Jian Ma (computer scientist)

Jian Ma
马坚
Citizenship	United States
Awards	Guggenheim Fellowship (2020) AAAS Fellow (2022)
Scientific career
Fields	Computational biology Computational genomics Machine learning Systems Biology
Institutions	Carnegie Mellon University (since 2016) University of Illinois Urbana–Champaign (2009–2015)
Academic advisors	David Haussler (postdoc advisor), Webb Miller (PhD advisor)
Website	www.cs.cmu.edu/~jianma/

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]

• 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.

• Jian Ma's publications indexed by Google Scholar