About
I’m a principal scientist with the Amazon AGI Science team in Sunnyvale, California, where I build customization solutions for Nova foundation models. I have 10+ years of research experience in multimodal foundation models, visual generation, and advancing AI through effective, robust and safe post-training methods. My work has driven high-impact AI innovations across consumer products, science, healthcare, and security. Prior to Amazon, I worked at Apple and LLNL. I completed my PhD at Arizona State University and am a recipient of the DoE/LLNL Early Career Researcher Award.
My past projects include Apple Intelligence, AI-powered workflows for inertial confinement fusion, Anchored Networks, Generative AI for CT Imaging, Multimodal AI for Security, Knowledge-driven healthcare AI and MedPerf.
Research
The Surprising Utility of Group Partitioning in Improving Conformal Prediction of Visual Classifiers under Distributional Shifts
K. Thopalli, V. Narayanaswamy, J. J. Thiagarajan
CVPR Uncertainty Quantification for Computer Vision Workshop 2025 [paper]
Conformal Edge-Weight Prediction in Latent Space
A. Choudhuri, Y. Zhong, M. Moharrami, C. Klymko, M. Heimann, J. J. Thiagarajan, B. Adhikari
SIAM Data Mining 2025 [paper]
Structure-Aware Representation Learning for Effective
Performance Prediction
T. Ramadan, N. Pinnow, C. Phelps, J. J. Thiagarajan, T. Islam
Concurrency and Computation: Practice and Experience [paper]
On The Role of Prompt Construction In Enhancing Efficacy
and Efficiency of LLM-Based Tabular Data Generation
B. Banday, K. Thopalli, T. Islam, J. J. Thiagarajan
IEEE ICASSP 2025 [paper]
Speeding Up Image Classifiers with Little Companions
Y. Liu, K. Thopalli, J. J. Thiagarajan
[preprint]
On the Use of Anchoring for Training Vision Models
V. Narayanaswamy, K. Thopalli, R. Anirudh, Y. Mubarka, W. Sakla, J. J. Thiagarajan
NeurIPS 2024 [paper] [project page] [code] Spotlight
Toward digital design at the exascale: An overview of project ICECap
J. Luc Peterson et al.
Physics of Plasmas 2024 [paper]
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
M. Olson, S. Liu, J. J. Thiagarajan, B. Kustowski, W. Wong, R. Anirudh,
MLST 2024 [paper]
A Stochastic Centering Framework for Improving Calibration in Graph Neural Networks
P. Trivedi, M. Heimann, R. Anirudh, D. Koutra, J. J. Thiagarajan
ICLR 2024 [paper] [project page]
On Estimating Link Prediction Uncertainty Using Stochastic Centering
P. Trivedi, D. Koutra, J. J. Thiagarajan
ICASSP 2024 [paper]
The Double-Edged Sword of AI Safety: Balancing Anomaly Detection and OOD Generalization via Model Anchoring
V. Narayanaswamy, R. Anirudh, J. J. Thiagarajan
ICASSP 2024 [paper]
Exploring the Utility of CLIP Priors for Visual Relationship Prediction
R. Subramanyam, T. S. Jayram, R. Anirudh, J. J. Thiagarajan
ICASSP 2024 [paper]
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
J. Liu, R. Anirudh, J. J. Thiagarajan, S. He, A. Mohan, U. Kamilov, H. Kim
ICCV 2023 [paper] [project page]
InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection
K. Thopalli, S. Devi, J. J. Thiagarajan
ICCV 2023 Workshop on Visual Inductive Priors for Data-Efficient Deep Learning [paper] Oral
Exploring Inlier and Outlier Specification for Improved Medical OOD Detection
V. Narayanaswamy, Y. Mubarka, R, Anirudh, D. Rajan, J. J. Thiagarajan
ICCV 2023 Workshop on Uncertainty Quantification for Computer Vision
[paper]
2022 Review of Data-Driven Plasma Science
S. Hamaguchi et al.
IEEE Transactions on Plasma Science 2023 [paper]
SiSTA: Target-Aware Generative Augmentations for Single-Shot Adaptation
K. Thopalli, R. Subramanyam, P. Turaga, J. J. Thiagarajan
ICML 2023 [paper] [project page] [code]
Adapting Blackbox Generative Models via Inversion
S. Mitra, R. Subramanyam, R.Anirudh, J. J. Thiagarajan, A. Shukla, P. Turaga
ICML 2023 Workshop on Challenges in Deployable AI
[paper]
On Estimating the Epistemic Uncertainty of Graph Neural Networks using Stochastic Centering
P. Trivedi, M. Heimann, R. Anirudh, D. Koutra, J. J. Thiagarajan
ICML 2023 Workshop on Data-Centric Machine Learning
[paper]
Characterizing Risk Regimes for Safe Deployment of Deep Regression Models
J. J. Thiagarajan, V. Narayanaswamy, R. Anirudh, P. Trivedi
ICML 2023 Workshop on Data-Centric Machine Learning
[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] [project page] [code] Oral
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 [paper]
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
P. Trivedi, D. Koutra, J. J. Thiagarajan
ICLR 2023 [paper] Spotlight
A Closer Look at Scoring Functions and Generalization Prediction
P. Trivedi, D. Koutra, J. J. Thiagarajan
IEEE ICASSP2023 [paper]
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]
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 2022 [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 Oral
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]
Single Model Uncertainty Estimation via Stochastic Data Centering
J. J. Thiagarajan, R. Anirudh, V. Nayaranaswamy, P. Bremer
Neurips 2022 [paper] [code] Spotlight
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] Oral
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
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
Uncertainty-Driven Counterfactual Explainers for CXR-Based Diagnosis Models
K. Thopalli, D. Rajan, P. Turaga, J. J. Thiagarajan
ICML2022 Interpretable AI in Healthcare Workshop
Sparsity Improves Unsupervised Attribute Discovery in StyleGAN.
S. Liu, R. Anirudh, J. J. Thiagarajan, P. Bremer
IEEE ICASSP 2022 [paper]
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 [paper]
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 [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 2022 [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 2022 [paper]
Enabling Machine Learning-Ready HPC Ensembles with Merlin
J. Peterson et al.
Future Generation Computer Systems 2022 [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 2022 [paper]
Designing Counterfactual Generators using Deep Model Inversion
J. J. Thiagarajan, V. Narayanaswamy, D. Rajan, J. Liang, A. Chaudhary, A. Spanias
Neurips 2021 [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]
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]
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]
Attribute-Guided Adversarial Training for Robustness to. Natural Perturbations
T. Gokhale, R.Anirudh, J. J. Thiagarajan, B. Kailkhura, C. Baral, Y. Yang
AAAI 2021 [paper]
Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks
U. Shanthamallu, J. J. Thiagarajan, A. Spanias
AAAI 2021 [paper]
Accurate and Robust Feature Importance Estimation Under Distribution Shifts
J. J. Thiagarajan, V. Narayanaswamy, R. Anirudh, P. Bremer
AAAI 2021 [paper] Spotlight
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 [paper] Best Paper Award
Using Deep Image Priors to Generate Counterfactual Explanations
V. Narayanaswamy, J. J. Thiagarajan, A. Spanias
IEEE ICASSP 2021 [paper]
MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis
R. Anirudh, J. J. Thiagarajan, R. Sridhar, P. Bremer
Frontiers in Big Data 2021 [paper]
Machine Learning Methods for Autism Spectrum Disorder Classification
R. Anirudh, J. J. Thiagarajan
Neural Engineering Techniques for Autism Spectrum Disorder 2021 [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]
Accelerating the rate of discovery: toward high-repetition-rate HED science
T. Ma et al.
Plasma Physics and Controlled Fusion 2021 [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]
MimicGAN: Corruption-Mimicking for Blind Image Recovery and Adversarial Defense
R. Anirudh J. J. Thiagarajan, B. Kailkhura, P. Bremer
IJCV Special Issue on GANs 2020 [paper]
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 2020 [paper] Editor's Spotlight
Learn-by-Calibrating: Using Calibration as a Training Objective
J. J. Thiagarajan, B. Venkatesh, D. Rajan
IEEE ICASSP 2020 [paper]
A Regularized Attention Mechanism for Graph Attention Networks
U. Shanthamallu, J. J. Thiagarajan, A. Spanias
IEEE ICASSP 2020 [paper]
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 2020 [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 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]
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 2019 [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 2019 [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]
Solving Inverse Problems in Geophysics Using Machine Learning
Y. Lin, Y. Wu, S. Wang, J. J. Thiagarajan, G. Guthrie, D. Coblentz
SIAM Annual Meeting 2018 [paper] Oral
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram
R. Anirudh, H. Kim, J. J. Thiagarajan, K. Champley, P. Bremer
CVPR 2018 [paper] Spotlight
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] Oral
Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections
J. J. Thiagarajan, S. Liu, K. N. Ramamurthy, P. Bremer
Computer Graphics Forum 2018 [paper]
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
H. Song, D. Rajan, J. J. Thiagarajan, A. Spanias
AAAI 2018 [paper] Oral
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 2018 [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] Oral
Cyclically Consistent Adversarial Networks for Reliable Surrogates in Inertial 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
Optimizing Kernel Machines Using Deep Learning
H. Song, J. J. Thiagarajan, P. Sattigeri, A. Spanias
IEEE Transactions on Neural Networks and Learning Systems 2018 [paper]
Triplet Network with Attention for Speaker Diarization
H. Song, J. J. Thiagarajan, M. Willi, V. Berisha, A. Spanias
Interspeech 2018 [paper] Oral
A deep learning approach to multiple kernel fusion
H. Song, J. J. Thiagarajan, P. Sattigeri, K. N. Ramamurthy, A. Spanias
IEEE ICASSP 2017 [paper] Oral
Learning Robust Representations for Computer Vision
P. Zheng, A. Aravkin, K. N. Ramamurthy, J. J. Thiagarajan
IEEE ICCV Workshops 2017 [paper] Oral
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] Oral
Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization
R. Anirudh, B. Kailkhura, J. J. Thiagarajan, P. Bremer
CVPR 2017 Workshops [paper] Oral
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 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 2016 [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]
Stair Blue Noise Sampling
B. Kailkhura, J. J. Thiagarajan, P. Bremer, P. K. Varshney
Siggraph Asia 2016 [paper] Oral
Consensus inference on mobile phone sensors for activity recognition
H. Song, J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias
IEEE ICASSP 2016 [paper] Oral
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]
Theoretical guarantees for poisson disk sampling using pair correlation function
B. Kailkhura, J. J. Thiagarajan, P. Bremer, P. K. Varshney
IEEE ICASSP 2016 [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 2016 [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] Oral
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 2015 [paper]
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning
J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias
IEEE Transactions on Image Processing 2014 [paper]
Learning Stable Multilevel Dictionaries for Sparse Representations
J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias
IEEE Transactions on Neural Networks and Learning Systems 2014 [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]
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]
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 2014 [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 2014 [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 2014 [paper]
Learning dictionaries with graph embedding constraints
K. N. Ramamurthy, J. J. Thiagarajan, P. Sattigeri, A. Spanias
Asilomar Conference 2013 [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]
Optimality and stability of the K-hyperline clustering algorithm
J. J. Thiagarajan, K. N. Ramamurthy, A. Spanias
Pattern Recognition Letters 2011 [paper]
Automated Domain Discovery for Domain Generalization
On-the-Fly Object Detection using StyleGAN with Clip Guidance
Towards Reliable and Interpretable Clinical Predictive Models
Heteroscedastic Calibration of Uncertainty Estimators
High-Dimensional Spectral Sampling
Calibrate and Prune
ROPE: Recoverable Order-Preserving Embeddings
Automatic Inference of the Quantile Parameter
Time series analysis using attention models
Customizing kernel machines with deep neural networks
Unsupervised audio source separation using generative priors
Universal image representation based on a multimodal graph
Audio source separation via multi-scale feature learning
Mimicking of corruption in images
Supra-fusion graph attention model
A triplet network with attention for speaker diarization
Multi-scale deep learning system
Identification of uncommon objects in containers
Measuring glomerular number from kidney mri images
Kernel sparse models for automated tumor segmentation
Ensemble sparse models for image analysis and restoration
Robust Adaptation and Proactive Failure Detection
Knowledge-Aware Deep Learning
Grounding Deep Models via Uncertainty Characterization
Building Machine Learning Models with Scientific Data
Towards Data-Efficient, Grounded andSafe Deep Models
Reliable and Interpretable Clinical Models
Data Skeptic Podcast on Interpretability, AI4Health
Interpretable AI for healthcare
Unsupervised Learning Lectures [Part 1] [Part 2] [Part 3]
Jay Thiagarajan 