Publications (By Topic)

A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
P. Trivedi, D. Koutra, J. J. Thiagarajan
ICLR 2023 [paper]

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
V. Narayanaswamy, Y. Mubarka, R.Anirudh, D. Rajan, A. Spanias, J. J. Thiagarajan
MIDL 2023 [paper]

A Closer Look at Scoring Functions and Generalization Prediction
P. Trivedi, D. Koutra, J. J. Thiagarajan
IEEE ICASSP2023 [preprint]

Single-Shot Domain Adaptation via Target-Aware Generative Augmentations
R. Subramanyam, K. Thopalli, S. Berman, P. Turaga, J. J. Thiagarajan
IEEE ICASSP 2023 [preprint] [code]

The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation
K. Thopalli, R. Anirudh, P.Turaga, J. J. Thiagarajan
IEEE Access 2023 [paper]

Enhanced Multi-Modal Latent Spaces in High-Energy Density Physics with the Relaxed Poincare Prior
A. Shukla, Y. Mubarka, R. Anirudh, J. J. Thiagarajan, E. Kur, D. Mariscal, B. Djordjevic, B. Kustowski, K. Swanson, B. Spears, T. Bremer, T. Ma, P. Turaga
2023 [preprint]

InterAug: Context-Aware Augmentations for Data-Efficient Object Detection
S. Devi, K. Thopalli, R. Dayana, P. Turaga, J. J. Thiagarajan
2023 [preprint]

Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
K. Thopalli, S. Katoch, P. Turaga, J. J. Thiagarajan
2023 [preprint] [code]

On-the-Fly Object Detection using StyleGAN with Clip Guidance
Y. Lu, S. Liu, J. J. Thiagarajan, W. Sakla, R.Anirudh
2023 [preprint]

Invert-to-Adapt: Data-Efficient GAN Transfer to OOD Data
S. Mitra, R.Anirudh, J. J. Thiagarajan, A. Shukla, P. Turaga
2023 [preprint]

Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
R. Subramanyam, M. Heimann, T. S. Jayram, R.Anirudh, J. J. Thiagarajan
WACV 2023 [preprint] [code]

Improving Diversity with Adversarially Learned Transformations for Domain Generalization
T. Gokhale, R.Anirudh, J. J. Thiagarajan, B. Kailkhura, C. Baral, Y. Yang
WACV 2023 [preprint] [code]

Out of Distribution Detection with Neural Network Anchoring
J. J. Thiagarajan, R. Anirudh
ACML 2022 [preprint] [code]

Domain Alignment Meets Fully Test-Time Adaptation
K. Thopalli, P. Turaga, J. J. Thiagarajan
ACML 2022 [preprint] [code]

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]

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]

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]

Revisiting 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]

Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation
J. J. Thiagarajan, S. Kashyap, A. Karagyris
IEEE ICMLA 2020 [paper] [slides]

Unsupervised Dimension Selection using a Blue Noise Spectrum
J. J. Thiagarajan, R. Anirudh, R. Sridhar, P. Bremer
IEEE ICASSP 2020 [paper] [poster]

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

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

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]

Subspace 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]

Single Model Uncertainty Estimation via Stochastic Data Centering
J. J. Thiagarajan, R. Anirudh, V. Nayaranaswamy, P. Bremer
Neurips 2022 [paper]

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]

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

Heteroscedastic 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]

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
J. J. Thiagarajan, B. Venkatesh, P. Sattigeri, P. Bremer
AAAI 2020 [preprint] [poster]

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

Analyzing Data-Centric Properties for Contrastive Learning on Graphs
P. Trivedi, M. Heimann, E. Lubana, D. Kotura, J. J. Thiagarajan
Neurips 2022 [preprint] [code]

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]

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

Improving Deep Embeddings for Inferencing with Multi-Layered Graphs
H. Song, J. J. Thiagarajan
IEEE Big Data 2019 [paper] [poster]

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

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

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]

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images
R. Subramayam, V. Narayanaswamy, M. Naufel, A. Spanias, J. J. Thiagarajan
ICML 2022 [paper] [code]

Designing Counterfactual Generators using Deep Model Inversion
J. J. Thiagarajan, V. Narayanaswamy, D. Rajan, J. Liang, A. Chaudhary, A. Spanias
Neurips 2021 [paper]

On the Design of Deep Priors for Unsupervised Audio Restoration
V. Narayanaswamy, J. J. Thiagarajan, A. Spanias
Interspeech 2021 [paper] [code]

Using Deep Image Priors to Generate Counterfactual Explanations
V. Narayanaswamy, J. J. Thiagarajan, A. Spanias
IEEE ICASSP 2021 [paper]

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

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]

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

Designing Deep Inverse Models for History Matching in Reservoir Simulations
V. Narayanaswamy, J. J. Thiagarajan, R. Anirudh, F. Forouzanfar, P. Bremer, X. Wu
Neurips 2019 Workshop on Solving Inverse Problems with DL [paper] [poster]

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]

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]

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

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]

Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
M. Olson, R. Anirudh, J. J. Thiagarajan, W. Wong, P. Bremer. S. Liu
CVPR 2023 [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]

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

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]

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

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

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]

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]

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

Improving Reliability of Clinical Models using Prediction Calibration
J. J. Thiagarajan, B. Venkatesh, D. Rajan, P. Sattigeri
MICCAI 2020 UNSURE Workshop [paper]

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

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]

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 ICF
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]

Improved Surrogates in Intertial Confinement Fusion with Manifold and Cycle Consistencies
R. Anirudh, J. J. Thiagarajan, P. Bremer, B. Spears
PNAS [paper] [code]

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

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

PADDLE: Performance Analysis Using a Data-Driven Learning Environment
J. J. Thiagarajan, R. Anirudh, B. Kailkhura, N. Jain, T. Islam, A. Bhatele, J. Yeom, T. Gamblin
IEEE IPDPS 2018 [paper]

Performance modeling under resource constraints using deep transfer learning
A. Marathe, R. Anirudh, N. Jain, A. Bhatele, J. J. Thiagarajan, B. Kailkhura, J. Yeom, T. Gamblin
Supercomputing 2017 [paper]

Towards real-time geologic feature detection from seismic measurements using a randomized machine-learning algorithm
Y. Lin, S. Wang, J. J. Thiagarajan
Society of Exploration Geophysicists Meeting 2017 [paper]

Data-Driven Metric Learning for History Matching
J. Miller, J. J. Thiagarajan, P. Bremer, N. Hoda, D. Stern, R. Mifflin
SPE Reservoir Simulation Conference 2017 [paper]

Data-driven performance modeling of linear solvers for sparse matrices
J. Yeom, J. J. Thiagarajan, A. Bhatele, G. Bronevetsky, T. Kolev
Supercomputing 2016 Workshops [paper]

A Machine Learning Framework for Performance Coverage Analysis of Proxy Applications
T. Islam, J. J. Thiagarajan, A. Bhatele, M. Schulz, T. Gamblin
Supercomputing 2016 [paper]

Identifying the Culprits Behind Network Congestion
A. Bhatele, A. Titus, J. J. Thiagarajan, N. Jain, T. Gamblin, P. Bremer, M. Schulz, L. Kale
IEEE IPDPS 2015 [paper]