Topics: computational pathology, spatial transcriptomics, gene expression prediction, mixture-of-experts, multimodal learning Skills: Programming Languages: Python; experience with PyTorch preferred Machine Learning: CNNs / vision encoders, mixture-of-experts, multimodal representation learning Data Analysis: handling large-scale histology image patches and gene expression matrices Bioinformatics Knowledge (preferred): familiarity with spatial transcriptomics or scRNA-seq data Difficulty: Advanced Size: Large (350 hours) Mentors: Ziheng Duan (contact person) Project Idea Description Histology imaging is one of the most widely available data modalities in biomedical research and clinical practice, capturing rich morphological information about tissues and disease states.
