• Collaborated with a Dubai-based client to develop a highly accurate and interpretable deep learning model for predicting COVID-19 mortality rates and ICU-admission likelihood in time-bound and resource-limited scenarios.

• Published results and the model-building process in a high-impact medical journal, showcasing its potential to aid frontline doctors in classifying patients in time-bound and resource-limited scenarios.

• Executed extensive data pre-processing and cleaning, employing diverse techniques to handle missing and unbalanced data, ensuring the model's robustness.

• Constructed the model using PyTorch and fine-tuned it using advanced techniques like early stopping, learning rate scheduling, and hyperparameter experimentation, achieving high accuracy and generalization performance.

• Analyzed the model's behavior, identifying influential features contributing to predictions, enabling doctors to comprehend underlying factors leading to patients' ICU-admission and mortality risks.