xAI
Date:
As I advanced through my studies, I became deeply engaged in machine learning (ML). This experience has honed my coding skills and enabled me to leverage ML to tackle complex problems. It has also highlighted the potential of combining ML with biological systems to create innovative solutions. During an internship, I worked on Explainable AI (XAI) and explored ways to explain the decisions of Graph Neural Networks (GNNs) for cancer prediction using SHAP (Shapley) values. This hands-on experience with XAI further deepened my understanding of how advanced ML models can be made more interpretable for critical applications in healthcare. Because we haven’t published yet, can’t explain more.