AI Foundational Optimizations

This project focuses on advancing the efficiency, scalability, and reliability of core AI models and algorithms. By optimizing training methods, model architectures, and computational processes, it drives the development of robust and high-performance AI systems capable of adapting to dynamic and data-intensive tasks.

People

Dr. Sanjay Chawla

Chief Scientist

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Dr. Mohamed M. Hefeeda

Research Director, QCAI

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Dr. Anas A. Al-Nuaimi

Senior Scientist

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Dr. Michaël J. Aupetit

Senior Scientist

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Dr. Abdelkader Baggag

Senior Scientist

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Dr. Mohammad Amin Sadeghi

Senior Scientist

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Keivin Isufaj

Software Engineer

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Johanne G. Medina

Research Associate

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Igli Mlloja

Research Assistant

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Publications

Deep learning, transformers and graph neural networks: a linear algebra perspective

Abdelkader Baggag, Yousef Saad
Numerical Algorithms (2025)

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)

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)

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)