I am an Assistant Professor of Biostatistics and Edens Family Assistant Professor of Healthcare Communications and Technology at the Department of Biostatistics and as a core member at the Center for Computational Molecular Biology at Brown University. I obtained my Ph.D. degree in Biostatistics at University of Michigan, Ann Arbor (Advisor: Dr. Xiang Zhou). Previously, I obtained my Bachelor's degree from Nankai University, China. My recent research interests focus on developing efficient statistical learning and AI methods to address a variety of biological problems and computational challenges in genomics and genetics, particularly single-cell RNA-sequencing, and spatially resolved transcriptomics. In addition to my methodological research, I also work on genetic risk prediction analysis for common health exposure traits in large biobanks such as UK Biobank, and the Michigan Genomics Initiative (MGI). Our lab’s research is supported by several grants from the American Cancer Society - Institutional Research Grants (ACS-IRG), the National Institutes of Health (NIH) (The National Institute of General Medical Sciences (NIGMS)), and the National Science Foundation (NSF).

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), and spatially resolved transcriptomics (SRT). 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) Powerful integrative statistical tests of association between gene sets and a phenotype; (2) Scalable Bayesian models and computational tools for high-throughput genomics data; (3) Effective and efficient optimization algorithms for multi-omics integrative analysis; (4) Genetic risk prediction analysis in large-scale bio-banks.

key words

1. Statistical Learning in Genetics and Genomics
2. Deep Learning
3. AI for Biology
4. Single-cell RNA-seq
5. Spatial Transcriptomics
6. Genetic Risk analysis, Polygenic Risk Score
7. High Dimensional Data
8. Statistical Optimization

Hiring

We have openings for multiple positions, including postdoctoral fellows, graduate students, and undergraduate students. We are seeking applications for a postdoctoral fellow position within our research group. This position emphasize developing statistical machine learning, and computational/AI methods in high-dimensional biological data from functional genomic seuqnecing studies or genome-wide association studies (GWASs). Specific areas include, but not limited to, (1) spatially resolved multi-omics data; (2) single-cell genomics; (3) genetic risk prediction; (4) integrative modeling of GWAS summary statistics and omics data; (5) large lanugage models in biomedical data and clinical informatics. This opportunity offers competitive benefits and involves working with diverse large-scale datasets. Prospective applicants should hold or be pursuing a PhD in computer science, AI, biostatistics, statistics, bioinformatics, computational biology, biomedical engeneering, mathematics, or a related quantitative discipline. Strong computational skills are required. Interested applicants should submit a brief statement of interest, CV, and contact information for three references. For questions regarding the position, please contact me at : ying_ma@brown.edu. Applications will be reviewed promptly and accepted until the positions are filled.

For prospective Ph.D. students: I can recruit students through both the Computational Biology and Biostatistics Ph.D. programs. The admission committees for both programs carefully review each application to select future students. If you are interested in working with me, please feel free to mention my name in your application. I look forward to the possibility of working with you in the future.

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