SETDiR: Scalable Extensible Toolkit for Dimensionality Reduction


  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.


  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.