Yuqi Tan

Advances in spatial biology have opened unprecedented opportunities to study tissue organization at cellular and molecular resolution. However, the complexity and scale of these datasets require innovative computational approaches. In this talk, I will highlight how AI-driven methods are transforming 2D and 3D spatial biology by enabling scalable, explainable analysis of tissue architecture. I will also discuss how these approaches accelerate discovery in homeostasis and cancer, paving the way for predictive and clinically impactful insights.

Yuqi Tan is a computational biologist specializing in spatial multi-omics, integrating expertise in machine learning with a deep understanding of molecular and cellular biology. Her research focuses on developing scalable machine learning models and streamlined computational frameworks to address complex biological questions, including improving stem cell engineering, identifying novel cell types, discovering spatial biomarkers for cancer immunotherapy response, and uncovering spatial mechanisms in neurological disease. She has led and continues to lead research teams in developing scalable machine learning methods to study the spatial organization of cell types in healthy aging and disease, leveraging spatial multi-omics and 3D pathology. Through mentoring nine students, designing and teaching three custom courses, and contributing to three global health projects, Dr. Tan is committed to fostering a diverse, equitable, and inclusive scientific environment that empowers the next generation of researchers.