In the summer of 2024, I was a machine learning and deep learning research intern at UCSF.

I worked under professor Li Zhang’s group researching how to predict T-cell receptor (TCR) and antigen peptide binding. T-cells play a critical role in the human immune response by binding to antigen peptides to destroy them. There is a wide variety of TCRs which each bind to specific antigens. Being able to accurately predict this binding behavior will help us create more potent and targeted immunotherapies. I worked to compare our group’s innovative deep learning pipeline with traditional machine learning methods and found that our pipeline yieled accurate results with great efficiency.

Our paper “PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning” was published in Briefings in Bioinformatics in July 2025.

See the publication here: https://www.researchgate.net/publication/393977691_PepTCR-Net_prediction_of_multi-class_antigen_peptides_by_T-cell_receptor_sequences_with_deep_learning