STREAMING ALGORITHMS
August 10- 13, 2015, Aarhus University, Denmark
madalgo.au.dk/events/summer-school-2015/
REGISTRATION IS NOW OPEN AT:
http://madalgo.au.dk/news/nyhed/artikel/madalgo-summer-school-2015-registration-is-open/
OVERVIEW AND GOAL
The MADALGO Summer School 2015 will introduce attendees to the latest
developments in streaming algorithms. The topics will include e.g. high dimensional
searching problems, sketching, dimensionality reduction and recent developments
on fast fourier transform.
LECTURES
The school will be taught by experts in streaming:
* Alexandr Andoni (Simons Institute, Berkeley)
* Michael Kapralov (IBM T. J. Watson)
* Jelani Nelson (Harvard)
* David Woodruff (IBM Almaden)
PARTICIPATION
The summer school will take place on August 10-13, 2015 at Center for
Massive Data Algorithmics (MADALGO) at the Department of Computer
Science, Aarhus University, Denmark. The school is targeted at graduate
students, as well as researchers interested in an in-depth introduction
to streaming algorithms. Registration is free on a first-come-first
serve basis - handouts, coffee breaks, lunches and a dinner will be
provided by MADALGO and Aarhus University.
ORGANIZING COMMITTEE
* Kasper Green Larsen (MADALGO, Aarhus University) (Main Organizer)
* Peyman Afshani (MADALGO, Aarhus University)
* Lars Arge (MADALGO, Aarhus University)
* Gerth S. Brodal (MADALGO, Aarhus University)
LOCAL ARRANGEMENTS
* Trine Ji Holmgaard (MADALGO, Aarhus University)
* Katrine Østergaard Rasmussen (MADALGO, Aarhus University)
ABOUT MADALGO
Center for Massive Data Algorithmics is a major basic research center
funded by the Danish National Research Foundation. The center is located
at the Department of Computer Science, Aarhus University, Denmark, but
also includes researchers at CSAIL, Massachusetts Institute of
Technology in the US, and at the Max Planck Institute for Informatics
and at Frankfurt University in Germany. The center covers all areas of
the design, analysis and implementation of algorithms and data
structures for processing massive data (interpreted broadly to cover
computations where data is large compared to the computational
resources), but with a main focus on I/O-efficient, cache-oblivious and
data stream algorithms.
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