I’m currently pursuing my M.S. in Computational Biology at Carnegie Mellon University (School of Computer Science) with a strong focus on single-cell data integration, predictive modeling for immunology, and generative AI in biology.
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🎓 Education:
- M.S. Computational Biology – Carnegie Mellon University (2024–2026)
- B.S. Computer Science – New York University, Summa Cum Laude (2020–2024)
- Minors in Biomolecular Science & Mathematics
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Research Highlights:
- γδ T-cell Predictive Modeling: Building models to predict cytotoxicity and expansion potential using single-cell RNA-seq data.
- Benchmarking Integration Pipelines: Extensive experience with Seurat (v4/v5) and Harmony for multi-dataset scRNA-seq integration.
- AI for Healthcare: Designed ML models for Parkinson’s disease detection and heart attack risk prediction.
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Tools & Skills:
Python, C++, R, SQL, Seurat, scikit-learn, AWS, Docker, Kubernetes, MySQL.
- Exploring generative AI + biology, especially how large models can discover preventive therapeutics.
- Developing benchmarking frameworks for rare cell type integration.
- Combining machine learning + computational genomics to uncover patterns in immune responses.
- Benchmarking Rare Cell Integration – Comparing Seurat v4/v5 & Harmony on rare immune cell populations.
- Speech Biomarkers for Parkinson’s Disease – ML models using acoustic speech features (KNN, RF, Naive Bayes).
- Heart Attack Risk Prediction – Logistic Regression, SVM, and Neural Networks for cardiovascular risk factors.