As a Master’s student in Biomedical Informatics at Xi’an Jiaotong University, affiliated with the Biomedical Informatics and Genomics Center, I am deeply passionate about leveraging computational tools to unravel complex biological systems and advance precision medicine. My research focuses on integrating multi-omics data and developing machine learning models to identify biomarkers for disease diagnosis and treatment.

The Hong Kong Laureate Forum presents an unparalleled opportunity to engage with world-renowned Shaw Laureates and fellow young scientists across disciplines. I am particularly inspired by the work of Prof. Eva Nogales in structural biology and Prof. Peter Walter in cellular signaling, as their research aligns with my interests in understanding biological mechanisms at a molecular level.

Participating in this forum will allow me to gain insights into cutting-edge scientific advancements, foster interdisciplinary collaborations, and refine my research approach. I am eager to contribute my expertise in biomedical informatics and learn from the diverse perspectives of global leaders in science.

This experience will not only enhance my academic journey but also empower me to contribute meaningfully to the scientific community. I am excited to be part of this transformative event and to connect with the next generation of scientific

Xi'an Jiaotong University

Xi'an Jiaotong University

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My research focuses on leveraging biomedical big data and artificial intelligence to advance precision medicine. Specifically, I integrate multi-omics data (genomics, transcriptomics, proteomics, etc.) and develop machine learning models to identify disease susceptibility biomarkers and improve diagnostic and therapeutic strategies. By analyzing high-throughput datasets, I aim to uncover novel molecular signatures associated with complex diseases, such as cancer and cardiovascular disorders. My work emphasizes the development of robust computational pipelines for data preprocessing, feature selection, and model optimization, ensuring the reliability and interpretability of the results. Additionally, I explore the potential of deep learning techniques to enhance the predictive power of these models. Ultimately, my research contributes to the understanding of disease mechanisms and facilitates the translation of omics data into clinically actionable insights, paving the way for personalized healthcare solutions.