Friday, January 20, 2012

Machine-Learning with Real-time & Streaming Applications

Dan Starr just sent me the following:

We are organizing a meeting on the UC Berkeley campus on a topic which may be of interest to your Nuit-Blanche website and may warrant being mentioned under your "CS meetings related to compressive sensing". If interested, please feel free to add this conference or disseminate the following announcement.

Sure Dan , it looks very interesting:
From Data to Knowledge: Machine-Learning with Real-time & Streaming ApplicationsMay 7-11 2012On 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)

I asked Dan, if there would be any video coverage of the event:

Our current plan is not to use the IT/AV services of UC Berkeley. The Local Organizing Committee does prefer conferences with video archives, so we may find an alternative option by May.

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