DCMB is pleased to welcome Cong Ma, Ph.D., as its new Assistant Professor, effective September 1, 2024.
Professor Cong Ma develops mathematical, statistical, and machine learning models to study the spatial heterogeneity and organization of cell types including genetic and epigenetic states, in a variety of tissues, particularly in cancer tissues. Ma’s computational models can be applied to different types of tissues, either for foundational biology investigation or clinical research.
Her work includes accurately identifying the tissue geometries and the associated gradient of epigenetic variation. These computational models bring insights into disease mechanisms and could lead to the discovery of diagnostic biomarkers. For example, in cancers, her computational analysis shows how tumorous tissues organization evolves in space and behavior, which can inform potential treatments.
In this area of research, Ma is looking forward to collaborating with professors at U-M and expanding her research to new types of data and answering new biological problems. Some of her interests are circulating tumor cells, tumor micro-environments, and temporally recorded gene expression.
This leading-edge technology, such as Xenium, Visium HD, and Slide-tags, is also capable of revealing biological variations across space at very high resolution (~10 micron to single-cell resolution). Since these variations couldn’t be seen, measured nor studied before, this new technology is opening up a vast field of investigation and the opportunity for more computational methods development.
So far, Ma has had specific experience with cortex, skin, and liver tissues, as well as various cancers. She works with publicly available data as well as data from collaborators.
As a new professor, Ma is also interested in further developing her mentoring skills and wishes to welcome one or two students in her lab.
Welcome professor Cong Ma!
Background
Ma has always been interested in mathematics and computation. She appreciates the certainty brought about by these methods and tools. “Usually, there is a definite answer, ‘true’ or ‘false’. There is a clear path from the assumption to the conclusion, and we can go back and do it again,” she said.
Math is one language to communicate about underlying mechanisms.
Ma earned her B.S. (2015) in Mathematics and Applied Mathematics from Zhejiang University in Hangzhou, China. While she found out that the program was surprisingly about pure math and theorems, she did a summer internship at the University of California-Davis with a biomedical engineer, Dr. Cheemeng Tan, Associate Professor, who introduced her to applying math to biological data. This was a pivoting moment for Ma who felt that all the math and computational work she did took meaning, and she decided to pursue computational biology.
Ma received her Ph.D. in 2020, in Computational Biology from Carnegie Mellon University in Pittsburgh, PA. Her advisor was professor Carl Kingsford. In this lab, she particularly developed computational methods for high-throughput RNA sequencing data. She focused on the actual sequences of the data from various genes, and analyzed large scale sequence variations and gene abundance. While alternative splicing is well known, the function for the many resulting isoforms is still being explored. This research could bring answers to this question and further inform cancer mechanisms.
From 2020 until now, Ma was a Postdoctoral Research Associate in the Department of Computer Science at Princeton University in professor Ben Raphael’s lab. There she has developed mathematical models and computational algorithms for single-cell RNA-seq and spatial transcriptomics data, including modeling layered spatial patterns, copy number aberrations, and structural variants.
Outside the lab, Ma likes cats because they are very cute, and she envies their flexibility and freedom!
