About the Astroinformatics Seminars

This new seminar series has been established to help draw together the astrophysics and computer science communities.  Computational astrophysics, for simulating on a cosmological scale or analyzing datasets gathered by telescopes or simulation output, is a critical tool in the advancement of cosmology.  For computer science, computational astrophysics presents challenges and opportunities at the leading edge of data-intensive scalable computing, machine learning, and computational science (eScience) tools and techniques.  To better work together we need to learn about each other's field.  Hence the Astroinformatics Seminar Series.

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Upcoming Seminars

Thursday November 5, 2009
1:00 - 2:00 pm (10:00-11:00 am pacific)
6501 Gates Hillman Center
Presented on video at the Univ. of Washington Astro Video Conference Room (B368 PAB)

Jeff Schneider, Carnegie Mellon University
Active Learning for Fitting Astrophysical Simulation Models to Observational Data

SLIDES AVAILABLE - PDF [9.5M]

Researchers in machine learning and operations research have made great strides in modeling and optimizing manufacturing and other commercial processes.  A more recent trend is to observe that many scientific modeling tasks can be approached with similar techniques. In this talk we consider a specific example of that: using active learning to fit cosmological models based on data from sky surveys.  We propose appropriate active learning algorithms, make observations about the implications of using Bayesian vs frequentist methods to test model fits, and address active learning for data/evidence fusion.  Results will be presented for the CMBFAST model and WMAP data, and future plans will be described in cosmology and other scientific applications.  We will conclude with some observations about active learning in parallel computing architectures.

Speaker Bio:
Dr. Jeff Schneider is an associate research professor in the Carnegie Mellon University School of Computer Science.  He received his PhD in Computer Science from the University of Rochester in 1995.  He has over 15 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry.  He has dozens of publications and has given numerous invited talks and tutorials on the subject.

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