### Deep Learning

**Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection**

V. Narayanaswamy, Y.Mubarka, R. Anirudh, D. Rajan, A. Spanias, J. J. Thiagarajan

[preprint]

**Domain Alignment Meets Fully Test-Time Adaptation**

K. Thopalli, P. Turaga, J. J. Thiagarajan

[preprint]

**Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification**

R. Subramanyam, M. Heimann, T. S. Jayram, R.Anirudh, J. J. Thiagarajan

[preprint]

**Improving Single-Stage Object Detectors for Night-Time Pedestrian Detection**

S. Devi, K. Thopalli, P. Malarvezhi, J. J. Thiagarajan

International Journal on Pattern Recognition and Aritifical Intelligence [paper]

**Improving Multi-Domain Generalization via Domain Re-labeli**ng

K. Thopalli, S. Katoch, P. Turaga, A. Spanias, J. J. Thiagarajan

[preprint]

**Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety**

P. Trivedi, D. Koutra, J. J. Thiagarajan

ICML2022 Principles of Distribution Shifts Workshop [paper]

**Improved Medical Out-of-Distribution Detectors For Modality and Semantic Shifts**

V. Narayanaswamy, Y.Mubarka, R. Anirudh, D. Rajan, A. Spanias, J. J. Thiagarajan

ICML2022 Principles of Distribution Shifts Workshop

**Geometric Alignment Improves Test-Time Adaptation**

K. Thopalli, P. Turaga, J. J. Thiagarajan

ICML2022 Updatable Machine Learning Workshop

**Learning Knowledge Graph Hierarchies for Few-Shot Dataset Generalization**

R. Subramanyam, M. Heimann, T. S. Jayram, R.Anirudh, J. J. Thiagarajan

[preprint]

R**evisiting Deep Subspace Alignment for Unsupervised Domain Adaptation**

K. Thopall, R. Anirudh, P. Turagai, J. J. Thiagarajan

[preprint]

**Predicting the Generalization Gap in Deep Models Using Anchoring**

V. Narayanaswamy, R. Anirudh, I. Kim, Y. Mubarka, A. Spanias, J. J. Thiagarajan

IEEE ICASSP 2022 [paper]

**Multi-Domain Ensembles for Domain Generalization**

K. Thopalli, S. Katoch, P. Turaga, A. Spanias, J. J. Thiagarajan

Neurips 2021 Workshop on Distribution Shifts [poster]

**Attribute-Guided Adversarial Training for Robustness to. Natural Perturbations**

T. Gokhale, R.Anirudh, J. J. Thiagarajan, B. Kailkhura, C. Baral, Y. Yang

AAAI 2021 [preprint]

**Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification**

D. Rajan, J. J. Thiagarajan,S. Kashyap, A. Karargyris

SPIE Medical Imaging 2021 [preprint] Best Paper Award

**Improving Task and Domain Generalization using Structured Meta Learning**

S. Katoch, K. Thopalli, P. Turaga, A. Spanias, J. J. Thiagarajan

[preprint]

**Calibrate and Prune: Using Prediction Calibration to Improve Lottery Tickets under Distribution Shifts**

B. Venkatesh, J. J. Thiagarajan, K. Thopalli, P. Sattigeri

[preprint]

**Triplet Network with Attention for Speaker Diarization**

H. Song, J. J. Thiagarajan, M. Willi, V. Berisha, A. Spanias

Interspeech 2018 [paper]

**A deep learning approach to multiple kernel fusion**

H. Song, J. J. Thiagarajan, P. Sattigeri, K. N. Ramamurthy, A. Spanias

IEEE ICASSP 2017 [paper]

**Learning Robust Representations for Computer Vision**

P. Zheng, A. Aravkin, K. N. Ramamurthy, J. J. Thiagarajan

IEEE ICCV Workshops 2017 [paper]

**Optimizing Kernel Machines Using Deep Learning**

H. Song, J. J. Thiagarajan, P. Sattigeri, A. Spanias

IEEE Transactions on Neural Networks and Learning Systems [paper]

**Auto-context modeling using multiple Kernel learning**

H. Song, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias

IEEE ICIP 2016 [paper]

**Sparsifying Word Representations for Deep Unordered Sentence Modeling**

P. Sattigeri, J. J. Thiagarajan

ACL Workshop on Representation Learning for NLP 2016 [paper]

**Consensus inference on mobile phone sensors for activity recognition**

H. Song, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias

IEEE ICASSP 2016 [paper]

**Beyond L2-loss functions for learning sparse models**

K. N. Ramamurthy, A. Aravkin, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias

IEEE ICASSP 2016 [paper]

**ROPE: Recoverable Order-Preserving Embedding of Natural Languag**

D. Widemann, E. Wang, J. J. Thiagarajan

[Tech. Report]

**A Randomized Ensemble Approach to Industrial CT Segmentation**

H. Kim, J. J. Thiagarajan, P. Bremer

IEEE ICCV 2015 [paper]

**Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach**

P. Khanduri, B. Kailkhura, J. J. Thiagarajan, P. K. Varshney

IEEE Signal Processing Letters [paper]

S**ubspace learning using consensus on the grassmannian manifold**

J. J. Thiagarajan, K. N. Ramamurthy

IEEE ICASSP [paper]

**Automatic Inference of the Quantile Parameter**

K. N. Ramamurthy, A. Aravkin, J. J. Thiagarajan

[preprint]

**Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning**

J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias

IEEE Transactions on Image Processing [paper]

**A scalable feature learning and tag prediction framework for natural environment sounds**

K. N. Ramamurthy, A. Aravkin, J. J. Thiagarajan

Asilomar Signals and Systems Conference 2014 [paper]

[Top]

### Uncertainty Quantification

**Single Model Uncertainty Estimation via Stochastic Data Centering**

J. J. Thiagarajan, R. Anirudh, V. Nayaranaswamy, P. Bremer

Neurips 2022 [preprint]

**Training Calibration-based Counterfactual Explainers for Deep Learning Models in Medical Image Analysis**

J. J. Thiagarajan, K. Thopalli, D. Rajan, P. Turaga

Scientific Reports [paper] [demo]

**Uncertainty Quantification in Scientific Machine Learning**

J. J. Thiagarajan

[Technical Report]

**Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models.**

J. J. Thiagarajan, B. Venkatesh, R.Anirudh, P. Bremer, J. Gaffney, G. Anderson, B. Spears

Nature Communications [paper] Editor's Spotlight

**Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification**

B. Venkatesh, J. J. Thiagarajan

[preprint]

H**eteroscedastic Calibration of Uncertainty Estimators in Deep Learning**

B. Venkatesh, J. J. Thiagarajan

[preprint]

**Learn-by-Calibrating: Using Calibration as a Training Objective**

J. J. Thiagarajan, B. Venkatesh, D. Rajan

IEEE ICASSP 2020 [paper]

[Top]

### Graph ML

**Understanding Self-Supervised Graph Representation Learning from a Data-Centric Perspective**

P. Trivedi, M. Heimann, E. Lubana, D. Kotura, J. J. Thiagarajan

KDD 2022 Workshop on Mining and Learning with Graphs [paper]

**A Content-First Benchmark for Self-Supervised Graph Representation Learning**

P. Trivedi, M. Heimann, E. Lubana, D. Kotura, J. J. Thiagarajan

The Webconf 2022 Workshop on Graph Learning Benchmarks [paper]

**Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks**

U. Shanthamallu, J. J. Thiagarajan, A. Spanias

AAAI 2021 [paper]

**A Regularized Attention Mechanism for Graph Attention Networks**

U. Shanthamallu, J. J. Thiagarajan, A. Spanias

IEEE ICASSP 2020 [paper]

**Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models**

Q. Li, B. Kailkhura, J. J. Thiagarajan, Z. Zhang, P. K. Varshney

Neurips Workshop 2017 [paper]

**Robust Local Scaling Using Conditional Quantiles of Graph Similarities**

J. J. Thiagarajan, P. Sattigeri, K. N. Ramamurthy, B. Kailkhura

International Conference on Data Mining Workshops 2017 [paper]

**Consensus inference with multilayer graphs for multi-modal data**

K. N. Ramamurthy, J. J. Thiagarajan, R. Sridhar, P. Kothandaraman, R. Nachiappan

Asilomar Conference 2014 [paper]

**Learning dictionaries with graph embedding constraints**

K. N. Ramamurthy, J. J. Thiagarajan, P. Sattigeri, A. Spanias

Asilomar Conference 2013 [paper]

[Top]

### Inverse Problems

**Designing Counterfactual Generators using Deep Model Inversion**

J. J. Thiagarajan, V. Narayanaswamy, D. Rajan, J. Liang, A. Chaudhary, A. Spanias

Neurips 2021 [paper]

**Using Deep Image Priors to Generate Counterfactual Explanations**

V. Narayanaswamy, J. J. Thiagarajan, A. Spanias

IEEE ICASSP 2021 [paper]

**MimicGAN: Corruption-Mimicking for Blind Image Recovery and Adversarial Defense**

R. Anirudh J. J. Thiagarajan, B. Kailkhura, P. Bremer

IJCV Special Issue on GANs [paper]

**Solving Inverse Problems in Geophysics Using Machine Learning**

Y. Lin, Y. Wu, S. Wang, J. J. Thiagarajan, G. Guthrie, D. Coblentz

SIAM Annual Meeting [paper]

**Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram**

R. Anirudh, H. Kim, J. J. Thiagarajan, K. Champley, P. Bremer

CVPR 2018 [paper]

**Automatic image annotation using inverse maps from semantic embeddings**

J. J. Thiagarajan, K. N. Ramamurthy, P. Sattigeri, P. Bremer, A. Spanias

IEEE ICIP 2014 [paper]

**Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation**

K. N. Ramamurthy, A. Aravkin, J. J. Thiagarajan

IEEE ICASSP 2014 [paper]

**Mixing matrix estimation using discriminative clustering for blind source separation**

J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias

Digital Signal Processing [paper]

[Top]

### Sampling

**Data-Efficient Scientific Design Optimization with Neural Network Surrogates**

J. J. Thiagarajan, R. Anirudh, V. Narayanaswamy, Y. Mubarka, I. Kim, P. Bremer, L. Peterson, B. Spears

ICML2022 ReALML Workshop [preprint]

**A statistical Mechanics Framework for Task-Agnostic Sample Design**

B. Kailkhura, J. J. Thiagarajan, Q. Li, J. Zhang, Y. Zhou, P. Bremer

Neurips 2020 [paper]

**Task-agnostic Sample Design for Machine Learning**

B. Kailkhura, J. J. Thiagarajan, Q. Li, J. Zhang, Y. Zhou, P. Bremer

ICML 2020 ReALML Workshop [paper]

**Coverage-Based Designs Improve Sample Miningand Hyper-Parameter Optimization**

G. Muniraju, B. Kailkhura, J. J. Thiagarajan, P Bremer

IEEE Transactions on Neural Networks and Learning Systems [paper]

**High-Dimensional Spectral Sampling**

J. J. Thiagarajan

[Technical Report]

**Bootstrapping Parameter Space Exploration for Fast Tuning**

J. J. Thiagarajan, N. Jain, R. Anirudh, A. Giminiez, R. Sridhar, A. Marathe, T. Wang, M. Emani, A. Bhatele, T. Gamblin

International Conference on Supercomputing 2018 [paper]

**Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization**

R. Anirudh, B. Kailkhura, J. J. Thiagarajan, P. Bremer

CVPR 2017 Workshops [paper]

**Stair Blue Noise Sampling**

B. Kailkhura, J. J. Thiagarajan, P. Bremer, P. K. Varshney

Siggraph Asia 2016 [paper]

**Theoretical guarantees for poisson disk sampling using pair correlation function**

B. Kailkhura, J. J. Thiagarajan, P. Bremer, P. K. Varshney

IEEE ICASSP 2016 [paper]

[Top]

### Explainability

**Cross-Model Attribute Discovery for Independently Trained StyleGANs**

M. Olson, R. Anirudh, J. J. Thiagarajan, W. Wong, P. Bremer. S. Liu

[preprint]

**Sparsity Improves Unsupervised Attribute Discovery in StyleGAN.**

S. Liu, R. Anirudh, J. J. Thiagarajan, P. Bremer

IEEE ICASSP 2022 [paper]

**Unsupervised Attribute Alignment for Characterizing Distribution Shift**

M. Olson, R. Anirudh, J. J. Thiagarajan, W. Wong, P. Bremer. S. Liu

Neurips 2021 Workshop on Distribution Shifts [paper]

**MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis**

R. Anirudh, J. J. Thiagarajan, R. Sridhar, P. Bremer

Frontiers in Big Data [paper]

**Accurate and Robust Feature Importance Estimation Under Distribution Shifts**

J. J. Thiagarajan, V. Narayanaswamy, R. Anirudh, P. Bremer

AAAI 2021 [paper]

**Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions**

P. Sattigeri, J. J. Thiagarajan, K. N. Ramamurthy, B. Kailkhura

Asilomar Conference 2020 [paper]

**Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections**

J. J. Thiagarajan, S. Liu, K. N. Ramamurthy, P. Bremer

Computer Graphics Forum [paper]

**Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications**

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. Bremer

IEEE Transactions on Visualization and Computer Graphics [paper]

**Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections**

S.Liu, P. Bremer, J. J. Thiagarajan, V. Srikumar, B. Wang, Y. Livnat, V. Pascucci

IEEE Transactions on Visualization and Computer Graphics [paper]

**The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High‐Dimensional Data**

S. Liu, P. Bremer, J. J. Thiagarajan, B. Wang, B. Summa, V. Pascucci

Computer Graphics Forum [paper]

**Multivariate volume visualization through dynamic projections**

S. Liu, B. Wang, J. J. Thiagarajan, P. Bremer, V. Pascucci

IEEE Symposium on Large Data Analysis and Visualization [paper]

[Top]

### AI4Health

**Using Direct Error Predictors to Improve Model Safety and Interpretability**

V. Narayanswamy, D. Rajan, A. Spanias, J. J. Thiagarajan

ICML2022 Interpretable AI in Healthcare Workshop [preprint]

**Uncertainty-Driven Counterfactual Explainers for CXR-Based Diagnosis Models**

K. Thopalli, D. Rajan, P. Turaga, J. J. Thiagarajan

ICML2022 Interpretable AI in Healthcare Workshop [preprint]

**MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation**

A. Karargyris et al.

[preprint]

**Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates**

R. Anirudh, J. J. Thiagarajan, P. Bremer, T. German, S. Del Valle, F. Steritz

ICML 2022 Healthcare AI and Covid-19 Workshop [preprint]

**Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models**

J. J. Thiagarajan, R. Anirudh, P. Bremer, T. German, S. Del Valle, F. Steritz

ICML 2022 Healthcare AI and Covid-19 Workshop [preprint]

**Machine Learning Methods for Autism Spectrum Disorder Classification**

R. Anirudh, J. J. Thiagarajan

Neural Engineering Techniques for Autism Spectrum Disorder [chapter]

**Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection**

S. Rao, V. Narayanaswamy, M. Esposito, J. J. Thiagarajan, A. Spanias

International Conference on ISSA 2021 [paper]

**College Life is Hard!-Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis**

T. Islam, P. W. Liang, F. Sweeney, C. Pragner, J. J. Thiagarajan, M. Sharmin, S. Ahmed

Computers, Software, and Applications Conference 2021 [paper]

**Attend and Diagnose: Clinical Time Series Analysis using Attention Models**

H. Song, D. Rajan, J. J. Thiagarajan, A. Spanias

AAAI 2018 [paper]

**Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data**

R. Anirudh, J. J. Thiagarajan, P. Bremer, H. Kim

SPIE Medical Imaging 2016 [paper]

**Measuring Glomerular Number from Kidney MRI Images**

J. J. Thiagarajan, K. N. Ramamurthy, B. Kanberoglu, D. Frakes, K. Bennett, A. Spanias

SPIE Medical Imaging 2016 [paper]

**Kernel Sparse Models for Automated Tumor Segmentation**

J. J. Thiagarajan, K. N. Ramamurthy, D. Rajan, A. Spanias, A. Puri, D. Frakes

International Journal on Artificial Intelligence Tools [paper]

**Automated tumor segmentation using kernel sparse representations**

J. J. Thiagarajan, D. Rajan, K. N. Ramamurthy, D. Frakes, A. Spanias

International Conference on Bioinformatics and Bioengineering 2013 [paper]

[Top]

### AI4Science

**A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane**

H. Bhatia, J. J. Thiagarajan, R. Anirudh, T. S. Jayram, T. Oppelstrup, H. Ingolfsson, F. LIghtstone, P. Bremer

Machine Learning Science and Technology [paper]

**2022 Review of Data-Driven Plasma Science**

S. Hamaguchi et al.

[preprint]

**Enabling Machine Learning-Ready HPC Ensembles with Merlin**

J. Peterson et al.

Future Generation Computer Systems [paper]

**Suppressing simulation bias in multi-modal data using transfer learning**

B. Kustowski, J. Gaffney, B. Spears, G. Anderson, R. Anirudh, P. Bremer, J. J. Thiagarajan, M. Kruse, R. Nora

Machine Learning: Science and Technology [paper]

**Geometric Priors for Scientific Generative Models in I**CF

A. Shukla, R. Anirudh, E. Kur, J. J. Thiagarajan, P. Bremer, P. Turaga, B. Spears, T. Ma

Neurips 2021 Workshop on Machine Learning for Physical Sciences [preprint]

**Accelerating the rate of discovery: toward high-repetition-rate HED science**

T. Ma et al.

Plasma Physics and Controlled Fusion [paper]

**Comparative Code Structure Analysis using Deep Learning for Performance Prediction**

T. Ramadan, T. Z. Islam, C. Phelps, N. Pinnow, J. J. Thiagarajan

IEEE ISPASS 2021 [paper]

**The Case of Performance Variability on Dragonfly-based Systems**

A. Bhatele, J. J. Thiagarajan, T. Groves, R. Anirudh, S. Smith, B. Cook, D. Lowenthal

IEEE IPDPS 2020 [paper]

**Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion**

R. Anirudh, J. J. Thiagarajan, S. Liu, P-T. Bremer, B. Spears

Neurips 2019 Workshop on Machine Learning for Physical Sciences [paper]

**Performance optimality or reproducibility: that is the question**

T. Patki, J. J. Thiagarajan, A. Ayala, T. Islam

Supercomputing 2019 [paper]

**Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion**

B. Kustowski, J. Gaffney, B. Spears, G. Anderson, J. J. Thiagarajan, R. Anirudh

IEEE Transactions on Plasma Science [paper]

**Parallelizing Training of Deep Generative Models on Massive Scientific Datasets**

S. A. Jacobs, B. Essen, D. Hysom, J. Yeom, T. Moon, R. Anirudh, J. J. Thiagarajan, S. Liu, P. Bremer, J. Gaffney, T. Benson, P. Robinson, L. Peterson, B. Spears

IEEE CLUSTER 2019 [paper]

**Deep learning: A guide for practitioners in the physical sciences**

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, K. Humbird

Physics of Plasmas [paper]

**Mitigating Inter-Job Interference Using Adaptive Flow-Aware Routing**

S. Smith, C. Cromey, D. Lowenthal, J. Domke, N. Jain, J. J. Thiagarajan, A. Bhatele

Superomputing 2018 [paper]

**Cyclically Consistent Adversarial Networks for Reliable Surrogates in Intertial Confinement Fusion**

R. Anirudh, J. J. Thiagarajan, P. Bremer, B. Spears

UQ SciML 2018

**Topology-Driven Analysis and Exploration of High-Dimensional Models**

S. Liu, K. Humbird, L. Peterson, J. J. Thiagarajan, B. Spears, P-T. Bremer

UQ SciML 2018

[Top]

### Sparse Representations

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]

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. Thiagarajan. *Computing persistent homology under random projection*. IEEE Workshop on Statistical Signal Processing. [paper]

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]

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]

P. Sattigeri, J. J. Thiagarajan*, *K. 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]

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*.

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

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. *Optimality and stability of the K-hyperline clustering algorithm*. Pattern 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]

J. J. Thiagarajan, K. N. Ramamurthy and A. Spanias. *Multilevel dictionary learning for sparse representation of images*. 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*.

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.

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.

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*. .

K. N. Ramamurthy, J. J. Thiagarajan and A. Spanias. *Template Learning using Wavelet Domain Statistical Models*. Research 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.

J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias. *Template Learning using Wavelet Domain Statistical Models*. Research 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]

[Top]