Jian Ma (computer scientist)
American computer scientist
Jian Ma (Chinese: 马坚) is an American computer scientist and computational biologist.[1] He is the Ray and Stephanie Lane Professor of Computational Biology in the School of Computer Science at Carnegie Mellon University.[2][3][4] He is a faculty member in the Ray and Stephanie Lane Computational Biology Department.
His lab develops machine learning algorithms to study the structure and function of the human genome[5] and cellular organization and their implications for health and disease. During his Ph.D. and postdoc training, he developed algorithms to reconstruct the ancestral mammalian genome.[6] His research group has recently pioneered a series of new machine learning methods for 3D genome organization, single-cell epigenomics, spatial omics, and complex molecular interactions. These methods are often pursued through the development of probabilistic models and advanced deep learning techniques, particularly graph-based representation learning, with the aim of driving discovery and guiding experimentation.
He received an NSF CAREER award in 2011.[7] In 2020, he was awarded a Guggenheim Fellowship[8][9][10] in Computer Science. He is an elected Fellow of the American Association for the Advancement of Science[11] and the American Institute for Medical and Biological Engineering.[12] He leads an NIH 4D Nucleome Center to develop machine learning algorithms to better understand the cell nucleus.[5][13] He is the Program Chair for RECOMB 2024.[14]
In 2024, he launched the Center for AI-Driven Biomedical Research (AI4BIO) at CMU, which will be a catalyst for innovations at the intersection of AI and biomedicine across the School of Computer Science and campus.[15][16]
Selected Recent Publications
- Chen V#, Yang M#, Cui W, Kim JS, Talwalkar A*, and Ma J*. Applying interpretable machine learning in computational biology - pitfalls, recommendations and opportunities for new developments. Nature Methods, 21(8):1454-1461, 2024.
- Xiong K#, Zhang R#, and Ma J. scGHOST: Identifying single-cell 3D genome subcompartments. Nature Methods, 21(5):814-822, 2024.
- Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z*, and Ma J*. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nature Genetics, 56(8):1701-1711, 2024.
- Zhang Y, Boninsegna L, Yang M, Misteli T, Alber F, and Ma J. Computational methods for analysing multiscale 3D genome organization. Nature Reviews Genetics, 5(2):123-141, 2024.
- Chidester B#, Zhou T#, Alam S, and Ma J. SPICEMIX enables integrative single-cell spatial modeling of cell identity. Nature Genetics, 55(1):78-88, 2023. [Cover Article]
- Zhang R#, Zhou T#, and Ma J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Systems, 13(10):P798-807.E6, 2022. [Cover Article]
- Zhu X#, Zhang Y#, Wang Y, Tian D, Belmont AS, Swedlow JR, and Ma J. Nucleome Browser: An integrative and multimodal data navigation platform for 4D Nucleome. Nature Methods, 19(8):911-913, 2022.
- Zhang R, Zhou T, and Ma J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nature Biotechnology, 40:254–261, 2022.
References
External links
|
|