SETDiR: Scalable Extensible Toolkit for Dimensionality Reduction

Publications

  1. S. Samudrala, O. Wodo, S. K. Suram, S. Broderick, K. Rajan, B. Ganapathysubramanian, “A Graph-Theoretic Approach for Characterization of Precipitates from Atom Probe Tomography data”, Journal of Computational Materials Science, under-review.
  2. P. V. Balachandran, S. Samudrala, B. Ganapathysubramanian, and K. Rajan, “Comparative study of data dimensionality reduction methods for the discovery of materials chemistries for toxicity immobilization. pre-print.
  3. S. Samudrala, J. Zola, S. Aluru, and B. Ganapathysubramanian, “Parallel framework for dimensionality reduction of large-scale datasets”, 2012. pre-print.
  4. S. Samudrala, S. Kishtampalli, S. Sundararajan, K. Rajan, B. Ganapathysubramanian, “Parametric Analytic Study of Atom Probe Tomography Data using Adaptive Sparse Grid Collocation Techniques”, In preparation.
  5. S.Samudrala, B.Ganapathysubramanian, “Extracting Topological Properties using Manifold Learning Techniques”, In preparation.

Conferences:

  1. S. Samudrala, J. Zola, S. Aluru, B. Ganapathysubramanian, “A Scalable Computational Framework for Manifold Learning - Comparison on Distributed and Shared Memory Architectures”, to be presented at 9th World Congress on Computational Mechanics, Sydney, Australia, July 19-23, 2010.