Jayaraman Jayaraman Thiagarajan

Email: jjayaram@llnl.gov
Phone: +19254242255

I am a Computer Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. I received my Master of Science and doctorate degrees in Electrical Engineering from Arizona State University in 2008 and 2013 respectively.

My research involves high-dimensional data analysis using tools from statistics, machine learning and computational topology, and building novel representation learning frameworks for use in image/video understanding, computer vision, and data mining.

Research Interests:

  • Sparse representations, unsupervised learning, graph-based methods, kernel methods, ensemble learning, deep learning, dimensionality reduction, statistical learning theory.
  • Signal processing, topological data analysis, data visualization.
  • Image understanding, reconstruction, segmentation, object recognition, semantic analysis, compressed sensing, activity recognition.

Professional Activities:

  • Memberships: IEEE, IEEE Signal Processing Society
  • Reviewer of several IEEE, Elsevier, EURASIP, Springer, and ACM conferences and journals.

Selected Publications:

Books and Theses:

1. J.J. Thiagarajan, K.N. Ramamurthy, P. Turaga and A. Spanias, “Image understanding using sparse representations”. Synthesis Lectures on Image, Video and Multimedia Processing, Morgan and Claypool Publishers, 2014.

2. J.J. Thiagarajan, “Sparse models in image understanding and computer vision”, PhD Dissertation, Arizona State University, 2013 [pdf].

3. J.J. Thiagarajan, and A. Spanias, “Analysis of the MPEG-1 Layer III algorithm using MATLAB”. Synthesis Lectures on Algorithms and Software in Engineering, Morgan and Claypool Publishers, 2011.

4. J.J. Thiagarajan, “Dictionary Learning Algorithms for Shift-invariant Representations and Pattern Classification”, M.S. Thesis, Arizona State University, 2013.

Journals:

[1] J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias, “Multiple kernel sparse represenations for supervised and unsupervised learning”. IEEE Transactions on Image Processing, 2014.

[2] J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias, “Learning stable multilevel dictionaries for sparse representation of images”. IEEE Transactions on Neural Networks and Learning Systems, 2014.

[3] J.J. Thiagarajan, K.N. Ramamurthy, D. Rajan, A. Puri, D. Frakes and A. Spanias, “Kernel sparse models for automated tumor segmentation”. International Journal on Artificial Intelligence Tools, 2014 (invited paper).

[4 K.N. Ramamurthy, J.J. Thiagarajan and A. Spanias, “Recovering non-negative and combined sparse representations”. Digital Signal Processing, 2014.

[5] K.N. Ramamurthy, J.J. Thiagarajan, P. Sattigeri and A. Spanias, “Ensemble sparse models for image analysis”. IEEE Transactions on Image Processing, 2014.

[6] J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias, “Mixing matrix estimation using discriminative clustering for blind source separation”. Digital Signal Processing, 2012.

[7] J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias, “Optimality and stability of the K-hyperline clustering algorithm”. Pattern Recognition Letters, 2010.

[8] K.N. Ramamurthy, J.J. Thiagarajan, P. Sattigeri and A. Spanias, “Transform domain features for ion-channel signal classification”. Biomedical Signal Processing and Control, 2010.

Conference Publications

[1] P. Sattigeri, J.J. Thiagarajan, M. Shah, K.N. Ramamurthy and A. Spanias, “A scalable feature learning and tag prediction framework for natural environment sounds”. IEEE Asilomar SSC, 2014.

[2] K.N. Ramamurthy, J.J. Thiagarajan, R. Sridhar, K. Premnishanth and N. Ramanathan, “Consensus inference with multilayer graphs for multi-modal data”. IEEE Asilomar SSC, 2014.

[3] S. Liu, B. Wang, J.J. Thiagarajan, P.T. Bremer and V. Pascucci, “Multivariate volume visualization through dynamic projections”. IEEE LDAV, 2014.

[4] J.J. Thiagarajan, K.N. Ramamurthy, P. Sattigeri, P.T. Bremer and A. Spanias, “Automated image annotation using inverse maps from semantic embeddings”. IEEE ICIP, 2014.

[5] H. Kim, J.J. Thiagarajan and P.T. Bremer, “Image segmentation using consensus from hierarchical segmentation ensembles”. IEEE ICIP, 2014.

[6] K.N. Ramamurthy, K. Varshney and J.J. Thiagarajan, “Computing persistent homology under random projection”. IEEE SSP Workshop, 2014.

[7] J.J. Thiagarajan, K.N. Ramamurthy and P.T. Bremer, “Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation ”. IEEE ICASSP, 2014.

[8] K.N. Ramamurthy, J.J. Thiagarajan, A. Spanias and P. Sattigeri, “Boosted dictionaries for image restoration based on sparse representations”. IEEE ICASSP, 2013.

[9] R. Anirudh, K.N. Ramamurthy, J.J. Thiagarajan, P. Turaga and Andreas Spanias, “A heterogeneous dictionary model representation and recognition of human actions”. IEEE ICASSP, 2013.

[10] J.J. Thiagarajan, K.N. Ramamurthy, P. Sattigeri and A. Spanias, “Supervised local sparse coding of sub-image features in image retrieval”. IEEE ICIP, 2012.

[11] K.N. Ramamurthy, J.J. Thiagarajan, P. Sattegeri and A. Spanias, “Learning dictionaries with graph embedding constraints for image classification”. IEEE Asilomar SSC, 2012 (Best paper finalist).

[12] J.J. Thiagarajan, K.N. Ramamurthy, D. Frakes, D. Rajan and A. Spanias, “Automated tumor segmentation using kernel sparse representations”. IEEE BIBE, 2012 (Best paper finalist).

[13] P. Sattigeri, J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias,“Implementation of a fast image coding and retrieval system using a GPU”. IEEE ESPA, 2012.

[14] J.J. Thiagarajan, K.N. Ramamurthy, and A. Spanias, “Multilevel dictionary learning for sparse representation of images”. IEEE DSP Workshop, 2011 (Best paper finalist).

[15] K.N. Ramamurthy, J.J. Thiagarajan and A. Spanias, “Improved sparse coding using manifold projections”. IEEE ICIP, 2011.

[16] J.J. Thiagarajan and A. Spanias, “Learning dictionaries for local sparse coding in image classification” IEEE Asilomar SSC, 2011.

[17] P. Knee, J.J. Thiagarajan, K.N. Ramamurthy and A. Spanias, “SAR target classification using sparse representations and spatial pyramids”. IEEE Radar conference, 2011.

[18] J.J. Thiagarajan, K.N. Ramamurthy, P. Knee, V. Berisha and A. Spanias, “Sparse representations for automatic target classification in SAR images”. IEEE ISCCSP, 2010.

[19] K.N. Ramamurthy, J.J. Thiagarajan and A. Spanias, “Template learning using wavelet domain statistical models”. Research and Development in Intelligent Systems XXVI, Springer, 2010.

[20] J.J. Thiagarajan, K.N. Ramamurthy, and A. Spanias, “Dimensionality reduction for distance based video clustering”. AIAI, 2010.

[21] K.N. Ramamurthy, J.J. Thiagarajan and A. Spanias, “Fast image registration using non-stationary Gauss Markov random field templates”. IEEE ICIP, 2009.

[22] K.N. Ramamurthy, J.J. Thiagarajan, P. Sattigeri and A. Spanias, “Transform domain features for ion-channel signal classification using support vector machines”. IEEE ITAB, 2009.

[23] J.J. Thiagarajan, K.N. Ramamurthy, and A. Spanias, “Shift-invariant sparse representation of images using learned dictionaries”. IEEE MLSP Workshop, 2008.