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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).

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.


📣 Latest News

2025-09-19: Our method JADE has been accepted to NeurIPS 2025 (Top Machine Learning Conference)!

2025-09-16: Our latest commentary paper was published in Nature Methods: Bridging histology and spatial gene expression across scales.

2025-09-01: Our lab has received three grants from NIH and NSF, including the MIRA ESI R35 Outstanding Investigator Award from NIGMS and two NSF grants. These awards will support five years of our lab’s research. More details: Brown SPH news and CCMB news.

2025-08-05: Andrew Yang’s work has been selected as a contributed poster at JSM 2025! He also won the Best Student Poster Award at NESS 2025!

2025-07-01: Ying Ma named to the endowed Edens Family Assistant Professorship in Healthcare Communications and Technology!

For more news, please visit the News page.