I am not at Stanford any more, so this web page is somewhat out of date.
I am currently at ICSI and the Department of Statistics at UC Berkeley.
Click here
for my new web page and for more information.
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.)
Michael Mahoney's old home page

Basic info
I am in the math department at Stanford University.
You can reach me via electronic mail:
mmahoney
is the username, and
cs dot stanford 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Also, some other (somewhat outdated) information can be found at my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 Yale computer science.

Recent items of interest:
The scribed lecture notes for
Stat260/CS294: Randomized Algorithms for Matrices and Data
have been posted.
Feedback welcome.
We will be running MMDS again!
MMDS 2014
will take place
on the campus of UC Berkeley on Tuesday, June 17 through Friday, June 20, 2014.
See the
main MMDS web page
for more information on registration, speakers, etc., all of which will be available soon.
I'm moving to ICSI and the Department of Statistics at UC Berkeley.
Teaching,
Fall 2013, at UC Berkeley:
Stat260/CS294: Randomized Algorithms for Matrices and Data.
A cleanedup version of the lecture notes should be available soon.
Our NRC report on the "Frontiers in Massive Data Analysis" is out and
available here.
I'm involved with running the program on "Theoretical Foundations of Big
Data Analysis," to be held at the Simons Institute at UC Berkeley during the
fall of 2013.
Click
here
for details; in particular, note the link at the bottom of the page to
information about fellowships for young researchers to participate.
We ran MMDS again.
MMDS 2012
took place on the campus of Stanford University on July 1013, 2012.
Speaker videos and presentations are now available.
For pdfs and videos of the speaker presentations, go to the main MMDS web
page; or click
here for the entire
video collection.
The overview article on
"Approximate Computation and Implicit Regularization for Very Largescale Data Analysis"
associated with the invited talk at PODS 2012 meeting is on the arXiv
here.
LSRN:
An initial version of our code for LSRN, the randomized leastsquares solver
for parallel environments, is available
here.
The monograph on "Randomized Algorithms for Matrices and Data" is available
in NOW's "Foundations and Trends in Machine Learning" series
here,
and it is also
available on the arXiv here.


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.)
The overview chapter on
"Algorithmic and Statistical Perspectives on LargeScale Data Analysis" is
finally on the arXiv
here; the book in which it will
appear is in press; and a video of the associated talk is
here.
Recent teaching:
Fall 2009:
CS369M: Algorithms for Massive Data Set Analysis
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;
older networks are still
here.
Our PNAS paper on "CUR Matrix Decompositions for Improved Data Analysis"
has appeared as
Proc. Natl. Acad. Sci. USA, 106, 697702 (2009).
Please email me if you can't access a copy from the PNAS web site.
Research interests

Algorithmic and statistical aspects of modern largescale data analysis.

Design, analysis, and implementation of randomized algorithms for very large
matrix, graph, and regression problems.

Implicit regularization and implicit optimization
methods in scalable approximation algorithms.

Applications to analytics and vector space analytics on large social and information networks.

Applications to DNA microarray, single nucleotide polymorphism, astronomical, and
medical imaging data.
Much of my current research focuses on
Randomized Numerical Linear Algebra, i.e., using random sampling and
random projection methods to solve very large matrixbased 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 largescale 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
largescale 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 additiveerror and relativeerror 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 proteinnucleic acid interactions.
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 largescale statistical data analysis.

We will be running MMDS again!
MMDS 2014
will take place
on the campus of UC Berkeley on June 1720, 2014.
See the
main MMDS web page
for more information on registration, speakers, etc., all of which will be available soon.

MMDS 2012
took place on the campus of Stanford University on July 1013, 2012.
For pdfs and videos of the speaker 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 1518, 2010.
MMDS 2010 addressed computation in largescale 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 2528, 2008.
MMDS 2008 grew out of our expectation for what
the algorithmic and statistical
foundations of
largescale
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 2124, 2006.
MMDS 2006 was originally motivated by the complementary perspectives
brought by numerical linear algebra and theoretical computer science to
matrix algorithms in largescale 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!
Publications
2013

Treelike Structure in Large Social and Information Networks,

A. B. Adcock, B. D. Sullivan, and M. W. Mahoney,

Proc. of the 2013 IEEE ICDM, (2013).

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),

Accepted for publication, Astronomical Journal.

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 9th IWOMP, 7183 (2013).

Frontiers in Massive Data Analysis,

Committee on the Analysis of Massive Data, et al. (M. I. Jordan, et al.),

The National Academies Press (2013)
(web).

A Statistical Perspective on Algorithmic Leveraging,

P. Ma, M. W. Mahoney, and B. Yu,

Technical Report, Preprint: arXiv:1306.5362 (2013)
(arXiv),

To appear in: Proc. of the 31st ICML Conference (2014),

Journal version submitted for publication.

Robust Regression on MapReduce,

X. Meng, and M. W. Mahoney,

Proc. of the 30th ICML Conference (2013)
(pdf).

Quantile Regression for Largescale Applications,

J. Yang, X. Meng, and M. W. Mahoney,

Technical Report, Preprint: arXiv:1305.0087 (2013)
(arXiv),
(code),

Proc. of the 30th ICML Conference (2013)
(pdf),

Accepted for publication, SIAM J. Scientific Computing.

Revisiting the Nystrom Method for Improved LargeScale Machine Learning,

A. Gittens and M. W. Mahoney,

Technical Report, Preprint: arXiv:1303.1849 (2013)
(arXiv),
(code),

Proc. of the 30th ICML Conference (2013)
(pdf),

Journal version submitted for publication.

Semisupervised Eigenvectors for Largescale Locallybiased Learning,

T. J. Hansen and M. W. Mahoney,

Proc. of the 2012 NIPS Conference
(pdf),
(code),

Technical Report, Preprint: arXiv:1304.7528 (2013)
(arXiv),

Accepted for publication, J. Machine Learning Research.
2012

Lowdistortion Subspace Embeddings in Inputsparsity Time and Applications to Robust Linear Regression,

X. Meng and M. W. Mahoney,

Technical Report, Preprint: arXiv:1210.3135 (2012)
(arXiv),

Proc. of the 45th STOC, 91100 (2013).

The Fast Cauchy Transform and Faster Robust Linear Regression,

K. L. Clarkson, P. Drineas, M. MagdonIsmail, M. W. Mahoney, X. Meng, and D. P. Woodruff,

Technical Report, Preprint: arXiv:1207.4684 (2012)
(arXiv),

Proc. of the 24th Annual SODA, 466477 (2013)
(pdf),

Journal version submitted for publication.

rCUR: an R package for CUR matrix decomposition,

A. Bodor, I. Csabai, M. W. Mahoney, and N. Solymosi,

BMC Bioinformatics, 13:103 (2012)
(pdf).

Approximate Computation and Implicit Regularization for Very Largescale Data Analysis,

M. W. Mahoney,

Technical Report, Preprint: arXiv:1203.0786 (2012)
(arXiv),

Proc. of the 2012 ACM Symposium on Principles of Database Systems, 143154, 2012
(pdf).

On the Hyperbolicity of SmallWorld and TreeLike Random Graphs,

W. Chen, W. Fang, G. Hu, and M. W. Mahoney,

Technical Report, Preprint: arXiv:1201.1717 (2012)
(arXiv),

Proc. of the 23rd ISAAC 278288 (2012)
(pdf),

Internet Mathematics, 9(4), 434491 (2013).
2011

Randomized Dimensionality Reduction for Kmeans Clustering,

C. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas,

Technical Report, Preprint: arXiv:1110.2897 (2011)
(arXiv),

Journal version submitted for publication.

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 UnderDetermined Systems,

X. Meng, M. A. Saunders, and M. W. Mahoney,

Technical Report, Preprint: arXiv:1109.5981 (2011)
(arXiv),
(code),

Accepted for publication, SIAM J. Scientific Computing.

Fast approximation of matrix coherence and statistical leverage,

P. Drineas, M. MagdonIsmail, 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, 34753506 (2012)
(pdf).

Localization on loworder eigenvectors of data matrices,

M. Cucuringu and M. W. Mahoney,

Technical Report, Preprint: arXiv:1109.1355 (2011)
(arXiv).

Efficient Genomewide Selection of PCACorrelated tSNPs for Genotype Imputation,

A. Javed, P. Drineas, M. W. Mahoney, and P. Paschou,

Annals of Human Genetics, 75, 707722 (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. 647672,
2012.)
2010

Computation in LargeScale 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,
ASASCGN 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 LargeScale Data Analysis,

M. W. Mahoney,

Technical Report, Preprint: arXiv:1010.1609 (2010)
(arXiv),

In:
Combinatorial Scientific Computing,
pp. 427469,
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, 121128 (2011)
(pdf).

Approximating HigherOrder Distances Using Random Projections,

P. Li, M. W. Mahoney, and Y. She,

Proc. of the 26th UAI Conference, 312321 (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 19th International WWW, 631640 (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, 23392365 (2012)
(ps,
pdf).

Unsupervised Feature Selection for the kmeans Clustering Problem,

C. Boutsidis, M. W. Mahoney, and P. Drineas,

Proc. of the 2009 NIPS Conference
(ps,
pdf).

Learning with Spectral Kernels and HeavyTailed 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 8th International SEA, 197208 (2009)
(ps,
pdf).

CUR Matrix Decompositions for Improved Data Analysis,

M. W. Mahoney and P. Drineas,

Proc. Natl. Acad. Sci. USA, 106, 697702 (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 20th Annual SODA, 968977 (2009)
(ps,
pdf).

Algorithmic and Statistical Challenges in Modern LargeScale 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
ASASCGN 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 WellDefined Clusters,

J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney,

Technical Report, Preprint: arXiv:0810.1355 (2008)
(arXiv),

Internet Mathematics, 6(1), 29123 (2009).

Unsupervised Feature Selection for Principal Components Analysis,

C. Boutsidis, M. W. Mahoney, and P. Drineas,

Proc. of the 14th Annual SIGKDD, 6169 (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 17th International WWW, 695704 (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, 219249 (2011).

PCACorrelated SNPs for Structure Identification in Worldwide Human Populations,

P. Paschou, E. Ziv, E. G. Burchard, S. Choudhry, W. RodriguezCintron, M. W. Mahoney, and P. Drineas,

PLoS Genetics, 3, 16721686 (2007)
(ps,
pdf).

RelativeError 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, 844881 (2008)
(ps,
pdf).

Feature Selection Methods for Text Classification,

A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. W. Mahoney,

Proc. of the 13th Annual SIGKDD, 230239 (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 19th Annual SODA, 932941 (2008)
(ps,
pdf),

SIAM J. Computing, 38, 20602078 (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), 96107 (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 32nd Annual VLDB, 1269 (2006)
(ps,
pdf).

Subspace Sampling and RelativeError Matrix Approximation: ColumnRowBased Methods,

P. Drineas, M. W. Mahoney, and S. Muthukrishnan,

Proc. of the 14th Annual ESA, 304314 (2006)
(ps,
pdf).

Subspace Sampling and RelativeError Matrix Approximation: ColumnBased Methods,

P. Drineas, M. W. Mahoney, and S. Muthukrishnan,

Proc. of the 10th Annual RANDOM, 316326 (2006)
(ps,
pdf).

TensorCUR Decompositions For TensorBased Data,

M. W. Mahoney, M. Maggioni, and P. Drineas,

Proc. of the 12th Annual SIGKDD, 327336 (2006)
(ps,
pdf),

SIAM J. Matrix Analysis and Applications, 30, 957987 (2008)
(ps,
pdf).

Polynomial Time Algorithm for ColumnRowBased RelativeError LowRank Matrix Approximation,

P. Drineas, M. W. Mahoney, and S. Muthukrishnan,

Technical Report, DIMACS TR 200604 March 2006
(ps,
pdf).

Sampling Algorithms for L2 Regression and Applications,

P. Drineas, M. W. Mahoney, and S. Muthukrishnan,

Proc. of the 17th Annual SODA, 11271136 (2006)
(ps,
pdf).
2005

A Randomized Algorithm for a TensorBased Generalization of the Singular Value Decomposition,

P. Drineas and M. W. Mahoney,

Technical Report, YALEU/DCS/TR1327, June 2005
(ps,
pdf),

Linear Algebra and its Applications, 420, 553571 (2007)
(ps,
pdf).

On the Nystrom Method for Approximating a Gram Matrix for Improved KernelBased Learning,

P. Drineas and M. W. Mahoney,

Technical Report, YALEU/DCS/TR1319, April 2005
(ps,
pdf),

Proc. of the 18th Annual COLT, 323337 (2005)
(ps,
pdf),

J. Machine Learning Research, 6, 21532175 (2005)
(ps,
pdf).
2004

Sampling Subproblems of Heterogeneous MaxCut Problems and Approximation Algorithms,

P. Drineas, R. Kannan, and M. W. Mahoney,

Technical Report, YALEU/DCS/TR1283, April 2004
(ps,
pdf),

Proc. of the 22nd Annual STACS, 5768 (2005)
(ps,
pdf),

Random Structures and Algorithms, 32:3, 307333 (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/TR1271, February 2004
(ps,
pdf),

SIAM J. Computing, 36, 184206 (2006)
(ps,
pdf).

Fast Monte Carlo Algorithms for Matrices II: Computing LowRank Approximations to a Matrix,

P. Drineas, R. Kannan, and M. W. Mahoney,

Technical Report, YALEU/DCS/TR1270, February 2004
(ps,
pdf),

SIAM J. Computing, 36, 158183 (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/TR1269, February 2004
(ps,
pdf),

SIAM J. Computing, 36, 132157 (2006)
(ps,
pdf).
2003

Rapid Mixing of Several Markov Chains for a HardCore Model,

R. Kannan, M. W. Mahoney, and R. Montenegro,

Proc. of the 14th Annual ISAAC, 663675 (2003)
(ps,
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, 1075810768 (2001)
(ps,
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, 93379349 (2001)
(ps,
pdf).

Diffusion Constant of the TIP5P Model of Liquid Water,

M. W. Mahoney and W. L. Jorgensen,

J. Chem. Phys., 114, 363366 (2001)
(ps,
pdf).
2000

A FiveSite 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, 89108922 (2000)
(ps,
pdf).
1997

Repression and Activation of PromoterBound 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: 293300, 1997
(ps,
pdf).

Discrete Representations of the Protein Calpha Chain,

X. F. de la Cruz, M. W. Mahoney, and B. K. Lee,

Fold. & Des. 2: 223234, 1997
(ps,
pdf).
Talks and presentations
Several recent talks:
Revisiting the Nystrom Method for Improved LargeScale Machine Learning
(pdf,
ppt)
Randomized Regression in Parallel and Distributed Environments
(talk from GraphLab 2013)
(pdf)
Theory (and some practice) of Randomized Algorithms for Matrices and Data
(talk from FOCS 2012 Workshop)
(pdf,
ppt)
Extracting insight from large networks: implications of smallscale and largescale structure
(pdf,
ppt)
Several tutorial presentations:
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 SIAMSDM06 2006 and VLDB 2006)
(ppt)
Randomized Algorithms for Matrices and Massive Data Sets
(at ACMSIGKDD 2005)
(ppt)
Several other older talks:
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 largescale data analysis
(PODS vsn)
(pdf,
ppt)
Approximate computation and implicit regularization in largescale data analysis
(Stats vrsn1)
(pdf,
ppt)
Approximate computation and implicit regularization in largescale 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 LargeScale 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 coresets for Lp regression and applications
(pdf,
ppt)
CUR Matrix Decompositions for Improved Data Analysis (talk from MMDS 2006)
(pdf,
ppt)
A RelativeError 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 KernelBased 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 MaxCut
(ps,
pdf)
The TIP5P Water talk:
The Computational Statistical Mechanics of Simple Models of Liquid Water
(
pdf)
Videos of several talks:
"Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments"
(at the Simons Big Data Workshop II, October 2013).
"Inputsparsity 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"
(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 smallscale and largescale 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 largescale data analysis,"
or click
here
(at the Workshop on Beyond WorstCase 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 LargeScale Data Analysis"
(at the SF Bay Area DMSIG 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 computer science has additional (somewhat outdated) information;
but you should click on it (or Google search for it) if you are interested in
extremely large homes.
