Projects
Three tools, three scales, one framework — the AI Virtual Cell.
Tumor Microenvironment Variational Inference. An uncertainty-aware decoder virtual instrument that combines scVI cellular representations with a Graph Attention Network and Monte Carlo Dropout to predict immune escape and anti-PD-1 immunotherapy response in melanoma patients.
GNN-based protein mutation effect prediction platform. Predicts how single-point mutations affect protein stability (ΔΔG) and antibody-antigen binding affinity using graph attention over protein structure graphs.
AI research assistant for the AIVC literature. Transforms academic papers into a queryable knowledge graph, enabling researchers to ask natural-language questions and receive cross-paper synthesis powered by a multi-agent RAG system.