Publications

Organized by the year of publication

2021    2020     2019     2018     2017     2016     2015     2014    Prior-to-2014

Preprints

K. Thopalli, S. Katoch, P. Turaga, A. Spanias, J. J. Thiagarajan. Multi-Domain Ensembles for Domain Generalization. 2021 [coming soon]

R. Subramanyam, M. Heimann, T. S. Jayram, R. Anirudh, B. Kailkhura, J. J. Thiagarajan. Learning Knowledge Graph Hierarchies for Improving Few-Shot Classification. 2021 [coming soon]

K. Thopalli, J. J. Thiagarajan, R. Anirudh and P. Turaga. Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation. 2021 [coming soon]

P. Trivedi,  M. Heimann, D. Koutra, J. J. Thiagarajan. Interrogating Paradigms in Self-supervised Graph Representation Learning. 2021 [coming soon]

T. Gokhale, R. Anirudh, J. J. Thiagarajan, B. Kailkhura, C. Baral. Y. Yang. ALT: Improving Diversity with Adversarially Learned Transformations for Domain Generalization [coming soon]

K. Thopalli, P. Turaga, J. J. Thiagarajan. Re-labeling Domains Improves Multi-Domain Generalization. 2021 [coming soon]

V. Narayanaswamy, R. Anirudh, I. Kim, Y. Mubarka, A. Spanias, J. J. Thiagarajan. Predicting the Generalization Gap in Deep Models Using Anchoring. 2021 [coming soon]

A. Karargyris et al. MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation. 2021 [preprint]

J. Peterson et al. Enabling Machine Learning-Ready HPC Ensembles with Merlin. 2021 [preprint]

J. J. Thiagarajan, R. Anirudh. Δ-UQ: Accurate Uncertainty Quantification via Anchor Marginalization. 2021 [preprint]

J. J. Thiagarajan, K. Thopalli, D. Rajan, P. Turaga. Training Calibration-based Counterfactual Explainers for Deep Learning Models in Medical Image Analysis. 2021 [preprint]

B. Venkatesh, J. J. Thiagarajan. Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning. 2021 [preprint]

B. Venkatesh, J. J. Thiagarajan. Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification. 2020 [preprint]

B. Venkatesh, J. J. Thiagarajan, K. Thopalli, P. Sattigeri. Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. 2020 [preprint]

J. J. Thiagarajan, P. Sattigeri, D. Rajan, B. Venkatesh. Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. 2020 [preprint]

S. Katoch, K. Thopalli, J. J. Thiagarajan, P. Turaga, A. Spanias. Invenio: Discovering Hidden Relationships Between Tasks/Domains using Structured Meta Learning. 2020 [preprint]

[Top]

2021

A. Shukla, R. Anirudh, E. Kur, J. J. Thiagarajan, P-T. Bremer, P. Turaga, B. Spears, T. Ma. Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion. Neurips 2021 Workshop on Machine Learning for Physical Sciences.

S. Liu, R. Anirudh, J. J. Thiagarajan, P. Bremer. Sparsity Improves Unsupervised Attribute Discovery in StyleGAN. Neurips 2021 Workshop on Distribution Shifts.

M. Olson, R. Anirudh, J. J. Thiagarajan, W. Wong, P. Bremer, S. Liu. Unsupervised Attribute Alignment for Characterizing Distribution Shift. Neurips 2021 Workshop on Distribution Shifts.

J. J. Thiagarajan, V. Narayanaswamy, D. Rajan, J. Liang, A. Chaudhary, A. Spanias. Designing Counterfactual Generators using Deep Model Inversion. Neurips [preprint]

T. Ma et al. Accelerating the rate of discovery: toward high-repetition-rate HED science. Plasma Physics and Controlled Fusion [paper]

V. Narayanaswamy, J. J. Thiagarajan, A. Spanias. On the Design of Deep Priors for Unsupervised Audio Restoration. Interspeech [preprint]

R. Anirudh and J. J. Thiagarajan. Machine Learning Methods for Autism Spectrum Disorder Classification. Neural Engineering Techniques for Autism Spectrum Disorder. Vol. 1 [chapter]

S. Rao, V. Narayanaswamy, M. Esposito, J. J. Thiagarajan, A. Spanias. Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection. International Conference on Information, Intelligence, Systems & Applications [paper]

T. Islam, P. W. Liang, F. Sweeney, C. Pragner, J. J. Thiagarajan, M. Sharmin, S. Ahmed. College Life is Hard!-Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis. Computers, Software, and Applications Conference (COMPSAC) [paper]

V. Narayanaswamy, J. J. Thiagarajan, A. Spanias. Using Deep Image Priors to Generate Counterfactual Explanations. IEEE ICASSP [preprint]

T. Ramadan, T. Z. Islam, C. Phelps, N. Pinnow, J. J. Thiagarajan. Comparative Code Structure Analysis using Deep Learning for Performance Prediction. IEEE International Symposium on Performance Analysis of Systems and Software [paper]

R. Anirudh, J. J. Thiagarajan, R. Sridhar and P-T. Bremer. MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis. Frontiers Big Data [paper]

U. Shanthamallu, J. J. Thiagarajan, A. Spanias. Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks. AAAI [preprint]

J. J. Thiagarajan, V. Narayanaswamy, R. Anirudh, P-T. Bremer, A. Spanias. Accurate and Robust Feature Importance Estimation under Distribution Shifts. AAAI [preprint]

T. Gokhale, R. Anirudh, B. Kailkhura, J. J. Thiagarajan, C. Baral, Y. Yang. Attribute-Guided Adversarial Training for Robustness to. Natural Perturbations. AAAI [preprint]

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

[Top]

2020

B. Kailkhura, J. J. Thiagarajan, Q. Li, J. Zhang, Y. Zhou, P-T. Bremer. A statistical Mechanics Framework for Task-Agnostic Sample Design. Neurips.

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. Nature Communications [preprint]

J. J. Thiagarajan, D. Rajan, S. Katoch and A. Spanias. DDxNet: A Multi-Speciality Diagnostic Model for ECG and EEG. Nature Scientific Reports. [paper][code]

J. J. Thiagarajan, B. Venkatesh, D. Rajan, P. Sattigeri. Improving Reliability of Clinical Models using Prediction Calibration. MICCAI UNSURE Workshop.

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]

[Top]

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]

[Top]

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]

[Top]

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]

[Top]

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]

[Top]

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]

[Top]

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]

[Top]

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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