My primary fascination revolves around graph-centric and data-driven research, with a particular emphasis on establishing trust in data analysis. I am deeply drawn to the potential of Graph Neural Networks (GNNs) not just for uncovering profound structures and patterns, but also for enhancing the trustworthiness of data interpretations. Having reliable, accessible, and systematically organized data is pivotal, and I envision GNNs as instrumental in achieving this. In my pursuits, I aim to amalgamate the intricacies of data management, mining, and machine learning to develop methods that underscore transparency, fairness, reliability, and robustness in algorithmic evaluations. The ultimate objective is to transcend the convolutions of data narratives, enabling decision-makers to interpret data with heightened accuracy and assurance. By embracing both the art and science of data, I am committed to bridging the chasm between multifaceted data landscapes and human comprehension, fostering an era where trust in data-driven insights becomes second nature.

Please reach out to collaborate 😃