MMDS 2010 Wrap up: The workshop concluded on June 18th. We kindly thank all
participants
for attending.
The Workshops on Algorithms for Modern Massive Data Sets (MMDS) will address algorithmic and statistical challenges in modern large-scale data analysis. The goals of this series of workshops are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets; and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote the cross-fertilization of ideas.
MMDS 2010 Wrap up: The workshop concluded on June 18th. We kindly thank all
participants
for attending.
MMDS 2010 will start at 9:45 AM on Tuesday, June 15. For those attendees who are jet-lagged or are early risers, breakfast and registration will be available starting at 8:00 AM just outside of Cubberley Auditorium!The preliminary schedule for MMDS 2010 is available below! All talks will be held in Cubberley Auditorium in the School of Education Building. You can find the directions to the venue here!.
There is a limited amount of funding available to help with travel and lodging reimbursements, in particular for junior researchers and presenters. Please contact the organizers as soon as possible if you are interested.
MMDS 2010 is now accepting registration from corporate, government, academic and student participants. The event dates are June 15-18th. Please register by June 10 to ensure participation.You can find information about lodging and getting around Stanford here!. More detailed information regarding the event location and schedule will soon be posted.
The workshop will host a poster session which is open to all registrants. The registration fee will be waived for student poster presenters.Please email a title and abstract to mmds-organizers@math.stanford.edu if you are interested.
MMDS 2010. Workshop on Algorithms for Modern Massive Data Sets.
Sponsored by Stanford University and the MMDS Foundation.
To take place on the campus of Stanford University.
June 15–18, 2010.
Organizing Committee: Michael Mahoney (chair), Alex Shkolnik, Petros Drineas, Lek-Heng Lim, Gunnar Carlsson
More details, including the registration web page and information about the poster session, will be available here soon!
| Time | Talk |
|---|---|
| 8:00 - 10:00 | Breakfast and Registration -- outside Cubberley Auditorium (at the Stanford School of Education, just off the Main Quad) |
| 9:45 - 10:00 | Welcome and Opening Remarks -- in Cubberley Auditorium |
| 10:00 - 11:00 | Tutorial: Peter Norvig Internet-Scale Data Analysis |
| 11:00 - 11:30 | Ashok Srivastava Virtual Sensors and Large-Scale Gaussian Processes |
| 11:30 - 12:00 | John Langford A Method for Parallel Online Learning |
| 2:00 - 3:00 | Tutorial: John Gilbert Combinatorial Scientific Computing: Experience and Challenges |
| 3:00 - 3:30 | Deepak Agarwal Recommender Probems for Content Optimization |
| 3:30 - 4:00 | James Demmel Minimizing Communication in Linear Algebra |
| 4:30 - 5:00 | Dmitri Krioukov Hyperbolic Mapping of Complex Networks |
| 5:00 - 5:30 | Mehryar Mohri Matrix Approximation for Large-Scale Learning |
| 5:30 - 6:00 | David Bader Massive-Scale Analytics of Streaming Social Networks |
| 6:00 - 6:30 | Ely Porat Fast Pseudo-Random Fingerprints |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Peter Bickel Statistical Inference for Networks |
| 10:00 - 10:30 | Jure Leskovec Inferring Networks of Diffusion and Influence |
| 11:00 - 11:30 | Michael W. Mahoney Geometric Network Analysis Tools |
| 11:30 - 12:00 | Edward Chang AdHEat - A New Influence-based Social Ads Model and its Tera-Scale Algorithms |
| 12:00 - 12:30 | Mauro Maggioni Intrinsic Dimensionality Estimation and Multiscale Geometry of Data Sets |
| 2:30 - 3:00 | Guillermo Sapiro Collaborative Hierarchical Sparse Models |
| 3:00 - 3:30 | Alekh Agarwal and Peter Bartlett Information-theoretic Lower Bounds on the Oracle Complexity of Convex Optimization |
| 3:30 - 4:00 | John Duchi and Yoram Singer Composite Objective Optimization and Learning for Massive Datasets |
| 4:30 - 5:00 | Steven Hillion MAD Analytics in Practice |
| 5:00 - 5:30 | Matthew Harding Outlier Detection in Financial Trading Networks |
| 5:30 - 6:00 | Neel Sundrahan Large Dataset Problems at the Long Tail |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Sebastiano Vigna Spectral Ranking |
| 10:00 - 10:30 | Robert Stine Streaming Feature Selection |
| 11:00 - 11:30 | Konstantin Mischaikow A Combinatorial Framework for Nonlinear Dynamics |
| 11:30 - 12:00 | Alfred Hero Sparse Correlation Screening in High Dimension |
| 12:00 - 12:30 | Susan Holmes Heterogeneous Data Challenge Combining Complex Data |
| 2:30 - 3:30 | Tutorial: Piotr Indyk Sparse Recovery Using Sparse Matrices |
| 3:30 - 4:00 | Sayan Mukherjee Efficient Dimension Reduction on Massive Data |
| 4:30 - 5:00 | Padhraic Smyth Statistical Modeling of Large-Scale Sensor Count Data |
| 5:00 - 5:30 | Ping Li Compressed Counting and Application in Estimating Entropy of Data Steams |
| 5:30 - 6:00 | Edo Liberty Scaleable Correlation Clustering Algorithms |
| Time | Talk |
|---|---|
| 9:00 - 10:00 | Tutorial: Petros Drineas Randomized Algorithms in Linear Algebra and Large Data Applications |
| 10:00 - 10:30 | Gunnar Martinsson Randomized methods for Computing the SVD/PCA of Very Large Matrices |
| 11:00 - 11:30 | Ilse Ipsen Numerical Reliability of Randomized Algorithms |
| 11:30 - 12:00 | Philippe Rigollet Optimal Rates of Sparse Esimation and Universal Aggregation |
| 12:00 - 12:30 | Alexandre d'Aspremont Subsampling, Spectral Methods & Semidefinite Programming |
| 2:30 - 3:00 | Gary Miller Specialized System Solvers for very large Systems: Theory and Practice |
| 3:00 - 3:30 | John Wright and Emmanuel Candes Robust Principal Component Analysis? |
| 3:30 - 4:00 | Alon Orlitsky Estimation, Prediction, and Classification over Large Alphabets |
| 4:30 - 5:00 | Ken Clarkson Numerical Linear Algebra in the Streaming Model |
| 5:00 - 5:30 | David Woodruff Fast Lp Regression in Data Streams |
| Alekh Agarwal | University of California, Berkeley |
| Deepak Agarwal | Yahoo! Research |
| Alexandre d'Aspremont | Princeton University |
| David Bader | Georgia Tech College of Computing |
| Peter Bickel | University of California, Berkeley |
| Emmanuel Candes | Stanford University |
| Edward Chang | Google Research |
| Ken Clarkson | IBM Almaden Research Center |
| Jim Demmel | University of California, Berkeley |
| John Duchi | University of California, Berkeley |
| John Gilbert | University of California, Santa Barbara |
| Matthew Harding | Stanford University |
| Alfred Hero | University of Michigan, Ann Arbor |
| Steven Hillion | Greenplum |
| Susan Holmes | Stanford University |
| Peter Indyk | Massachusetts Institute of Technology |
| Ilse Ipsen | North Carolina State University |
| Dmitri Krioukov | Cooperative Association for Internet Data Analysis |
| John Langford | Yahoo! Research |
| Jure Leskovec | Stanford University |
| Ping Li | Cornell University |
| Edo Liberty | Yahoo! Research |
| Mauro Maggioni | Duke University |
| Gunnar Martinsson | University of Colorado, Boulder |
| Gary Miller | Carnegie Mellon University |
| Konstantin Mischaikow | Rutgers University |
| Mehryar Mohri | New York University |
| Sayan Mukherjee | Duke University |
| Peter Norvig | Google Research |
| Alon Orlitsky | University of California, San Diego |
| Ely Porat | Bar-Ilan University |
| Guillermo Sapiro | University of Minnesota |
| Padhraic Smyth | University of California, Irvine |
| Ashok Srivastava | National Aeronautics and Space Administration |
| Neel Sundaresan | eBay Research |
| Robert Stine | University of Pennsylvania |
| Sebastiano Vigna | Università Degli Studi Di Milano |
| Patrick Wolfe | Harvard University |
| David Woodruff | IBM Almaden Research Center |
| John Wright | Microsoft Research Asia |
| Peter Bartlett | University of California, Berkeley |
| Robert Calderbank | Princeton University |
| Fan Chung | University of California, San Diego |
| Yoram Singer | Google Research |
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MMDS 2008. Workshop on Algorithms for Modern Massive Data Sets, Stanford, CA, June 25–28, 2008.
MMDS 2006. Workshop on Algorithms for Modern Massive Data Sets, Stanford, CA, June 21–24, 2006.