After KAUST Academy
Ahmed defended a Master's thesis in computer science at York University in April 2026 and works as an ML Specialist at Vector Institute. Ahmed's benchmark paper SONIC-O1 was accepted at the EMNLP 2026 main conference, and a factuality paper was accepted at ACL 2026 Findings.
Summer project
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.
With KAUST Academy
- Cohort 2023
Artificial Intelligence Specialization
King Abdullah University of Science and Technology · Artificial Intelligence Summer School
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