Advancing Machine Learning Research for Biological Sciences: We develop next-generation machine learning tools tailored for biological research. Our focus is on improving causality, interpretability, disentanglement, uncertainty quantification, and decision-making in machine learning models to enhance their robustness and scientific utility.

Designing Innovative Computational Tools for Molecular Biology Research: We leverage cutting-edge genetic engineering and high-throughput profiling technologies, such as CRISPR and single-cell RNA sequencing, to study complex diseases and drive drug discovery. By integrating computational methods with experimental biology, particularly in immunology, we aim to make significant advances in understanding cellular processes and disease mechanisms.