Gene Golub SIAM Summer School 2015 RandNLA: Randomization in Numerical Linear Algebra Delphi, Greece (June 15-26, 2015)

 

 

 

 

  Michael Mahoney just sent me the following:

 

Hi Igor,

Hope all is going well with you. 

In case you haven't seen it, I wanted to bring to your attention a summer school this summer on Randomized Numerical Linear Algebra, the 2015 Gene Golub SIAM Summer School, that I am co-organizing with Petros Drineas, Ilse Ipsen, and Stratis Gallopoulos.  It's a great chance to learn about the area, and we are able to offer full support to undergraduate and graduate students to attend this two-week school.  Plus, it will be held in Delphi, Greece!  Dates are from June 15 to June 26, 2015.

The web site of the summer school is

http://www.cs.rpi.edu//~drinep/G2S3_RandNLA_2015/

The application deadline is February 1st, 2015.

I'm not sure what is your policy about posting things like this on your blog, but I'm sure that it would be of interest to your followers, and so if it is acceptable, we would certainly appreciate your mentioning it on the blog.  Of course, let me know if you have any questions.

With best regards,

Michael

Thanks Michael

 

 

 

From the page:

 

 

Gene Golub SIAM Summer School 2015

RandNLA: Randomization in
Numerical Linear Algebra

Delphi, Greece (June 15-26, 2015)

The goal of RandNLA is to design novel algorithms for numerical linear algebra problems by using randomization, e.g., random sampling and random projections. It is a topic that has received a great deal of interdisciplinary interest in recent years, with contributions coming from numerical linear algebra, theoretical computer science, scientific computing, statistics, optimization, data analysis, and machine learning, as well as application areas such as genetics, physics, astronomy, and internet modeling.
The summer school is designed to bring graduate students up to date on the state of the art in the theory, numerical aspects, and data analysis applications of RandNLA. Since RandNLA is quite interdisciplinary, students will be selected from a wide range of backgrounds. Thus the courses are designed to provide students with an overview of RandNLA and also an understanding of the complementary strengths and weaknesses that different traditional research areas bring to this new research area.
The sixth Gene Golub SIAM Summer School, hosted by the University of Patras, Greece, will take place at the European Cultural Centre of Delphi (ECCD), located in Delphi, Greece.

The Summer school is being organized by

• Petros Drineas, Rensselaer Polytechnic Institute, USA
• Efstratios Gallopoulos, University of Patras, Greece
• Ilse Ipsen, North Carolina State University, USA
• Michael W. Mahoney, University of California at Berkeley, USA

Application procedure (Deadline: February 1, 2015)

The program features:

The summer school will bring graduate students up to date on the state-of-the-art of RandNLA, through lectures, interactive tutorials, and hands-on projects. Since RandNLA is quite interdisciplinary, students are likely to come from a variety of different backgrounds within computer science and mathematics. Topics to be covered include:

• Introduction and interdisciplinary overview of RandNLA
• Random variables, expectation, moments, concentration, coupon collector's problem, Chernoff and Hoeffding bounds
• Matrix valued random variables, matrix concentration bounds, coherence
• Monte Carlo methods, different sampling methods (with and without replacement, Bernoulli), importance sampling
• Matrix multiplication, SVD, low rank approximations, leverage scores
• RandNLA in theoretical computer science
• Models of data access, pass efficiency
• Lower bounds
• Input-sparsity time embeddings
• Graph sparsification
• RandNLA in numerical linear algebra
• Computational implementation of sampling methods
• Conditioning of sampled matrices
• Randomized pre-conditioners for least squares
• Sensitivity and numerically stable computation of leverage scores
• Rank-revealing decompositions, subset selection
• CUR and Nystrom methods
• Applications of RandNLA
• Large-scale machine learning, geometric data analysis
• Parameter estimation, model reduction, discrete empirical interpolation
• Genomics
• Astronomy

 

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