanford)
  • Hari Narayanan (intern, Yahoo; faculty at Washington, then TIFR, Mumbai)
  • Jeff Phillips (intern, Yahoo; now faculty at Utah)
  • Lek-Heng Lim (intern, Yahoo; now faculty at Chicago)
  • Boulos Harb (intern, Yahoo; now at Google)

    • Research interests

    • Algorithmic and statistical aspects of modern large-scale data analysis.

    • Randomized linear algebra and randomized numerical linear algebra.

    • Scalable convex and non-convex optimization in parallel/distributed environments.

    • Implicit regularization and implicit optimization methods in scalable approximation algorithms.

    • Graph approximation algorithms and applications to large social and information networks.

    • Applications to DNA microarray, SNP, astronomical, medical imaging, and other scientific data.

    A lot of my work has focused on Randomized Linear Algebra, i.e., using random sampling and random projection methods to solve very large matrix-based problems; developing geometric network analysis tools, i.e., using scalable approximation algorithms with a geometric or statistical flavor to analyze the structure and dynamics of large informatics graphs; developing approximate computation and regularization methods for large informatics graphs; applications to community detection, clustering, and information dynamics in large social and information networks; and applications to DNA single nucleotide polymorphism (SNP) data, astronomical and medical imaging data, and large-scale statistical data analysis more generally.

    In the past, I developed and analyzed algorithms for large matrix, graph, and regression problems, and I applied these and related tools to the statistical data analysis of extremely large scientific and Internet data sets. For example, I worked on large-scale web analytics, machine learning, and query log analysis; applications of graph partitioning algorithms to clustering and community identification; and applications of randomized matrix algorithms to hyperspectral medical image data, DNA microarray data, and DNA SNP data.

    In the more distant past, I have also worked on developing and analyzing Monte Carlo algorithms for performing useful computations on extremely large matrices, e.g., the additive-error and relative-error CUR matrix decompositions. Past research has also included work in computational statistical mechanics on the development and analysis of the TIP5P model of liquid water, as well as work in both computational and experimental biophysics on proteins and protein-nucleic acid interactions.

    Other items of interest

    Software. (See publication list for code to reproduce results on any one paper.)

  • Alchemist project. For more details, see the RISE project page on Alchemist or the KDD 2018 paper or the CUG 2018 paper.
  • Subsampled Second-order non-convex optimization. For more details, see the arXiv paper.
  • GPU-accelerated Sub-sampled Newton's Method. 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.

    • Teaching.

  • Spring 2018: Linear Algebra for Data Science: A four-unit introductory class on linear algebra, from the perspective of probablity and related topics in the mathematics of data. (Expanded from a two-unit "connector" class on mathematics for matrices/graphs/data that I designes and taught during Spring 2017 and Spring 2016.) (I have a detailed but work-in-progress set of course notes for this class. I will post them at an appropriate point; but, in the meantime, email me if you are interested in them.)
  • Spring 2015: "Stat260/CS294: Topics in Spectral Graph Methods", an overview of practical spectral graph theory. Update: full set of course lectures.
  • Fall 2013: "Stat260/CS294: Randomized Algorithms for Matrices and Data", an overview of Randomized Linear Algebra. Update: full set of course lectures.
  • Fall 2009: CS369M: Algorithms for Massive Data Set Analysis

    • Workshops.

  • "Foundations of Data Science": Fall 2018 program at the Simons Institute at UC Berkeley.
  • MMDS 2016, 2014, ...: for more information, see the main MMDS web page, or the MMDS Video Library.
  • "Mathematics of Data": 2016 Summer School at the PCMI (Park City Mathematics Institute); for more information, see the flyer and the (upcoming) edited volume of lectures.
  • "RandNLA: Randomization in Numerical Linear Algebra": 2015 Gene Golub SIAM Summer School, June 2015, in Delphi, Greece; for more information, see the flyer, or the SIAM News article about it.
  • "Theoretical Foundations of Big Data Analysis": Fall 2013 program at the Simons Institute at UC Berkeley.

    • FODA/TRIPODS.

  • I am the PI on the new UC Berkeley FODA (Foundations of Data Analysis) Institute grant, which is part of the NSF TRIPODS program, to deepen the theoretical foundations of data science in a new transdisciplinary institute. Also involved are coPIs Bin Yu, Fernando Perez, Michael Jordan, and Dick Karp. Here is more information about it:
    The main FODA web page.
    The UC Berkeley press release about it.
    The summary article from Datanami about it.
    The original NSF announcement.
    The the report from the planning workshop.

    • Some press.

  • RISELab challenges, as described in our tecnical report.
  • Video synopsis of the Communications of the ACM article on "RandNLA: Randomized Numerical Linear Algebra."
  • Postdoc Julian Shun who has been awarded the ACM's doctoral dissertation award for his 2015 CMU doctoral thesis "Shared-Memory Parallelism Can Be Simple, Fast, and Scalable".
  • AMPLab-Cray-NERSC/LBL collaboration, on HPC Wire, which describes our project to implement randomized linear algebra (RLA) algorithms in Spark on HPC platforms for LBL's scientific data applications.
  • Role of RLA in HPC, as described HPC Wire.

    • Miscellaneous.

  • Charles Martin gave a talk on our recent work on "Why Deep Learning Works: Self Regularization in Neural Networks" at LBNL, June 2018.
  • Kai Rothauge gave a talk on our recent work on "Alchemist: An Apache Spark <=> MPI Interface" at the Spark+AI meeting, June 2018.
  • I gave a talk on our recent work on "Alchemist: An Apache Spark <=> MPI Interface" at the XLDB meeting, May 2018.
  • I attended and gave a talk on "BIG Biomedicine and the Foundations of BIG Data Analysis" at Stanford University at Stanford Medical School, May 23, 2014, at their "Big Data in BioMedicine" Conference. Click "here" for a video of the talk. They also got two nice CEO-style pictures: here I am, and here I am again.
  • Click here for information (including the slides and video!) on the Tutorial on "Geometric Tools for Identifying Structure in Large Social and Information Networks," given originally at ICML10 and KDD10 and subsequently at many other places. (The slides are also linked to below.)
  • We have made public many of the networks we have used in our "Community Structure" papers. Click here for the networks and for related information.

    • Publications

    2018
    • Distributed Second-order Convex Optimization,
      • C.-H. Fang, S. B Kylasa, F. Roosta-Khorasani, M. W. Mahoney, and A. Grama,
      Technical Report, Preprint: arXiv:1807.07132 (2018) (arXiv),
    • Alchemist: An Apache Spark <=> MPI Interface,
      • A. Gittens, K. Rothauge, M. W. Mahoney, S. Wang, L. Gerhardt, Prabhat, J. Kottalam, M. Ringenburg, and K. Maschhoff,
      Technical Report, Preprint: arXiv:1806.01270 (2018) (arXiv),
      Accepted for publication, CUG 2018.
    • Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist,
      • A. Gittens, K. Rothauge, S. Wang, M. W. Mahoney, L. Gerhardt, Prabhat, J. Kottalam, M. Ringenburg, and K. Maschhoff,
      Technical Report, Preprint: arXiv:1805.11800 (2018) (arXiv),
      Accepted for publication, KDD 2018.
    • Group Collaborative Representation for Image Set Classification,
      • B. Liu, L. Jing, J. Li, J. Yu, A. Gittens, and M. W. Mahoney,
      International Journal of Computer Vision, 1-26 (2018) (pdf).
    • Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap,
      • M. E. Lopes, S. Wang, M. W. Mahoney,
      Technical Report, Preprint: arXiv:1803.08021 (2018) (arXiv),
      Proc. of the 35th ICML Conference 3223-3232 (2018) (pdf)
      Journal version submitted for publication.
    • GPU Accelerated Sub-Sampled Newton's Method,
      • S. B. Kylasa, F. Roosta-Khorasani, M. W. Mahoney, and A. Grama,
      Technical Report, Preprint: arXiv:1802.09113 (2018) (arXiv), (code),
      Submitted for publication.
    • Hessian-based Analysis of Large Batch Training and Robustness to Adversaries,
      • Z. Yao, A. Gholami, Q. Lei, K. Keutzer, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1802.08241 (2018) (arXiv),
      Submitted for publication.
    • Inexact Non-Convex Newton-Type Methods,
      • Z. Yao, P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1802.06925 (2018) (arXiv),
      Submitted for publication.
    • Out-of-sample extension of graph adjacency spectral embedding,
      • K. Levin, F. Roosta-Khorasani, M. W. Mahoney, and C. E. Priebe,
      Technical Report, Preprint: arXiv:1802.06307 (2018) (arXiv),
      Proc. of the 35th ICML Conference 2981-2990 (2018) (pdf)
      Journal version submitted for publication.
    2017
    • Lectures on Randomized Numerical Linear Algebra,
      • P. Drineas and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1712.08880 (2017) (arXiv),
      To appear in: Lectures of the 2016 PCMI Summer School on Mathematics of Data.
    • Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization,
      • A. Devarakonda, K. Fountoulakis, J. Demmel, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1712.06047 (2017) (arXiv),
      Accepted for publication, Proc. IPDPS 2018.
    • Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior, (click here for a blog about this paper)
      • C. H. Martin and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1710.09553 (2017) (arXiv),
    • LASAGNE: Locality And Structure Aware Graph Node Embedding,
      • E. Faerman, F. Borutta, K. Fountoulakis, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1710.06520 (2017) (arXiv),
    • A Berkeley View of Systems Challenges for AI,
      • I. Stoica, D. Song, R. A. Popa, D. A. Patterson, M. W. Mahoney, R. H. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. Gonzalez, K. Goldberg, A. Ghodsi, D. E. Culler, and P. Abbeel,
      Technical Report No. UCB/EECS-2017-159, October 2017 (www),
    • GIANT: Globally Improved Approximate Newton Method for Distributed Optimization,
      • S. Wang, F. Roosta-Khorasani, P. Xu, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1709.03528 (2017) (arXiv), (Spark code), (Python code),
      Submitted for publication.
    • Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study,
      • P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1708.07827 (2017) (arXiv), (code),
      Submitted for publication.
    • Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information,
      • P. Xu, F. Roosta-Khorasani, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1708.07164 (2017) (arXiv),
      Submitted for publication.
    • A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication,
      • M. E. Lopes, S. Wang, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1708.01945 (2017) (arXiv),
      Submitted for publication.
    • Capacity releasing diffusions for speed and locality,
      • D. Wang, K. Fountoulakis, M. Henzinger, M. W. Mahoney, and S. Rao,
      Technical Report, Preprint: arXiv:1706.05826 (2017) (arXiv),
      Proc. of the 34th ICML Conference 3598-3607 (2017) (pdf) (supp) (talk).
    • Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds,
      • S. Wang, A. Gittens, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1706.02803 (2017) (arXiv),
      Accepted for publication, J. Machine Learning Research.
    • Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction,
      • K. E. Bouchard, A. F. Bujan, F. Roosta-Khorasani, S. Ubaru, Prabhat, A. M. Snijders, J.-H. Mao, E. F. Chang, M. W. Mahoney, S. Bhattacharyya,
      Technical Report, Preprint: arXiv:1705.07585 (2017) (arXiv),
      Proc. of the 2017 NIPS Conference (pdf).
    • Skip-Gram - Zipf + Uniform = Vector Additivity,
      • A. Gittens, D. Achlioptas, and M. W. Mahoney,
      Proc. of the 55th ACL Meeting 69-76 (2017) (pdf).
    • Principles and Applications of Science of Information [Scanning the Issue],
      • T. Courtade, A. Grama, M. W. Mahoney, and T. Weissman,
      Proceedings of the IEEE, 105(2): 183-188 (2017) (pdf).
    • Social Discrete Choice Models,
      • D. Zhang, K. Fountoulakis, J. Cao, M. Yin, M. W. Mahoney, and A. Pozdnoukhov,
      Technical Report, Preprint: arXiv:1703.07520 (2017) (arXiv),
      Submitted for publication.
    • Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging,
      • S. Wang, A. Gittens, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1702.04837 (2017) (arXiv),
      Proc. of the 34th ICML Conference 3608-3616 (2017) (pdf),
      J. Machine Learning Research, 18(218): 1-50 (2018) (pdf).
    2016
    • Avoiding communication in primal and dual block coordinate descent methods,
      • A. Devarakonda, K. Fountoulakis, J. Demmel, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1612.04003 (2016) (arXiv),
      Submitted for publication.
    • Feature-distributed sparse regression: a screen-and-clean approach,
      • J. Yang, M. W. Mahoney, M. A. Saunders, and Y. Sun,
      Proc. of the 2016 NIPS Conference (pdf).
    • Multi-label learning with semantic embeddings,
      • L. Jing, M. Cheng, L. Yang, A. Gittens, M. W. Mahoney,
      ICLR 2017 OpenReview.net (www),
    • Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxy Data,
      • D. Lawlor, T. Budavari, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1609.03932 (2016) (arXiv),
      The Astrophysical Journal, 833:1, 26 (2016) (pdf).
    • Lecture Notes on Spectral Graph Methods,
      • M. W. Mahoney,
      Technical Report, Preprint: arXiv:1608.04845 (2016) (arXiv),
    • Lecture Notes on Randomized Linear Algebra,
      • M. W. Mahoney,
      Technical Report, Preprint: arXiv:1608.04481 (2016) (arXiv),
    • An optimization approach to locally-biased graph algorithms,
      • K. Fountoulakis, D. F. Gleich, M. W. Mahoney,
      Technical Report, Preprint: arXiv:1607.04940 (2016) (arXiv),
      Proceedings of the IEEE, 105(2): 256-272 (2017) (pdf).
    • DCAR: A Discriminative and Compact Audio Representation to Improve Event Detection,
      • L. Jing, B. Liu, J. Choi, A. Janin, J. Bernd, M. W. Mahoney, and G. Friedland,
      Technical Report, Preprint: arXiv:1607.04378 (2016) (arXiv),
      Proc. of the 2016 ACM Multimedia Conference 57-61 (2016) (pdf),
      IEEE Transactions on Multimedia, 19(12): 2637-2650 (2017) (pdf).
    • Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies,
      • A. Gittens, A. Devarakonda, E. Racah, M. Ringenburg, L. Gerhardt, J. Kottalam, J. Liu, K. Maschhoff, S. Canon, J. Chhugani, P. Sharma, J. Yang, J. Demmel, J. Harrell, V. Krishnamurthy, M. W. Mahoney, and Prabhat,
      Technical Report, Preprint: arXiv:1607.01335 (2016) (arXiv), (code),
      Proc. 2016 IEEE BigData, 204-213 (2016) (pdf).
    • Sub-sampled Newton Methods with Non-uniform Sampling,
      • P. Xu, J. Yang, F. Roosta-Khorasani, C. Re, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1607.00559 (2016) (arXiv),
      Proc. of the 2016 NIPS Conference (pdf).
    • Approximating the Solution to Mixed Packing and Covering LPs in parallel time,
      • M. W. Mahoney, S. Rao, D. Wang, and P. Zhang,
      Proc. of the 43rd ICALP Conference, 52:1-52:14 (2016) (pdf).
    • A Simple and Strongly-Local Flow-Based Method for Cut Improvement,
      • N. Veldt, D. F. Gleich, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1605.08490 (2016) (arXiv),
      Proc. of the 33rd ICML Conference 1938-1947 (2016) (pdf), (supp).
    • RandNLA: Randomized Numerical Linear Algebra,
      • P. Drineas and M. W. Mahoney,
      Communications of the ACM, 59, 80-90 (2016) (pdf).
    • FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods,
      • X. Cheng, F. Roosta-Khorasani, S. Palombo, P. L. Bartlett, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1605.08108 (2016) (arXiv),
      Proc. of the 21st International Conference on AISTATS, PMLR 84:404-414 (2018) (pdf, supp).
    • Parallel Local Graph Clustering,
      • J. Shun, F. Roosta-Khorasani, K. Fountoulakis, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1604.07515 (2016) (arXiv),
      Proceedings of the VLDB Endowment, 9(12) 1041-1052 (2016) (pdf).
    • A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark,
      • A. Gittens, J. Kottalam, J. Yang, M. F. Ringenburg, J. Chhugani, E. Racah, M. Singh, Y. Yao, C. Fischer, O. Ruebel, B. Bowen, N. G. Lewis, M. W. Mahoney, V. Krishnamurthy, and Prabhat,
      Proc. 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, at IPDPS, 2016 (pdf).
    • Mining Large Graphs,
      • D. F. Gleich and M. W. Mahoney,
      In Handbook of Big Data. pp. 191-220, edited by P. Buhlmann, P. Drineas, M. Kane, and M. van de Laan, Chapman and Hall/CRC Press, 2016 (pdf).
    • Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms,
      • M. W. Mahoney and P. Drineas,
      In Handbook of Big Data. pp. 137-154, edited by P. Buhlmann, P. Drineas, M. Kane, and M. van de Laan, Chapman and Hall/CRC Press, 2016 (pdf).
    • Variational Perspective on Local Graph Clustering,
      • K. Fountoulakis, X. Cheng, J. Shun, F. Roosta-Khorasani and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1602.01886 (2016) (arXiv),
      Mathematical Programming, 1-21 (2017) (pdf).
    • Sub-Sampled Newton Methods II: Local Convergence Rates,
      • F. Roosta-Khorasani and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1601.04738 (2016) (arXiv),
      Journal version submitted for publication.
    • Sub-Sampled Newton Methods I: Globally Convergent Algorithms,
      • F. Roosta-Khorasani and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1601.04737 (2016) (arXiv),
      Journal version submitted for publication.
    • RandNLA, Pythons, and the CUR for Your Data Problems: Reporting from G2S3 2015 in Delphi,
      • E. Gallopoulos, P. Drineas, I. Ipsen, and M. W. Mahoney,
      SIAM News 49:1 January/February 2016 (web), (pdf).
    2015
    • Faster Parallel Solver for Positive Linear Programs via Dynamically-Bucketed Selective Coordinate Descent,
      • D. Wang, M. W. Mahoney, N. Mohan, and S. Rao,
      Technical Report, Preprint: arXiv:1511.06468 (2015) (arXiv).
    • A Local Perspective on Community Structure in Multilayer Networks,
      • L. G. S. Jeub, M. W. Mahoney, P. J. Mucha, and M. A. Porter,
      Technical Report, Preprint: arXiv:1510.05185 (2015) (arXiv),
      Network Science, 5(2): 144-163, 2017 (pdf).
    • Optimal Subsampling Approaches for Large Sample Linear Regression,
      • R. Zhu, P. Ma, M. W. Mahoney, and B. Yu,
      Technical Report, Preprint: arXiv:1509.05111 (2015) (arXiv).
    • Unified Acceleration Method for Packing and Covering Problems via Diameter Reduction,
      • D. Wang, S. Rao, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1508.02439 (2015) (arXiv),
      Proc. of the 43rd ICALP Conference, 50:1-50:13 (2016) (pdf).
    • Using local spectral methods to robustify graph-based learning algorithms,
      • D. F. Gleich and M. W. Mahoney,
      Proc. of the 21st Annual SIGKDD, (2015) (pdf) (code).
    • Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation,
      • R. Wang, Y. Li, M. W. Mahoney, and E. Darve,
      Technical Report, Preprint: arXiv:1502.03571 (2015) (arXiv).
    • Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions,
      • J. Yang, O. Rubel, Prabhat, M. W. Mahoney, and B. P. Bowen,
      Analytical Chemistry, 87 (9), 4658-4666 (2015) (pdf) (code).
    • Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrom Method,
      • D. G. Anderson, S. S. Du, M. W. Mahoney, C. Melgaard, K. Wu, and M. Gu,
      Proc. of the 18th International Conference on AISTATS, PMLR 38:19-27 (2015) (pdf, supp) (code).
    • Weighted SGD for Lp Regression with Randomized Preconditioning,
      • J. Yang, Y.-L. Chow, C. Re, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1502.03571 (2015) (arXiv),
      Proc. of the 27-th Annual SODA, 558-569 (2016) (pdf),
      J. Machine Learning Research, 18(211): 1-43 (2018) (pdf).
    • Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments,
      • J. Yang, X. Meng, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1502.03032 (2015) (arXiv) (code),
      Proceedings of the IEEE 104(1): 58-92 (2016) (pdf).
    2014
    • Tree decompositions and social graphs,
      • A. B. Adcock, B. D. Sullivan, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1411.1546 (2014) (arXiv), (code).
      Internet Mathematics, 12(5), 315-361 (2016) (pdf).
    • Fast Randomized Kernel Methods With Statistical Guarantees,
      • A. El Alaoui and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1411.0306 (2014) (arXiv),
      Proc. of the 2015 NIPS Conference (pdf).
    • Signal Processing for Big Data (Editorial for Special Issue)
      • G. B. Giannakis, F. Bach, R. Cendrillon, M. Mahoney, and J. Neville,
      IEEE Signal Processing Magazine, 31: 15-16 (September 2014) (pdf).
    • A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares,
      • G. Raskutti and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1406.5986 (2014) (arXiv),
      Proc. of the 32nd ICML Conference (2015) (pdf),
      J. Machine Learning Research, 17(214): 1-31, (2016) (pdf).
    • Random Laplace Feature Maps for Semigroup Kernels on Histograms,
      • J. Yang, V. Sindhwani, Q. Fan, H. Avron, and M. W. Mahoney,
      Proc. of the 27th CVPR Conference, 971-978 (2014) (pdf).
    • Anti-differentiating Approximation Algorithms: A case study with Min-cuts, Spectral, and Flow,
      • D. F. Gleich and M. W. Mahoney,
      Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 1018-1025 (2014) (pdf) (code, code) (talk).
    • Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels,
      • J. Yang, V. Sindhwani, H. Avron, and M. W. Mahoney,
      Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 485-493 (2014) (pdf), (code),
      Technical Report, Preprint: arXiv:1412.8293 (2014) (arXiv),
      J. Machine Learning Research, 17(120): 1-38 (2016) (pdf).
    • Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks,
      • L. G. S. Jeub, P. Balachandran, M. A. Porter, P. J. Mucha, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1403.3795 (2014) (arXiv), (code, code),
      Physical Review E, 91, 012821 (2015) (pdf).
    • A new spin on an old algorithm: technical perspective on "Communication costs of Strassen's matrix multiplication,"
      • M. W. Mahoney,
      Communications of the ACM, 57(2): 106 (2014) (pdf).
    2013
    • Tree-like Structure in Large Social and Information Networks,
      • A. B. Adcock, B. D. Sullivan, and M. W. Mahoney,
      Proc. of the 2013 IEEE ICDM, 1-10 (2013) (pdf).
    • Objective Identification of Informative Wavelength Regions in Galaxy Spectra,
      • C.-W. Yip, M. W. Mahoney, A. S. Szalay, I. Csabai, T. Budavari, R. F. G. Wyse, and L. Dobos,
      Technical Report, Preprint: arXiv:1312.0637 (2013) (arXiv),
      Astronomical Journal, 147, 5, 110 (2014) (pdf).
    • Evaluating OpenMP Tasking at Scale for the Computation of Graph Hyperbolicity,
      • A. B. Adcock, B. D. Sullivan, O. R. Hernandez, and M. W. Mahoney,
      Proc. of the 9-th IWOMP, 71-83 (2013) (pdf).
    • Frontiers in Massive Data Analysis,
      • Committee on the Analysis of Massive Data, et al. (M. I. Jordan, et al.),
      The National Academies Press (2013) (pdf), (web).
    • A Statistical Perspective on Algorithmic Leveraging,
      • P. Ma, M. W. Mahoney, and B. Yu,
      Technical Report, Preprint: arXiv:1306.5362 (2013) (arXiv),
      Proc. of the 31st ICML Conference, JMLR W&CP 32 (1): 91-99 (2014) (pdf),
      J. Machine Learning Research, 16, 861-911 (2015) (pdf).
    • Robust Regression on MapReduce,
      • X. Meng, and M. W. Mahoney,
      Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 888-896 (2013) (pdf).
    • Quantile Regression for Large-scale Applications,
      • J. Yang, X. Meng, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1305.0087 (2013) (arXiv), (code),
      Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 881-887 (2013) (pdf),
      SIAM J. Scientific Computing, 36(5), S78-S110 (2014) (pdf).
    • Revisiting the Nystrom Method for Improved Large-Scale Machine Learning,
      • A. Gittens and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1303.1849 (2013) (arXiv), (code),
      Proc. of the 30th ICML Conference, JMLR W&CP 28(3): 567-575 (2013) (pdf),
      J. Machine Learning Research, 17(117): 1-65 (2016) (pdf).
    2012
    • Semi-supervised Eigenvectors for Large-scale Locally-biased Learning,
      • T. J. Hansen and M. W. Mahoney,
      Proc. of the 2012 NIPS Conference (pdf), (code),
      Technical Report, Preprint: arXiv:1304.7528 (2013) (arXiv),
      J. Machine Learning Research, 15, 3691-3734 (2014) (pdf).
    • Low-distortion Subspace Embeddings in Input-sparsity Time and Applications to Robust Linear Regression,
      • X. Meng and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1210.3135 (2012) (arXiv),
      Proc. of the 45-th STOC, 91-100 (2013) (pdf).
    • The Fast Cauchy Transform and Faster Robust Linear Regression,
      • K. L. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff,
      Technical Report, Preprint: arXiv:1207.4684 (2012) (arXiv),
      Proc. of the 24-th Annual SODA, 466-477 (2013) (pdf),
      SIAM J. Computing, 45, 763-810 (2016) (pdf).
    • rCUR: an R package for CUR matrix decomposition,
      • A. Bodor, I. Csabai, M. W. Mahoney, and N. Solymosi,
      BMC Bioinformatics, 13:103 (2012) (pdf), (code).
    • Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis,
      • M. W. Mahoney,
      Technical Report, Preprint: arXiv:1203.0786 (2012) (arXiv),
      Proc. of the 2012 ACM Symposium on Principles of Database Systems, 143-154, 2012 (pdf).
    • On the Hyperbolicity of Small-World and Tree-Like Random Graphs,
      • W. Chen, W. Fang, G. Hu, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1201.1717 (2012) (arXiv),
      Proc. of the 23-rd ISAAC 278-288 (2012) (pdf),
      Internet Mathematics, 9(4), 434-491 (2013) (pdf).
    2011
    • Randomized Dimensionality Reduction for K-means Clustering,
      • C. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas,
      Technical Report, Preprint: arXiv:1110.2897 (2011) (arXiv),
      IEEE Transactions on Information Theory, 61(2), 1045-1062 (2015) (pdf).
    • Regularized Laplacian Estimation and Fast Eigenvector Approximation,
      • P. O. Perry and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1110.1757 (2011) (arXiv),
      Proc. of the 2011 NIPS Conference (pdf).
    • LSRN: A Parallel Iterative Solver for Strongly Over- or Under-Determined Systems,
      • X. Meng, M. A. Saunders, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1109.5981 (2011) (arXiv), (code),
      SIAM J. Scientific Computing, 36(2), C95-C118 (2014) (pdf).
    • Fast approximation of matrix coherence and statistical leverage,
      • P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. P. Woodruff,
      Technical Report, Preprint: arXiv:1109.3843 (2011) (arXiv),
      Proc. of the 29th ICML Conference (2012) (pdf),
      J. Machine Learning Research, 13, 3475-3506 (2012) (pdf).
    • Localization on low-order eigenvectors of data matrices,
      • M. Cucuringu and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1109.1355 (2011) (arXiv).
    • Efficient Genomewide Selection of PCA-Correlated tSNPs for Genotype Imputation,
      • A. Javed, P. Drineas, M. W. Mahoney, and P. Paschou,
      Annals of Human Genetics, 75, 707-722 (2011) (pdf).
    • Randomized Algorithms for Matrices and Data,
      • M. W. Mahoney,
      Foundations and Trends in Machine Learning, NOW Publishers, Volume 3, Issue 2, 2011 (now),
      TR version: Technical Report, Preprint: arXiv:1104.5557 (2011) (arXiv).
      (Abridged version in: Advances in Machine Learning and Data Mining for Astronomy, edited by M. J. Way, et al., pp. 647-672, 2012.)
    2010
    • Computation in Large-Scale Scientific and Internet Data Applications is a Focus of MMDS 2010,
      • M. W. Mahoney,
      Technical Report, Preprint: arXiv:1012.4231 (2010) (arXiv),
      Appeared in SIGKDD Explorations, SIGACT News, ASA-SCGN Newsletter, and IMS Bulletin.
    • CUR from a Sparse Optimization Viewpoint,
      • J. Bien, Y. Xu, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1011.0413 (2010) (arXiv),
      Proc. of the 2010 NIPS Conference (ps, pdf).
    • Algorithmic and Statistical Perspectives on Large-Scale Data Analysis,
      • M. W. Mahoney,
      Technical Report, Preprint: arXiv:1010.1609 (2010) (arXiv),
      In: Combinatorial Scientific Computing, pp. 427-469, edited by U. Naumann and O. Schenk, 2012.
    • Implementing regularization implicitly via approximate eigenvector computation,
      • M. W. Mahoney and L. Orecchia,
      Technical Report, Preprint: arXiv:1010.0703 (2010) (arXiv),
      Proc. of the 28th ICML Conference, 121-128 (2011) (pdf) (talk).
    • Approximating Higher-Order Distances Using Random Projections,
      • P. Li, M. W. Mahoney, and Y. She,
      Proc. of the 26th UAI Conference, 312-321 (2010) (ps, pdf),
      Technical Report, Preprint: arXiv:1203.3492 (2012) (arXiv).
    • Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving,
      • P. Drineas and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1005.3097 (2010) (arXiv).
    • Empirical Comparison of Algorithms for Network Community Detection,
      • J. Leskovec, K. J. Lang, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:1004.3539 (2010) (arXiv),
      Proc. of the 19-th International WWW, 631-640 (2010) (ps, pdf).
    2009
    • A Local Spectral Method for Graphs: with Applications to Improving Graph Partitions and Exploring Data Graphs Locally,
      • M. W. Mahoney, L. Orecchia, and N. K. Vishnoi,
      Technical Report, Preprint: arXiv:0912.0681 (2009) (arXiv),
      J. Machine Learning Research, 13, 2339-2365 (2012) (ps, pdf).
    • Unsupervised Feature Selection for the k-means Clustering Problem,
      • C. Boutsidis, M. W. Mahoney, and P. Drineas,
      Proc. of the 2009 NIPS Conference (ps, pdf).
    • Learning with Spectral Kernels and Heavy-Tailed Data,
      • M. W. Mahoney and H. Narayanan,
      Technical Report, Preprint: arXiv:0906.4539 (2009) (arXiv).
    • Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow,
      • K. J. Lang, M. W. Mahoney, and L. Orecchia,
      Proc. of the 8-th International SEA, 197-208 (2009) (ps, pdf).
    • CUR Matrix Decompositions for Improved Data Analysis,
      • M. W. Mahoney and P. Drineas,
      Proc. Natl. Acad. Sci. USA, 106, 697-702 (2009) (ps, pdf).
    2008
    • An Improved Approximation Algorithm for the Column Subset Selection Problem,
      • C. Boutsidis, M. W. Mahoney, and P. Drineas,
      Technical Report, Preprint: arXiv:0812.4293 (2008) (arXiv),
      Proc. of the 20-th Annual SODA, 968-977 (2009) (ps, pdf).
    • Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008
      • M. W. Mahoney, L.-H. Lim, and G. E. Carlsson
      Technical Report, Preprint: arXiv:0812.3702 (2008) (arXiv),
      Appeared in SIGKDD Explorations (ps, pdf), SIAM News (ps, pdf), and ASA-SCGN Newsletter (ps, pdf), and abridged versions appeared in IMS Bulletin (ps, pdf) and AmStat News.
    • Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters,
      • J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:0810.1355 (2008) (arXiv),
      Internet Mathematics, 6(1), 29-123 (2009) (pdf).
    • Unsupervised Feature Selection for Principal Components Analysis,
      • C. Boutsidis, M. W. Mahoney, and P. Drineas,
      Proc. of the 14-th Annual SIGKDD, 61-69 (2008) (ps, pdf).
    • Statistical Properties of Community Structure in Large Social and Information Networks,
      • J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney,
      Proc. of the 17-th International WWW, 695-704 (2008) (ps, pdf).
    2007
    • Faster Least Squares Approximation,
      • P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlos,
      Technical Report, Preprint: arXiv:0710.1435 (2007) (arXiv),
      Numerische Mathematik, 117, 219-249 (2011) (pdf).
    • PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations,
      • P. Paschou, E. Ziv, E. G. Burchard, S. Choudhry, W. Rodriguez-Cintron, M. W. Mahoney, and P. Drineas,
      PLoS Genetics, 3, 1672-1686 (2007) (ps, pdf).
    • Relative-Error CUR Matrix Decompositions,
      • P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
      Technical Report, Preprint: arXiv:0708.3696 (2007) (arXiv),
      SIAM J. Matrix Analysis and Applications, 30, 844-881 (2008) (ps, pdf).
    • Feature Selection Methods for Text Classification,
      • A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. W. Mahoney,
      Proc. of the 13-th Annual SIGKDD, 230-239 (2007) (ps, pdf).
    • Sampling Algorithms and Coresets for Lp Regression,
      • A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney,
      Technical Report, Preprint: arXiv:0707.1714 (2007) (arXiv),
      Proc. of the 19-th Annual SODA, 932-941 (2008) (ps, pdf),
      SIAM J. Computing, 38, 2060-2078 (2009) (ps, pdf).
    • Web Information Retrieval and Linear Algebra Algorithms,
      • A. Frommer, M. W. Mahoney, and D. B. Szyld (Eds.),
      Proc. of Dagstuhl Seminar 07071, (2007) (web).
    • Intra- and interpopulation genotype reconstruction from tagging SNPs,
      • P. Paschou, M. W. Mahoney, A. Javed, J. R. Kidd, A. J. Pakstis, S. Gu, K. K. Kidd, and P. Drineas,
      Genome Research, 17(1), 96-107 (2007) (ps, pdf).
    2006
    • Bridging the Gap Between Numerical Linear Algebra, Theoretical Computer Science, and Data Applications,
      • G. H. Golub, M. W. Mahoney, P. Drineas, and L.-H. Lim,
      SIAM News 39:8 October 2006 (ps, pdf).
    • Randomized Algorithms for Matrices and Massive Data Sets,
      • P. Drineas and M. W. Mahoney,
      Proc. of the 32-nd Annual VLDB, 1269 (2006) (ps, pdf).
    • Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods,
      • P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
      Proc. of the 14-th Annual ESA, 304-314 (2006) (ps, pdf).
    • Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods,
      • P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
      Proc. of the 10-th Annual RANDOM, 316-326 (2006) (ps, pdf).
    • Tensor-CUR Decompositions For Tensor-Based Data,
      • M. W. Mahoney, M. Maggioni, and P. Drineas,
      Proc. of the 12-th Annual SIGKDD, 327-336 (2006) (ps, pdf),
      SIAM J. Matrix Analysis and Applications, 30, 957-987 (2008) (ps, pdf).
    • Polynomial Time Algorithm for Column-Row-Based Relative-Error Low-Rank Matrix Approximation,
      • P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
      Technical Report, DIMACS TR 2006-04 March 2006 (ps, pdf).
    • Sampling Algorithms for L2 Regression and Applications,
      • P. Drineas, M. W. Mahoney, and S. Muthukrishnan,
      Proc. of the 17-th Annual SODA, 1127-1136 (2006) (ps, pdf).
    2005
    • A Randomized Algorithm for a Tensor-Based Generalization of the Singular Value Decomposition,
      • P. Drineas and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1327, June 2005 (ps, pdf),
      Linear Algebra and its Applications, 420, 553-571 (2007) (ps, pdf).
    • On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning,
      • P. Drineas and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1319, April 2005 (ps, pdf),
      Proc. of the 18-th Annual COLT, 323-337 (2005) (ps, pdf),
      J. Machine Learning Research, 6, 2153-2175 (2005) (ps, pdf).
    2004
    • Sampling Sub-problems of Heterogeneous Max-Cut Problems and Approximation Algorithms,
      • P. Drineas, R. Kannan, and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1283, April 2004 (ps, pdf),
      Proc. of the 22-nd Annual STACS, 57-68 (2005) (ps, pdf),
      Random Structures and Algorithms, 32:3, 307-333 (2008) (ps, pdf).
    • Fast Monte Carlo Algorithms for Matrices III: Computing an Efficient Approximate Decomposition of a Matrix,
      • P. Drineas, R. Kannan, and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1271, February 2004 (ps, pdf),
      SIAM J. Computing, 36, 184-206 (2006) (ps, pdf).
    • Fast Monte Carlo Algorithms for Matrices II: Computing Low-Rank Approximations to a Matrix,
      • P. Drineas, R. Kannan, and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1270, February 2004 (ps, pdf),
      SIAM J. Computing, 36, 158-183 (2006) (ps, pdf).
    • Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication,
      • P. Drineas, R. Kannan, and M. W. Mahoney,
      Technical Report, YALEU/DCS/TR-1269, February 2004 (ps, pdf),
      SIAM J. Computing, 36, 132-157 (2006) (ps, pdf).
    2003
    • Rapid Mixing of Several Markov Chains for a Hard-Core Model,
      • R. Kannan, M. W. Mahoney, and R. Montenegro,
      Proc. of the 14-th Annual ISAAC, 663-675 (2003) (pdf).
    2001
    • Quantum, Intramolecular Flexibility, and Polarizability Effects on the Reproduction of the Density Anomaly of Liquid Water by Simple Potential Functions,
      • M. W. Mahoney and W. L. Jorgensen,
      J. Chem. Phys., 115, 10758-10768 (2001) (pdf).
    • Rapid Estimation of Electronic Degrees of Freedom in Monte Carlo Calculations for Polarizable Models of Liquid Water,
      • M. W. Mahoney and W. L. Jorgensen,
      J. Chem. Phys., 114, 9337-9349 (2001) (pdf).
    • Diffusion Constant of the TIP5P Model of Liquid Water,
      • M. W. Mahoney and W. L. Jorgensen,
      J. Chem. Phys., 114, 363-366 (2001) (pdf).
    2000
    • A Five-Site Model for Liquid Water and the Reproduction of the Density Anomaly by Rigid, Nonpolarizable Potential Functions,
      • M. W. Mahoney and W. L. Jorgensen,
      J. Chem. Phys., 112, 8910-8922 (2000) (pdf).
    1997
    • Repression and Activation of Promoter-Bound RNA Polymerase Activity by Gal Repressor,
      • H. E. Choy, R. R. Hanger, T. Aki, M. Mahoney, K. Murakami, A. Ishihama, and S. Adhya,
      J. Mol. Biol. 272: 293-300, 1997 (pdf).
    • Discrete Representations of the Protein C-alpha Chain,
      • X. F. de la Cruz, M. W. Mahoney, and B. K. Lee,
      Fold. & Des. 2: 223-234, 1997 (pdf).

    MMDS Workshops

    I run the MMDS meetings. We started the MMDS Workshops on "Algorithms for Modern Massive Data Sets" to address algorithmic and statistical challenges in modern large-scale statistical data analysis.

    • MMDS 2016 took place on the campus of UC Berkeley on June 21-24, 2016. See the main MMDS web page for more information.

    • MMDS 2014 took place on the campus of UC Berkeley on June 17-20, 2014. See the main MMDS web page for more information. Click here for the entire video collection.

    • MMDS 2012 took place on the campus of Stanford University on July 10-13, 2012. For pdfs and videos of the presentations, go to the main MMDS web page; or click here for the entire video collection.

    • MMDS 2010 took place on the campus of Stanford University on June 15-18, 2010. MMDS 2010 addressed computation in large-scale scientific and internet data applications more generally. See the MMDS web page for details, including articles and all the speaker overheads!

    • MMDS 2008 took place on June 25-28, 2008. MMDS 2008 grew out of our expectation for what the algorithmic and statistical foundations of large-scale data analysis should look like a generation from now. Click here for an article that appeared in SIGKDD Explorations and SIAM News about the meeting.

    • MMDS 2006 took place on June 21-24, 2006. MMDS 2006 was originally motivated by the complementary perspectives brought by numerical linear algebra and theoretical computer science to matrix algorithms in large-scale data applications. Click here for an article in SIAM News about the meeting.
    These MMDS meetings generated intense interdisciplinary interest and were a big success --- so keep an eye out for future MMDSs!

    Talks and presentations

    Several recent talks:

  • Alchemist: An Apache Spark <=> MPI Interface (Jun 2018) (pdf)
  • Biomedicine & the Foundations of Data? (May 2018) (pdf)
  • Scientific Machine Learning with Alchemist (An Apache Spark <=> MPI Interface) and Beyond (Apr 2018) (pdf)
  • Numerically-intensive Machine Learning at Scale (Fall 2017) (pdf)
  • Second-order Machine Learning (Fall 2017) (pdf)

    • Several tutorial presentations:

  • RandNLA: Randomization in Numerical Linear Algebra (5.5 hr version at UW Madison Summer School, July 2018) (pdf)
  • Randomization in Numerical Linear Algebra: Theory and Practice (2.0 hr version at SIAM ALA Meeting, October 2015) (pdf)
  • Past, Present and Future of Randomized Numerical Linear Algebra: (3.0 hr version at Simons' Institute 2013 Big Data Bootcamp) (Part I: pdf, ppt and Part II: pdf, ppt)
  • Theory (and some practice) of Randomized Algorithms for Matrices and Data (tutorial from FOCS 2012 Workshop) (pdf, ppt)
  • Geometric Tools for Identifying Structure in Large Social and Information Networks (1.5 hr version at SAMSI Opening Workshop 2010, etc.) (pdf, ppt)
  • Geometric Tools for Identifying Structure in Large Social and Information Networks (2 hr version at ICASSP 2011, etc.) (pdf, ppt)
  • Geometric Tools for Identifying Structure in Large Social and Information Networks (3 hr version at ICML 2010 and KDD 2010, etc.) (pdf, ppt) (The pdf file in four pieces: here, here, here, and here.)
  • Randomized Algorithms for Matrices and Massive Data Sets (at SIAM-SDM06 2006 and VLDB 2006) (ppt)
  • Randomized Algorithms for Matrices and Massive Data Sets (at ACM-SIGKDD 2005) (ppt)

    • Several other older talks:

  • UC Berkeley's FODA Institute: Foundations of Data Analysis (NSF TRIPODS Kickoff, Oct 2017) (pdf)
  • Second-order Machine Learning (Pre-fall 2017) (pdf)
  • Local graph analytics: beyond characterizing community structure (Spring 2017) (pdf)
  • Terabyte-scale Computational Statistics (talk Fall 2016) (pdf)
  • Scientific Matrix Factorizations in Spark at Scale: Cross-platform performance, scaling, and comparisons with C+MPI (talk at 2016 Dato Data Science Summit and elsewhere) (pdf)
  • Optimization Algorithms for Analyzing Large Datasets (talk at 2016 PCMI Summer School) (pdf)
  • Foundations of Data Science (talk at NSF pre-TRIPODS workshop/meeting, Apr 2016) (pdf)
  • Sub-Sampled Newton Methods (talk at ITA 2016 and elsewhere) (pdf)
  • Challenges in Multiresolution Methods for Graph-based Learning (talk, 3of3, from NIPS15 Workshops) (pdf)
  • Using Local Spectral Methods in Theory and in Practice (talk, 2of3, from NIPS15 Workshops) (pdf)
  • Column Subset Selection on Terabyte-sized Scientific Data (talk, 1of3, from NIPS15 Workshops) (pdf)
  • Linear and Sublinear Linear Algebra Algorithms: Preconditioning Stochastic Gradient Algorithms with Randomized Linear Algebra (DIMACS, Aug 2015) (pdf)
  • Overview of RandNLA: Randomized Numerical Linear Algebra (pdf)
  • Tree-like structure in social graphs (pdf (big=28MB), ppt (big=48MB))
  • Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and network data (pdf, ppt)
  • Locally-biased and semi-supervised eigenvectors (talk from MMDS 2014) (pdf, ppt)
  • Implicit regularization in sublinear approximation algorithms (pdf, ppt)
  • BIG Biomedicine and the Foundations of BIG Data Analysis (at Big Data in Biomedicine at Stanford's Medical School, 5/23/14) (pdf, ppt)
  • Revisiting the Nystrom Method for Improved Large-Scale Machine Learning (pdf, ppt)
  • Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments (version from Simons Big Data Workshop II) (pdf)
  • Input-sparsity Time Algorithms for Embeddings and Regression Problems (talk from Simons Big Data Workshop I) (pdf)
  • Randomized Regression in Parallel and Distributed Environments (talk from GraphLab 2013) (pdf)
  • Extracting insight from large networks: implications of small-scale and large-scale structure (pdf, ppt)
  • Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments (version from MMDS 2012) (pdf)
  • Sensors, networks, and massive data (pdf, ppt)
  • Randomized Algorithms for Matrices and Data (pdf, ppt)
  • Approximate computation and implicit regularization in large-scale data analysis (PODS vsn) (pdf, ppt)
  • Approximate computation and implicit regularization in large-scale data analysis (Stats vrsn1) (pdf, ppt)
  • Approximate computation and implicit regularization in large-scale data analysis (Short vrsn) (pdf, ppt)
  • Looking for clusters in your data ... in theory and in practice (pdf, ppt)
  • Fast Approximation of Matrix Coherence and Statistical Leverage (pdf, ppt)
  • Implementing regularization implicitly via approximate eigenvector computation (pdf, ppt)
  • Linear Algebra and Machine Learning of Large Informatics Graphs (pdf, ppt)
  • Geometric Network Analysis Tools (talk from MMDS 2010) (pdf, ppt)
  • Algorithmic and Statistical Perspectives on Large-Scale Data Analysis (pdf, ppt)
  • Community structure in large social and information networks (newer) (pdf, ppt)
  • Statistical leverage and improved matrix algorithms (newer and long) (pdf, ppt)
  • Approximation Algorithms as Experimental Probes of Informatics Graphs (pdf, ppt)
  • Community structure in large social and information networks (talk from MMDS 2008) (pdf, ppt)
  • Community structure in large social and information networks (older) (pdf, ppt)
  • Statistical leverage and improved matrix algorithms (older and short) (pdf, ppt)
  • Sampling algorithms and core-sets for Lp regression and applications (pdf, ppt)
  • CUR Matrix Decompositions for Improved Data Analysis (talk from MMDS 2006) (pdf, ppt)
  • A Relative-Error CUR Decomposition for Matrices and Its Data Applications (pdf, ppt)
  • Sampling Algorithms for L2 Regression and Applications (talk from SODA 2006) (pdf, ppt)
  • Approximating a Gram Matrix for Improved Kernel-Based Learning (talk from COLT 2005) (ps, pdf)
  • Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis (newer) (pdf, ppt)
  • Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis (older) (pdf)
  • CUR Matrix Decomposition with Applications to Algorithm Design and Massive Data Set Analysis (pdf)
  • Fast Monte Carlo Algorithms for Massive Data Sets and Approximating Max-Cut (ps, pdf)

    • The TIP5P Water talk:

  • The Computational Statistical Mechanics of Simple Models of Liquid Water (pdf)

    • Videos of several talks:

  • "Biomedicine & the Foundations of Data?"
    (at the BD2K Guide to the Fundamentals of Data Science seminar series).
  • "Matrix factorizations at scale: a comparison of scientific data analytics on Spark and MPI"
    (at the 2016 Dato Data Science Summit).
  • "Challenges in Multiresolution Methods for Graph-based Learning"
    (at the NIPS Workshops, December 2015).
  • "Using Local Spectral Methods in Theory and in Practice"
    (at the NIPS Workshops, December 2015).
  • "Linear and Sublinear Linear Algebra Algorithms: Preconditioning Stochastic Gradient Algorithms with Randomized Linear Algebra"
    (at DIMACS, August 2015).
  • "Eigenvector localization and implicit regularization for large-scale graphs and networked data"
    (at CIMAT, March 2015).
  • "Locally-biased and semi-supervised eigenvectors"
    (at MMDS 2014, June 2014).
  • "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"
    (at the SF Bay Area DM-SIG Meeting, June 2, 2014).
  • "BIG Biomedicine and the Foundations of BIG Data Analysis"
    (at the Big Data in BioMedicine Conference at Stanford University, May 2014).
  • "Randomized Matrix Algorithms and Large-scale Scientific Data Analysis"
    (at the 2014 ASE Conference, May 31, 2014).
  • "Eigenvector localization, implicit regularization, and algorithmic anti-differentiation for large-scale graphs and networked data" (or try also here for another version)
    (at ICERM and IMA, May and April 2014, respectively).
  • "Randomized matrix algorithms and large-scale scientific data analysis"
    (at the University of Michigan, April 2014).
  • "Recent Results in Randomized Numerical Linear Algebra" (or click here)
    (at the NIPS 2013 Workshops, December 2013).
  • "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"
    (at the Simons Big Data Workshop II, October 2013).
  • "Input-sparsity Time Algorithms for Embeddings and Regression Problems"
    (at the Simons Big Data Workshop I, September 2013).
  • "Past, Present and Future of Randomized Numerical Linear Algebra: Part I (PD) and Part II" (MM)
    (at the Simons Big Data Bootcamp, September 2013).
  • "Randomized Regression in Parallel and Distributed Environments"
    (at the GraphLab Workshop, July 2013).
  • "Sensors, networks, and massive data" or click here
    (at Kavli FoS, November 2012).
  • "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"
    (at MMDS 2012, July 2012).
  • "Extracting insight from large networks: implications of small-scale and large-scale structure"
    (at the University of Marlyand, April 2012).
  • "Fast Approximation of Matrix Coherence and Statistical Leverage"
    (at the NIPS Workshops, December 2011).
  • "Approximate computation and implicit regularization in large-scale data analysis" or click here
    (at the Workshop on Beyond Worst-Case Analysis, September 2011).
  • "Linear Algebra and Machine Learning of Large Informatics Graphs"
    (at the NIPS Workshops, December 2010).
  • "Geometric Tools for Identifying Structure in Large Social and Information Networks"
    (90 minute version, tutorial at SAMSI Opening workshop, August 2010).
  • "Geometric Tools for Graph Mining of Large Social and Information Networks"
    (3 hour version, tutorial at KDD 2010, July 2010).
  • "Community Structure in Large Social and Information Networks"
    (at the Newton Institute in Cambridge, June 2010).
  • "Algorithmic and Statistical Perspectives on Large-Scale Data Analysis"
    (at the SF Bay Area DM-SIG Meeting, February 2010).
  • "Community Structure in Large Social and Information Networks," in avi or mpg (note: slow to download),
    (at the 2009 IIT Kanpur Processing Massive Data Sets Workshop, December 2009).
  • "Statistical Leverage and Improved Matrix Algorithms"
    (at the 2009 ICML Workshops, June 2009).
  • "Sampling Algorithms and Coresets for Lp Regression and Applications," in mpg (note: slow to download),
    (at the 2006 IIT Kanpur Data Streams Workshop, December 2006).
  • "CUR Matrix Decompositions for Improved Data Analysis"
    (at Johns Hopkins University, March 2006).
  • "Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis"
    (at the 2005 IPAM Summer School, July 2005).

    • My old web page at Yale has additional (somewhat outdated) information; and my less old web page at Stanford has additional (somewhat less outdated) information; but you should click on them (or Google search for them) if you are interested in extremely large homes.