Upcoming Conference: “Machine-Learning with Real-time & Streaming Applications”


From Data to Knowledge: Machine-Learning with Real-time & Streaming Applications
May 7-11 2012
On the Campus of the University of California, Berkeley



Olfa Nasraoui (Louisville), Petros Drineas (RPI), Muthu Muthukrishnan (Rutgers),
Alex Szalay (John Hopkins), David Bader (Georgia Tech),
Eamonn Keogh (UC Riverside), Joao Gama (Univ. of Porto, Portugal),
Michael Franklin (UC Berkeley), Ziv Bar-Joseph (Carnegie Mellon University)


We are experiencing a revolution in the capacity to quickly collect
and transport large amounts of data. Not only has this revolution
changed the means by which we store and access this data, but has also
caused a fundamental transformation in the methods and algorithms that
we use to extract knowledge from data. In scientific fields as diverse
as climatology, medical science, astrophysics, particle physics,
computer vision, and computational finance, massive streaming data
sets have sparked innovation in methodologies for knowledge discovery
in data streams. Cutting-edge methodology for streaming data has come
from a number of diverse directions, from on-line learning, randomized
linear algebra and approximate methods, to distributed optimization
methodology for cloud computing, to multi-class classification
problems in the presence of noisy and spurious data.

This conference will bring together researchers from applied
mathematics and several diverse scientific fields to discuss the
current state of the art and open research questions in streaming data
and real-time machine learning. The conference will be domain driven,
with talks focusing on well-defined areas of application and
describing the techniques and algorithms necessary to address the
current and future challenges in the field.

Sessions will be accessible to a broad audience and will have a single
track format with additional rooms for breakout sessions and posters.
There will be no formal conference proceedings, but conference
applicants are encouraged to submit an abstract and present a talk
and/or poster.


Feb 29     : Initial registration ends, participants announced.
May 7 – 11 : Conference.

 * * SESSIONS * *

Stochastic Data Streams
   Muthu Muthukrishnan: (Dept. of Computer Science, Rutgers University)

Real-Time Machine Learning in Astrophysics
   Alex Szalay:      (Dept. of Physics and Astronomy, John Hopkins University)

Real-Time Analytics with Streaming Databases
   Michael Franklin: (Computer Science Dept., UC Berkeley)

Classification of Sensor Network Data Streams
   Joao Gama:    (Lab. of A.I. & Decision Support, Economics at Univ. of Porto)

Randomized and Approximation Algorithms
   Petros Drineas:   (Computer Science Dept., Rensselaer Polytechnic Institute)

Time-Series Clustering and Classification
   Eamonn Keogh:     (Computer Science and Engineering Dept., UC Riverside)

Time Series in the Biological and Medical Sciences
   Ziv Bar-Joseph:   (Computer Science Dept., Carnegie Mellon University)

Streaming Graph/Network Data & Architectures
   David Bader:      (College of Computing, Georgia Tech)

Data Mining of Data Streams
   Olfa Nasraoui:    (Dept. of CS & Computer Engineering, Univ. of Louisville)

 * * Local Organizing Committee * *

Joshua Bloom: (Dept. of Astronomy, UC Berkeley)
Damian Eads:  (Dept. of CS, UC Santa Cruz; Dept. of Eng, Univ. of Cambridge)
Berian James: (Dept. of Astr, UC Berkeley; Dark Cosmology Centre, U Copenhagen)
Peter Nugent: (Comp. Cosmology, Lawrence Berkeley National Lab.)
John Rice:    (Dept. of Statistics, UC Berkeley)
Joseph Richards: (Dept. of Astronomy & Dept. of Statistics, UC Berkeley)
Dan Starr:    (Dept. of Astronomy, UC Berkeley)

 * * Scientific Organizing Committee * *

Leon Bottou:     (NEC Labs)
Emmanuel Candes: (Stanford)
Brad Efron:      (Stanford)
Alex Gray:       (Georgia Tech)
Michael Jordan:  (Berkeley)
John Langford:   (Yahoo)
Fernando Perez:  (Berkeley)
Ricardo Vilalta: (Houston)
Larry Wasserman: (CMU)