Scholarly Products

[Recent] [IMAGE 2024] [Journals] [Conferences] [Editorial] [Books] [Patents] [Presentations] [Keynote Speeches] [Tutorials] [All]

Journals

P. Chowdhury, A. Mustafa, M. Prabhushankar and G. AlRegib, “A Unified Framework for Evaluating Robustness of Machine Learning Interpretability for Prospect Risking,” in Geophysics, vol. 90, no. 3, Jan. 3, 2025. [PDF] [Code]
R. Benkert, M. Prabhushankar, and G. AlRegib, “Effective Data Selection for Seismic Interpretation Through Disagreement,” in IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 62, pp. 1-12, Jun. 4, 2024. [PDF]
M. Prabhushankar, and G. AlRegib, “VOICE: Variance of Induced Contrastive Explanations to Quantify Uncertainty in Neural Network Interpretability,” in Journal of Selected Topics in Signal Processing (J-STSP) Special Series on AI in Signal & Data Science, pp. 1-13, May. 23, 2024. [PDF] [Code]
C. Zhou, G. AlRegib, A. Parchami, and K. Singh, “TrajPRed: Trajectory Prediction With Region-Based Relation Learning,” in IEEE Transactions on Intelligent Transportation Systems (T-ITS), vol. 25, no. 8, pp. 9787-9796, Mar. 04, 2024. [PDF] [Code]
G. AlRegib, M. Prabhushankar, K. Kokilepersaud, P. Chowdhury, Z. Fowler, S. Trejo Corona, L. Thomaz, and A. Majumdar, “Ophthalmic Biomarker Detection: Highlights From the IEEE Video and Image Processing Cup 2023 Student Competition,,” in IEEE Signal Processing Magazine, vol. 41, no. 4, pp. 96-104, 2024. [PDF] [Code]

Conference Papers

K. Kokilepersaud, S. Kim, M. Prabhushankar and G. AlRegib, “HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, Feb. 28 – Mar. 4, 2025. [PDF] [Code]
R. Benkert, M. Prabhushankar, and G. AlRegib, “Targeting Negative Flips in Active Learning Using Validation Sets,” in IEEE Conference on Big Data 2024, Washington DC, USA, Dec. 15-18, 2024. [PDF] [Code] [Link]
J. Quesada*, Z. Fowler*, M. Alotaibi, M. Prabhushankar, and G. AlRegib, “Benchmarking Human and Automated Prompting in the Segment Anything Model,” in IEEE Conference on Big Data 2024, Washington DC, USA, Dec. 15-18, 2024. [PDF] [Code]
K. Kokilepersaud, Yavuz Yarici, M. Prabhushankar, and G. AlRegib, “Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships Into the Contrastive Loss,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 27-30, 2024. [PDF] [Code]
Y. Yarici, K. Kokilepersaud, M. Prabhushankar, and G. AlRegib, “Explaining Representation Learning With Perceptual Components,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 27-30, 2024. [PDF] [Code]
E. Ozturk, M. Prabhushankar, and G. AlRegib, “Intelligent Multi-View Test Time Augmentation,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 27-30, 2024. [PDF] [Code]
P. Chowdhury, M. Prabhushankar, G. AlRegib and M. Deriche, “Are Objective Explanatory Evaluation Metrics Trustworthy? An Adversarial Analysis,” in IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 27-30, 2024. [PDF] [Code]
R. Benkert, M. Prabhushankar, and G. AlRegib, “Transitional Uncertainty With Layered Intermediate Predictions,” in 41st International Conference on Machine Learning (ICML), Vienna, Austria, Jul. 21-27, 2024. [PDF] [Code]

Presentations

G. AlRegib, “Inferential Machine Learning: Towards Human-Collaborative Foundation Models,” at Springer Nature Computational Science Seminars, Keynote, Mar. 13, 2025. (doi: 10.52843/cassyni.z4r8dt) [Link]
G. AlRegib, “Computational Visual Explanations: A Case Study in Robustness, Uncertainty, and Intervenability,” at AI Transforming Systems, Dhahran , Saudi Arabia, Keynote, Feb. 5-7, 2025. [PDF]
G. Kaviani, Y. Yarici, M. Prabhushankar, and G. Alregib, “Exploring Human Daily Activity Through a Hierarchical Multimodal Lens,” at WiML Workshop @ NeurIPS 2024, Dec 10 – Dec 15, 2024.
G. AlRegib, “Visual Explanations in AI,” at Colour & Visual Computing Symposium, Gjøvik, Norway, Keynote, Sept. 05-06, 2024. [Link]
E. Ozturk, M. Prabhushankar, and G. AlRegib, “Multi-View Seismic Segmentation With Test-Time Augmentation,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
C. Zhou, M. Prabhushankar, and G. AlRegib, “Generative Modeling of Disagreements for Expertise-Based Seismic Fault Labels,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
J. Quesada, M. Prabhushankar, and G. AlRegib, “Uncertainty-Aware Seismic Fault Annotations Can Reveal Insights Into Label Expertise and Confidence,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
P. Chowdhury, M. Prabhushankar, and G. AlRegib, “Optimizing Prompting for Foundation Models in Seismic Image Segmentation,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
K. Kokilepersaud, M. Prabhushankar, and G. AlRegib, “Integrating Granularity Information Into Seismic Contrastive Learning,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
K. Kokilepersaud, M. Prabhushankar, and G. AlRegib, “Assessing Seismic Data Through Self-Supervised Representational Analysis,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
C. Zhou, M. Prabhushankar, and G. AlRegib, “Interpretational Uncertainty-Based Seismic Fault Labeling With Reduced Expert Annotations,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.
M. Alotaibi, M. Prabhushankar, K. Kokilepersaud, and G. AlRegib, “Improving Seismic Interpretation Accuracy and Efficiency With Human-Machine Collaboration,” at International Meeting for Applied Geoscience and Energy (IMAGE), Houston, TX, Aug. 26-30, 2024.

Keynote Speeches

G. AlRegib, “Inferential Machine Learning: Towards Human-Collaborative Foundation Models,” at Springer Nature Computational Science Seminars, Keynote, Mar. 13, 2025. (doi: 10.52843/cassyni.z4r8dt) [Link]
G. AlRegib, “Computational Visual Explanations: A Case Study in Robustness, Uncertainty, and Intervenability,” at AI Transforming Systems, Dhahran , Saudi Arabia, Keynote, Feb. 5-7, 2025. [PDF]
G. AlRegib, “Visual Explanations in AI,” at Colour & Visual Computing Symposium, Gjøvik, Norway, Keynote, Sept. 05-06, 2024. [Link]

Tutorials

G. AlRegib, M. Prabhushankar, “Inferential Machine Learning: Towards Human-Collaborative Foundation Models,” at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, USA, Feb. 28, 2025. ([ORAL (top 7.56%)]),[PDF] [Link]
G. AlRegib, M. Prabhushankar and X. Wang, “Inferential Machine Learning: Towards Human-Collaborative Vision and Language Models,” at 39th Annual AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, USA, Feb. 26 , 2025. [PDF] [Link]
G. AlRegib and M. Prabhushankar, “Inferential Machine Learning: Towards Human-Collaborative Foundation Models,” at IEEE Conference on Big Data 2024, Washington DC, USA, Dec. 16 , 2024. [PDF] [Link]
G. AlRegib and M. Prabhushankar, “Robust Neural Networks: Towards Explainability, Uncertainty, and Intervenability,” at 7th IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), Aug. 9, 2024. [PDF] [Link]
G. AlRegib and M. Prabhushankar, “Robust Image Understanding: Towards Explainability, Uncertainty, and Intervenability,” at 2024 IEEE International Conference on Multimedia and Expo (ICME), Jul. 15, 2024. [Link]
G. AlRegib and M. Prabhushankar, “Robustness at Inference: Towards Explainability, Uncertainty, and Intervenability,” at IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 17, 2024. [Link]
G. AlRegib, M. Prabhushankar, “Formalizing Robustness in Neural Networks: Explainabilty, Uncertainty, and Intervenability,” at 38th Annual AAAI Conference on Artificial Intelligence (AAAI), Feb. 20, 2024. [PDF] [Link]
G. AlRegib, M. Prabhushankar, “Robust Neural Networks: Towards Explainabilty, Uncertainty, and Intervenability,” at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Jan. 04, 2024. [PDF] [Link]

Non-provisional Patents

G. AlRegib, M. J. Mathew, and D. Temel, “Transfer Learning for Medical Applications Using Limited Data,” No: 62/853,753, granted on Dec. 3, 2024.
Print Friendly, PDF & Email