My projects in explainable AI aims to make today’s powerful models genuinely understandable to humans, and to use those explanations to advance machine learning for scientific discovery.
2025 to Present
From Interpretability to Explainability
Explaining the model training procedure that transforms interpretation into explanation.
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2024 to Present
Interpretable TCR-Epitope Prediction
Interpretable models for predicting and interpreting how T-cell receptors recognize peptide-MHC complexes.
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2024 to Present
Antigen Processing Prediction Acceleration
GPU-accelerated modeling of antigen processing to predict CD4+ T-cell epitopes.
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2024 to Present
Brain-Vision Decoding
Understanding the neural mechanisms underlying visual recognition and information decoding. (Collaborative project with Zach and Prof. Ding.)
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