I am a fifth year PhD student at the department of Biostatistics at University of North Carolina at Chapel Hill supervised by Michael I Love and Didong Li. My recent research interests focus on developing efficient statistical and deep learning methods to address a variety of biological problems and computational challenges in functional genomics, particularly bulk and single-cell multi-omics data, and pertubation experiments(CRISPRi).

Research Interests

My research interests focus on developing efficient statistical learning methods to address a variety of biological problems and computational challenges in genomics and genetics. These challenges typically arise with the high-dimensional data generated by rapidly evolving sequencing technologies, e.g., single-cell RNA-seq (scRNA-seq). With the emergence of these large-scale data, I have been continually motivated to develop tailored statistical models to advance our understanding in cellular heterogeneity, tissue organization, and the underlying mechanisms of various types of cancers. In particular, my work centers on these important areas, (1) Hierarchical Bayesian models to detect Allelic imbalance in single cell RNA-seq data; (2) Hypothesis Testing of Enrichment Analysis; (3) Identifying Regulatory Network from sparse non-uniform time-series omics data; (4) Predicting Intervention Effects in Perturbation Experiments; (5) Fine-mapping for multi-ancestry variants.

key words

1. Deep learning with special focus on NLP, Generative AI
2. Multi-view learning
3. Time series forcasting
4. Pertubation experiments
5. Single-cell experiments

📣 Latest News

For more news, please visit the News page.