Deep Learning Research
Deep Learning Research
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Explainability
Explainability
Model attribution
Integrated measures
Kernel formulations
Papers: ICPR 2024 (1), ICPR 2024 (2), ICML-W 2023, ICML-W 2023
Optimizations in Training
Optimizations in Training
Data reduction
Network optimization
[SPIE 2026]
Computer Vision Research
Computer Vision Research
Robustness & Adversarial Attacks
Robustness & Adversarial Attacks
Reconstructive training for image classification tasks.
Papers: SADM 2025, WACV-W 2022, ISVC 2019
Other applications in 3D objects: ICIP 2022
Eye Tracking for VR/AR
Eye Tracking for VR/AR
Semantic segmentation of images and video.
Papers: ECCV-W 2020 [video], ICCV-W 2019
Other applications in VR/AR: VR 2023, NLPIR 2024
Applications in the Physical Sciences
Applications in the Physical Sciences
Deep Learning for Nuclear Materials
Deep Learning for Nuclear Materials
Ongoing collaborative work with UTSA Physics, UTSA Engineering, Los Alamos National Lab, Argonne National Lab, UNLV, St. Mary’s.
Deep neural networks for semantic segmentation of micrographs and images of nuclear fuel forms during the fabrication process.
Papers: [SPIE 2026] CVPR-W 2025 [2], IEEE Access 2023
Datasets: Graphite-23 [IEEE Dataport]
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