Publications

Organized by the year of publication

2020     2019     2018     2017     2016     2015     2014    Prior-to-2014

Preprints

J. J. Thiagarajan, B. Venkatesh, R. Anirudh, P-T. Bremer, J. Gaffney, G. Anderson, B. Spears. Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models. [preprint]

D. Rajan, J. J. Thiagarajan, S. Kashyap, A. Karargyris. Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. [preprint]

J. J. Thiagarajan, P. Sattigeri, D. Rajan, B. Venkatesh. Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. [preprint]

B. Venkatesh, J. J. Thiagarajan, K. Thopalli, P. Sattigeri. Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. [preprint]

S. Katoch, K. Thopalli, J. J. Thiagarajan, P. Turaga, A. Spanias. Invenio: Discovering Hidden Relationships Between Tasks/Domains using Structured Meta Learning. [preprint]

B. Venkatesh, J. J. ThiagarajanHeteroscedastic Calibration of Uncertainty Estimators in Deep Learning. [preprint]

K. Thopalli, J. J. Thiagarajan, R. Anirudh and P. Turaga. SALT: Subspace Alignment as an Auxiliary Task for Domain Adaptation. [preprint]

B. Kailkhura, J. J. Thiagarajan, Q. Li and P-T. Bremer. A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis. [preprint]

V. Narayanaswamy, S. Katoch, J. J. Thiagarajan, H. Song and A. Spanias. Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets. [preprint]

J. J. Thiagarajan, D. Rajan, S. Katoch and A. Spanias. DDxNet: A Multi-Speciality Diagnostic Model for ECG and EEG. [preprint coming soon]

R. Anirudh, J. J. Thiagarajan,  R. Sridhar and P-T. Bremer. Interpretability via Graph-based Introspection. [preprint]

[Top]

2020

V. Narayanaswamy, J. J. Thiagarajan, R. Anirudh, A. Spanias. Unsupervised Audio Source Separation using Generative Priors. Interspeech 2020. [preprint][code]

S. Liu, R. Anirudh, J. J. Thiagarajan, P-T. Bremer. Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections. Machine Learning: Science and Technology. [paper][code]

B. Kailkhura, J. J. Thiagarajan, Q. Li, J. Zhang, Y. Zhou, P-T. Bremer. Task-agnostic Sample Design for Machine Learning. Workshop on Real World Experiment Design and Active Learning. ICML 2020. [paper]

R. Anirudh, J. J. Thiagarajan, P-T. Bremer, B. Spears. Improved Surrogates in Intertial Confinement Fusion with Manifold and Cycle Consistencies. Proceedings of the National Academy of Sciences. [paper]

G. Muniraju, B. Kailkhura, J. J. Thiagarajan and P-T. Bremer. Coverage-Based Designs Improve Sample Miningand Hyper-Parameter Optimization. IEEE Transactions on Neural Networks and Learning Systems 2020. [paper]

A. Bhatele, J. J. Thiagarajan, T. Groves, R. Anirudh, S. Smith, B. Cook, D. Lowenthal. The Case of Performance Variability on Dragonfly-based Systems. IEEE IPDPS 2020. [paper]

R. Anirudh, J. J. Thiagarajan, B. Kailkhura and P-T. Bremer. MimicGAN: Corruption-Mimicking for Blind Image Recovery and Adversarial Defense. IJCV Special Issue on GANs 2020. [paper]

U. Shanthamallu, J. J. Thiagarajan and A. Spanias. A Regularized Attention Mechanism for Graph Attention Networks. IEEE ICASSP 2020. [preprint]

J. J. Thiagarajan, Bindya Venkatesh, Deepta Rajan. Learn-by-Calibrating: Using Calibration as a Training Objective. IEEE ICASSP 2020 [preprint]

J. J. Thiagarajan, B. Venkatesh, P. Sattigeri, P-T. Bremer. Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. AAAI Conference on Artificial Intelligence 2020. [paper] [poster]

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2019

J. J. Thiagarajan, S. Kashyap, A. Karagyris. Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation. IEEE ICMLA 2019. [paper] [slides]

J. J. ThiagarajanHigh-Dimensional Spectral Sampling. Technical Report 2019. [report]

R. Anirudh, H. Kim, J. J. Thiagarajan, K. A. Mohan, K. Champley. Improving Limited Angle CT Reconstruction with a Robust GAN Prior. Neurips Solving Inverse Problems with Deep Learning Workshop 2019. [paper] [poster]

R. Anirudh, J. J. Thiagarajan, S. Liu, P-T. Bremer, B. Spears. Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion. Neurips Machine Learning and the Physical Sciences Workshop 2019. [paper] [poster]

V. Narayanaswamy, J. J. Thiagarajan, R. Anirudh, F. Forouzanfar, P-T. Bremer, X. Wu. Designing Deep Inverse Models for History Matching in Reservoir Simulations. Neurips Machine Learning and the Physical Sciences Workshop 2019. [paper] [poster]

U. Shanthamallu, Q. Li, J. J. Thiagarajan, R. Anirudh, A. Kaplan, P-T. Bremer. Modeling Human Brain Connectomes using Structured Neural Networks. Neurips Graph Representation Learning Workshop 2019. [paper] [poster]

H. Song, J. J. ThiagarajanImproving Deep Embeddings for Inferencing with Multi-Layered Graphs. Deep Graph Learning: Methodologies and Applications, IEEE Big Data 2019. [preprint] [poster]

T. Patki, J. J. ThiagarajanA. Ayala, T. Islam. Performance optimality or reproducibility: that is the question. Supercomputing 2019. [paper] [slides]

U. Shanthamallu, J. J. Thiagarajan, H. Song and A. Spanias. GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention ModelsIEEE Transactions on Neural Networks and Learning Systems 2019. [paper]

B. Kustowski, J. Gaffney, B. Spears, G. Anderson, J. J. Thiagarajan, R. Anirudh. Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion. IEEE Transactions on Plasma Science 2019. [paper]

S. Liu, D. Wang, D. Maljovec, R. Anirudh, J. J. Thiagarajan, S. A. Jacobs, B. V. Essen, D. Hysom, J. Yeom, J. Gaffney, L. Peterson, H. Bhatia, V. Pascucci, B. Spears, P-T. Bremer. Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. IEEE Transactions on Visualization and Graphics 2019. [paper]

J. J. Thiagarajan, D. Rajan and P. Sattigeri. Can Deep Clinical Models Handle Real-World Domain Shifts? KDD Workshop on Applied Data Science for Healthcare 2019. Best Paper Award. [paper] [poster]

S. A. Jacobs, B. V. Essen, D. Hysom, J. Yeom, T. Moon, R. Anirudh, J. J. Thiagarajan, S. Liu, P-T. Bremer, J. Gaffney, T. Benson, P. Robinson, L. Peterson, B. Spears. Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. IEEE CLUSTER 2019. [paper]

R. Anirudh and J. J. Thiagarajan. Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. IEEE ICASSP 2019. [paper] [poster]

J. J. Thiagarajan, I. Kim, R. Anirudh and P-T. Bremer. Understanding Deep Neural Networks using Input Uncertainties. IEEE ICASSP 2019. [paper] [slides]

J. J. Thiagarajan, R. Anirudh, R. Sridhar and P-T. Bremer. Unsupervised Dimension Selection using a Blue Noise Spectrum. IEEE ICASSP 2019. [paper] [poster]

V. Narayanaswamy, J. J. Thiagarajan, H. Song and A. Spanias. Designing an Effective Metric Learning Pipeline for Speaker Diarization. IEEE ICASSP 2019. [paper] [slides]

K. Thopalli, R. Anirudh, J. J. Thiagarajan and P. Turaga. Multiple Subspace Alignment Improves Domain Adaptation. IEEE ICASSP 2019. [paper] [poster]

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2018

B. Spears, J. Brase, P-T. Bremer, B. Chen, J. Field, J. Gaffney, M. Kruse, S. Langer, K. Lewis, R. Nora, J. L. Peterson, J. J. Thiagarajan, B. Van Essen and K. Humbird. Deep learning: A guide for practitioners in the physical sciences. Physics of Plasmas. [paper]

S. Smith, C. Cromey, D. Lowenthal, J. Domke, N. Jain, J. J. Thiagarajan and A. Bhatele. Mitigating Inter-Job Interference Using Adaptive Flow-Aware Routing. International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). [paper]

R. Anirudh, J. J. Thiagarajan, P-T. Bremer and B. Spears. Cyclically Consistent Adversarial Networks for Reliable Surrogates in Intertial Confinement Fusion. Sci-ML Workshop. [paper]

S. Liu, K. Humbird, L. Peterson, J. J. Thiagarajan, B. Spears and P-T. Bremer. Topology-Driven Analysis and Exploration of High-Dimensional Models. Sci-ML Workshop. [paper]

Y. Lin, S. Wang, J. J. Thiagarajan, G. Guthrie and D. Coblentz. Efficient data-driven geologic feature characterization from pre-stack seismic measurements using randomized machine learning algorithm. Geophysics Journal International. [paper]

B. Kailkhura, J. J. Thiagarajan, C. Rastogi, P.K. Varshney and P-T. Bremer. A Spectral Approach for the Design of Experiments: Design. Journal of Machine Learning Research .[paper]

H. Song, M. Willi, J. J. Thiagarajan, V. Berisha and A. Spanias. Triplet Network with Attention for Speaker Diarization. InterSpeech. [paper]

Y. Lin, Y. Wu, S. Wang, J. J. Thiagarajan, G. Guthrie and D. Coblentz. Solving Inverse Problems in Geophysics Using Machine LearningSIAM Annual Meeting. [slides]

J. J. Thiagarajan, N. Jain, R. Anirudh, A. Giminiez, R. Sridhar, A. Marathe, T. Wang, M. Emani, A. Bhatele, T. Gamblin. Bootstrapping Parameter Space Exploration for Fast Tuning. International Conference on Supercomputing. [paper]

J. J. Thiagarajan, S. Liu, K. N. Ramamurthy and P.-T. Bremer. Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections. Computer Graphics Forum. [paper]

H. Song, J. J. Thiagarajan, P. Sattigeri and A. Spanias. Optimizing Kernel Machines Using Deep Learning. IEEE Transactions on Neural Networks and Learning Systems. [paper]

D. Rajan and J. J. ThiagarajanA Generative Modeling Approach to Limited Channel ECG Classification. International Conference of the IEEE Engineering in Medicine and Biology Society. [paper]

J. J. Thiagarajan, R. Anirudh, B. Kailkhura, N. Jain, T. Islam, A. Bhatele, J. Yeom and T. Gamblin. PADDLE: Performance Analysis Using a Data-Driven Learning EnvironmentIEEE International Parallel and Distributed Processing Symposium (IPDPS). [paper]

S. Liu, P-T. Bremer, J. J. Thiagarajan, V. Srikumar, B. Wang, Y. Livnat and V. Pascucci. Visual Exploration of Semantic Relationships in Neural Word Embeddings. IEEE Transactions on Visualization and Computer Graphics. [paper]

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2017

R. Anirudh, H. Kim, J. J. Thiagarajan, K. Champley and P-T. Bremer. Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram. Computer Vision and Pattern Recognition. [paper]

R. Anirudh, J. J. Thiagarajan, R. Sridhar and P-T. Bremer. MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis. [preprint]

A. Marathe, R. Anirudh, N. Jain, A. Bhatele, J. J. Thiagarajan, B. Kailkhura, J. Yeom and T. Gamblin. Performance modeling under resource constraints using deep transfer learning. Supercomputing. [paper]

H. Song, D. Rajan, J. J. Thiagarajan and A. Spanias. Attend and Diagnose: Clinical Time Series Analysis using Attention Models. AAAI Conference on Artificial Intelligence. [paper]

Y. Lin, S. Wang and J. J. ThiagarajanTowards real-time geologic feature detection from seismic measurements using a randomized machine-learning algorithm. Society of Exploration Geophysicists Meeting. [paper]

P. Zheng, A. Aravkin, K. N. Ramamurthy and J. J. ThiagarajanLearning Robust Representations for Computer Vision. ICCV Workshops. [paper]

R. Anirudh, B. Kailkhura, J. J. Thiagarajan and P-T. Bremer. Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. CVPR Workshops. [paper]

H. Song, J. J. Thiagarajan, P. Sattigeri, K. N. Ramamurthy and J. J. Thiagarajan. A deep learning approach to multiple kernel fusion. IEEE ICASSP. [paper]

R. Anirudh, J. J. Thiagarajan, I. Kim and W. Polonik. Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series. [preprint]

Q. Li, B. Kailkhura, J. J. Thiagarajan, Z. Zhang and P. K. Varshney. Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. NIPS Time Series Workshop. [paper]

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2016

J. J. Thiagarajan, P. Sattigeri, K. N. Ramamurthy and B. Kailkhura. Robust Local Scaling Using Conditional Quantiles of Graph Similarities. International Conference on Data Mining Workshops. [paper]

J. J. Thiagarajan, B. Kailkhura, P. Sattigeri and K. N. Ramamurthy. TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. NIPS Interpretable Machine Learning in Complex Systems. [paper]

B. Kailkhura, J. J. Thiagarajan, P-T. Bremer and P. K. Varshney. Stair Blue Noise Sampling. ACM Transactions on Graphics (Presented at Siggraph Asia). [paper]

J. Yeom, J. J. Thiagarajan, A. Bhatele, G. Bronevetsky and T. Kolve. Data-driven performance modeling of linear solvers for sparse matrices. International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. [paper]

T. Islam, J. J. Thiagarajan, A. Bhatele, M. Schulz and T. Gamblin. A Machine Learning Framework for Performance Coverage Analysis of Proxy Applications. International Conference for High Performance Computing, Networking, Storage and Analysis. [paper]

J. Miller, J. J. Thiagarajan, P-T. Bremer, N. Hoda, D. Stern and R. Mifflin. Data-Driven Metric Learning for History Matching. SPE Reservoir Simulation Conference. [paper]

H. Song, J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Auto-context modeling using multiple Kernel learning. IEEE International Conference on Image Processing. [paper]

B. Kailkhura, J. J. Thiagarajan, P-T. Bremer and P. K. Varshney. Theoretical guarantees for poisson disk sampling using pair correlation function. IEEE ICASSP. [paper]

H. Song, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias and Pavan Turaga. Consensus inference on mobile phone sensors for activity recognition. IEEE ICASSP. [paper]

K. N. Ramamurthy, A. Aravkin and J. J. Thiagarajan. Beyond L2-loss functions for learning sparse models. IEEE ICASSP. [paper]

R. Anirudh, J. J. Thiagarajan, P-T. Bremer and H. Kim. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. SPIE Medical Imaging Conference. [paper]

D. Widemann, E. Wang and J. J. Thiagarajan. ROPE: Recoverable Order-Preserving Embedding of Natural Language. Technical Report. [preprint]

P. Khanduri, B. Kailkhura, J. J. Thiagarajan and P. K. Varshney. Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. IEEE Signal Processing Letters. [paper]

P. Sattigeri and J. J. ThiagarajanSparsifying Word Representations for Deep Unordered Sentence Modeling.  ACL Workshop on Representation Learning for NLP. [paper]

S. Liu, P-T. Bremer, J. J. Thiagarajan, B. Wang, B. Summa and V. Pascucci. The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High‐Dimensional Data. Computer Graphics Forum. [paper]

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2015

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Learning Stable Multilevel Dictionaries for Sparse Representations. IEEE Transactions on Neural Networks and Learning Systems. [paper]

S. Liu, B. Wang, J. J. Thiagarajan, P-T. Bremer and V. Pascucci. Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections. Computer Graphics Forum [paper][demo]

K. N. Ramamurthy, A. Aravkin and J. J. ThiagarajanAutomatic Inference of the Quantile Parameter. [preprint]

H. Kim, J. J. Thiagarajan and P-T. Bremer. A Randomized Ensemble Approach to Industrial CT Segmentation. IEEE International Conference on Computer Vision (ICCV). [paper]

J. J. Thiagarajan and K. N. Ramamurthy. Subspace learning using consensus on the grassmannian manifold. IEEE ICASSP. [paper]

S. Ranganath, J. J. Thiagarajan, K. N. Ramamurthy, S. Hu, M. Banavar and A. Spanias. Undergraduate Signal Processing Laboratories for the Android Operating System. American Society for Engineering Education. [paper]

A. Bhatele, A. Titus, J. J. Thiagarajan, N. Jain, T. Gamblin, P-T. Bremer, M. Schulz and L. Kale. Identifying the Culprits Behind Network Congestion. International Parallel and Distributed Processing Symposium. [paper]

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2014

K. N. Ramamurthy, J. J. Thiagarajan, R. Sridhar, P. Kothandaraman and R. Nachiappan. Consensus inference with multilayer graphs for multi-modal data. Asilomar Signals and Systems Conference. [paper]

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. Asilomar Signals and Systems Conference. [paper]

S. Liu, B. Wang, J. J. Thiagarajan, P-T. Bremer and V. Pascucci. Multivariate volume visualization through dynamic projections. IEEE Symposium on Large Data Analysis and Visualization. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning. IEEE Transactions on Image Processing. [paper]

H. Kim, J. J. Thiagarajan and P-T. Bremer. Image segmentation using consensus from hierarchical segmentation ensembles. IEEE International Conference on Image Processing. [paper]

J. J. Thiagarajan, K. N. Ramamurthy, P. Sattigeri, P-T. Bremer and A. Spanias. Automatic image annotation using inverse maps from semantic embeddings. IEEE International Conference on Image Processing. [paper]

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. Recovering non-negative and combined sparse representations. Digital Signal Processing. [paper]

K. N. Ramamurthy, K. Varshney and J. J. ThiagarajanComputing persistent homology under random projection. IEEE Workshop on Statistical Signal Processing. [paper]

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

J. J. Thiagarajan, K. N. Ramamurthy, D. Rajan, A. Spanias, A. Puri and D. Frakes. Kernel Sparse Models for Automated Tumor Segmentation. International Journal on Artificial Intelligence Tools. [paper]

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Prior-to-2014

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

R. Anirudh, K. N. Ramamurthy, J. J. Thiagarajan, P. Turaga and A. Spanias. A heterogeneous dictionary model for representation and recognition of human actions. IEEE ICASSP. [paper]

S. Mehta, A. Spanias, J. J. Thiagarajan, M. Banavar, K. N. Ramamurthy, R. Santucci, C. Pattichis, P. Spanias and H. Krishnamoorthi. An integrated graphical environment for web-based learning. ASEE Annual Conference and Exposition. [paper]

A. Spanias, J. J. Thiagarajan, K. N. Ramamurthy, M. Banavar, S. Ranganath, X. Zhang, G. Kalyanasundaram and D. Rajan. E-book on DSP theory with interactive ios, java, and android simulations. ASEE Annual Conference and Exposition. [paper]

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

J. J. Thiagarajan, D. Rajan, K. N. Ramamurthy, D. Frakes and A. Spanias. Automated tumor segmentation using kernel sparse representations. International Conference on Bioinformatics and Bioengineering. [paper]

K. N. Ramamurthy, J. J. Thiagarajan, P. Sattigeri and A. Spanias. Learning dictionaries with graph embedding constraints. Asilomar Signals and Systems. [paper]

S. Ranganath, J. J. Thiagarajan. K. N. Ramamurthy, S. Hu, M. Banavar and A. Spanias. Performing signal analysis laboratories using Android devices. IEEE Frontiers in Education. [paper]

J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias and P. Nasiopoulos. Learning Multilevel Dictionaries for Compressed Sensing Using Discriminative Clustering. International Conference on Information Hiding and Multimedia Signal Processing. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Local Sparse Coding for Image Classification and Retrieval. [preprint]

J. Liu, S. Hu, J. J. Thiagarajan, X. Zhang, S. Ranganath, M. Banavar and A. Spanias. Interactive DSP laboratories on mobile phones and tablets. IEEE ICASSP [paper]

P. Sattigeri, J. J. ThiagarajanK. N. Ramamurthy and A. Spanias. Implementation of a fast image coding and retrieval system using a GPU. IEEE International Conference on Emerging Signal Processing Applications. [paper]

X. Zhang, D. Vogel, M. Banavar, S. Hu, A. Spanias, P. Spanias and J. J. ThiagarajanUsing modern mobile technologies in STEM education. IEEE Frontiers in Education. [paper]

P. Sattigeri, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias, M. Goryll and T. Thorton. De-noising and event extraction for silicon pore sensors using matrix decomposition. Sensor Signal Processing for Defence. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Learning dictionaries for local sparse coding in image classification. Asilomar SSC Conference. [paper]

K. Tsakalis, J. J. ThiagarajanT. Duman, M. Reisslein,G. Tong Zhou, X. Ma and P. Spanias. Modules and laboratories for a pathways course in signals and systems. IEEE Frontiers in Education. [paper]

J. Liu, A. Spanias, M. Banavar, J. J. Thiagarajan, K. N. Ramamurthy, S. Hu and X. Zhang. Interactive signal-processing labs and simulations on iOS devices. IEEE Frontiers in Education. [paper]

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. Improved sparse coding using manifold projections. IEEE International Conference on Image Processing. [paper]

P. Sattigeri, K. N. Ramamurthy, J. J. Thiagarajan, M. Goryll, A. Spanias and T. Thornton. Analyte detection using an ion-channel sensor array. International Conference on Digital Signal Processing. [paper]

K. N. Ramamurthy, J. J. Thiagarajan, P. Sattigeri, M. Goryll, A. Spanias, T. Thornton and S. Philips. Transform domain features for ion-channel signal classificationBiomedical Signal Processing and Control. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Optimality and stability of the K-hyperline clustering algorithmPattern Recognition Letters. [paper]

P. Knee, J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. SAR target classification using sparse representations and spatial pyramids. IEEE RadarCon. [paper]

C. Huang, J. J. Thiagarakan, A. Spanias and C. Pattichis. A Java-DSP interface for analysis of the MP3 algorithm. IEEE DSP Conference. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Multilevel dictionary learning for sparse representation of imagesIEEE DSP Conference. [paper]

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. An Interactive Speech Coding Tool using LabVIEW. IEEE DSP Conference. [paper]

P. Sattigeri, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias, M. Goryll and T. Thorton. Robust PSD features for ion-channel signals Sensor Signal Processing for Defence. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Dimensionality Reduction for Distance Based Video Clustering. IFIP Advances in Information and Communication Technology. [paper]

P. Sattigeri, J. J. Thiagarajan, K. N. Ramamurthy, B. Konnanath, T. Matthew, A. Spanias, M. Goryll and T. Thorton. Signal processing for biologically inspired sensors. International Symposium on Communications, Control and Signal Processing. [paper]

J. J. Thiagarajan, K. N. Ramamurthy, P. Knee, A. Spanias and V. Berisha. Sparse representations for automatic target classification in SAR images. International Symposium on Communications, Control and Signal Processing. [paper]

P. Sattigeri, J. J. Thiagarajan, K. N. Ramamurthy, P. Joshi, A. Spanias, M. Goryll and T. Thorton. Analysis of Coulter counting data from nanopores using clustering. Sensor Signal Processing for Defence[paper]

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. Template Learning using Wavelet Domain Statistical ModelsResearch and Development in Intelligent Systems. [paper]

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. Fast image registration with non-stationary Gauss-Markov random field templates. IEEE International Conference on Image Processing. [paper]

K. N. Ramamurthy, J. J. Thiagarajan,P. Sattigeri, B. Konnanath, A. Spanias, T. Thorton, S. Prasad and S. Philips. Transform domain features for ion-channel signal classification using support vector machines. International Conference on Information Technology and Applications in Biomedicine. [paper]

J. J. Thiagarajan, K. N. Ramamurthy,  and A. Spanias. Template Learning using Wavelet Domain Statistical ModelsResearch and Development in Intelligent Systems. [paper]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. Shift-invariant sparse representation of images using learned dictionaries. IEEE Workshop on Machine Learning for Signal Processing. [paper]

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