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Faisal AlZahrani

After KAUST Academy

Faisal was promoted to Senior Consultant in the Data & AI competency at EY in September 2026. In October 2024, Faisal's team Enjaad won first place at the Ministry of Health's Sehathon. In summer 2024, Faisal completed the Alat-KAUST summer program at the University of Oxford.

Summer projects

Artificial Intelligence Specialization · Cohort 2024

Tactical Target Differentiation

The AF-100 Tactical Target Differentiation project focuses on developing an advanced computer vision system to accurately distinguish between military and civilian objects in real-time during military operations. Utilizing deep learning techniques like Convolutional Neural Networks (CNNs) and Transfer Learning, the system enhances target identification and classification. Object detection algorithms, including YOLO and Faster R-CNN, are employed to ensure rapid and accurate differentiation in various environments. Image segmentation methods, like U-Net, improve detection accuracy, particularly in complex scenes. The project also explores synthetic data generation and domain adaptation to ensure robustness. The system is designed for integration into existing military reconnaissance and

See it in the program report (PDF)

Artificial Intelligence Specialization · Cohort 2023

Addressing Bias: Through Ensemble Learning and Regularized Fine-Tuning

Addressing biases in AI models is crucial for ensuring fair and accurate predictions. However, obtaining large, unbiased datasets for training can be challenging. Our project proposes a comprehensive approach using multiple methods to remove bias in AI models, with only a small dataset and a potentially biased pretrained model. We train multiple models with the counter-bias of the pretrained model through data splitting, local training, and regularized fine-tuning, gaining potentially counter-biased models. Then, we employ ensemble learning for all models to reach unbiased predictions. To further accelerate the inference time of our ensemble model, we conclude our solution with knowledge distillation that results in a single unbiased neural network. We demonstrate the effectiveness of our approach through experiments on the CIFARI0O and HAMIOO00 datasets, showcasing promising results. This work contributes to the ongoing effort to create more unbiased and reliable AI models, even with limited data availability.

See it in the program report (PDF)

With KAUST Academy

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