AI Foundational Optimizations
Peer-reviewed research from QCAI, published in leading international venues.
2026
3 publicationsAI Foundational Optimizations2026
Rapid disaster damage assessment using deep adversarial sliced Wasserstein domain adaptation
Fatma AlNaimi, Abdulaziz Al-Homaid, Ferda Ofli, Abdelkader Baggag — Neural Computing and Applications (2026)
AI Foundational Optimizations2026
Particles Don’t Care About Z: Towards Scaling Entropy Estimation of Unnormalized Densities
Safa Messaoud, Skander Charni, Elaa Bouazza, Ali Pourghasemi, Halima Bensmail — ICML (2026)
AI Foundational Optimizations2026
ISilDR: Isometric Seriation-based Dimensionality Reduction for Visual Cluster Analysis
Rene Cutura, Sophie Sadler, Quynh Quang Ngo, Michaël Aupetit, Michael Sedlmair — IEEE Transactions on Visualization and Computer Graphics (2026)
2025
7 publicationsAI Foundational Optimizations2025
Uncertainty-Aware LLMs Fail to Flag Misleading Contexts
Tianyi Zhou, Johanne Medina, Sanjay Chawla — NeurIPS 2025 – Reliable ML Workshop (2025)
AI Foundational Optimizations2025
Explaining the role of Intrinsic Dimensionality in Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Hassan Sajjad, Sanjay Chawla — ICML (2025)
AI Foundational Optimizations2025
Deep learning, transformers and graph neural networks: a linear algebra perspective
Abdelkader Baggag, Yousef Saad — Numerical Algorithms (2025)
AI Foundational Optimizations2025
Distortion-aware Brushing for Reliable Cluster Analysis in Multidimensional Projections
Hyeon Jeon, Michael Aupetit, Soohyun Lee, Kwon Ko, Youngtaek Kim, Ghulam Jilani Quadri — IEEE Transactions on Visualization and Computer Graphics (2025)
AI Foundational Optimizations2025
Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
Tianyi Zhou, Johanne Medina, Sanjay Chawla Proceedings of the AAAI Conference on Artificial Intelligence — Proceedings of the AAAI Conference on Artificial Intelligence (2025)
AI Foundational Optimizations2025
ArnoldiGCL: Graph Contrastive Learning via Learnable Arnoldi-Based Guided Spectral Chebyshev Polynomial Filters
Mustafa Coşkun, Abdelkader Baggag, Mehmet Koyutürk — Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2025)
AI Foundational Optimizations2025
Measuring the Validity of Clustering Validation Datasets
Hyeon Jeon, Michael Aupetit, DongHwa Shin, Aeri Cho, Seokhyeon Park, Jinwook Seo — IEEE Transaction on Pattern Analysis and Machine Intelligence (2025)
2024
2 publicationsAI Foundational Optimizations2024
S²AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
Safa Messaoud, Billel Mokeddem, Zhenghai Xue, Linsey Pang, Bo An, Haipeng Chen, Sanjay Chawla — ICLR (2024)
AI Foundational Optimizations2024
A pragmatic perspective on AI transparency at workplace
Ghanim Al-Sulaiti, Mohammad Amin Sadeghi, Lokendra Chauhan, Ji Lucas, Sanjay Chawla, Ahmed Elmagarmid — AI and Ethics (2024)
2023
1 publicationsAI Foundational Optimizations2023
Classes are Not Clusters: Improving Label-Based Evaluation of Dimensionality Reduction
Hyeon Jeon, Yun-Hsin Kuo, Michael Aupetit, Kwan-Liu Ma, Jinwook Seo — IEEE Transactions on Visualization and Computer Graphics (2023)
2020
1 publicationsAI Foundational Optimizations2020
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Benoît Colange, Jaakko Peltonen, Michael Aupetit, Denys Dutykh, Sylvain Lespinats — Proceedings of NeurIPS (2020)
2019
1 publicationsAI Foundational Optimizations2019
Toward Perception-Based Evaluation of Clustering Techniques for Visual Analytics
Michael Aupetit, Michael Sedlmair, Mostafa M. Abbas, Abdelkader Baggag, Halima Bensmail — Proceedings of the IEEE Visualization Conference (2019)
2018
1 publicationsAI Foundational Optimizations2018
Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment
Luis Gustavo Nonato, Michael Aupetit — IEEE Transactions on Visualization and Computer Graphics (2018)