[DMANET] MADALGO Summer School on Learning at Scale

MADALGO Summer School on

LEARNING AT SCALE

August 11- 14, 2014, Aarhus University, Denmark

madalgo.au.dk/learningatscale2014


OVERVIEW AND GOAL
The MADALGO Summer School 2014 will introduce attendees to the latest
developments in learning at scale. The topics will include high
dimensional inference, algorithmic perspectives on learning and
optimization, and challenges in learning with huge data.

LECTURES
The school will be taught by experts in learning:
* Amr Ahmed (Google)
* Mikhail Belkin (Ohio State)
* Stefanie Jegelka (Berkeley)
* Ankur Moitra (MIT)

PARTICIPATION
The summer school will take place on August 11-14, 2014 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 Learning. Registration will open soon at the school webpage.
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
* Suresh Venkatasubramanian (University of Utah)
* Peyman Afshani (MADALGO, Aarhus University)
* Lars Arge (MADALGO, Aarhus University)
* Gerth S. Brodal (MADALGO, Aarhus University)
* Kasper Green Larsen (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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