Mathematical Challenges in Scientific Data Mining

January 14 - 18, 2002


Advances in technology have enabled us to collect data from observations, simulations, and experiments at an ever-increasing pace. For the scientist to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated techniques, such as the ones in data mining, must be applied to find the useful information in the data. Data Mining is a multi-disciplinary field, borrowing and enhancing ideas from diverse areas such as statistics, signal and image processing, image understanding, mathematical optimization, computer vision, and pattern recognition. It is rich in challenging mathematical problems, where the complexity and size of the data is matched only by the diversity of applications. Several recent workshops on the subject have indicated that this a field of active research with potential beneficiaries in areas such as astronomy, remote sensing, physics, bio-informatics, medical imaging, non-destructive evaluation, combinatorial chemistry, etc.

The goals for the workshop are:

  • Help mathematicians and data miners to understand the problems faced by domain scientists in analyzing their data
  • Enable data miners to identify techniques being used to solve similar problems in different domains
  • Enable all participants to better understand the mathematics under-lying various techniques
  • Identify open mathematical problems that must be addressed for data mining to be successfully applied to complex data sets in science and engineering applications

Organizing Committee

Ananth Grama (Purdue University, Department of Computer Sciences)
Chandrika Kamath (Lawrence Livermore National Laboratory)
B. S. Manjunath (UCSB, Electrical and Computer Engineering)
Padhraic Smyth (University of California at Irvine, Information and Computer Science)