I am a PhD candidate in the Joint CMU – University of Pittsburgh PhD Program in Computational Biology, where I work on AI/ML research for immunology in the Das Lab at the Center for Systems Immunology.
My research interests include statistical modeling and deep learning for noisy, high-dimensional biological signals. Currently, I am developing an interpretable variant of attention mechanisms for transformer models.
Research Areas
- Interpretable Machine Learning – Building transparent models that reveal biological mechanisms, not just predictions. My work on SLIDE (Nature Methods) discovers latent factor interactions across biological domains.
- Computational Immunology – Applying ML to multi-omic data (single-cell RNA-seq, spatial transcriptomics, systems serology) to understand immune responses in disease.
- Deep Learning for Sequences – Developing attention-based architectures for biological sequence data with built-in interpretability.
Education
- PhD, Computational Biology – Carnegie Mellon University & University of Pittsburgh (in progress)
- Passed thesis proposal and doctoral candidate exam (October 2023)
Selected Honors
- Research presentation award, University of Pittsburgh Immunology Retreat (2024)
- Quantitative Methodologies Pilot Program (QuMP) grant, co-PI (2023)
Feel free to reach out at xiaoh@pitt.edu.