Sha Cao, Ph.D.
Associate Professor of Medical & Molecular Genetics
Sha Cao is highly motivated to pursue an academic career in bioinformatics and computational biology applied in translational sciences. My research tracks include: 1) development of novel statistical and machine learning techniques; and 2) addressing important translational and biological questions, through multiple omics data mining and quantitative modeling. Her current research focuses are:
Multiple omics data mining. This has been my main research focus, and the challenges we are dealing with include: how to effectively integrate multiple omics data types and enable knowledge transfer among them, locating latent structures within a dataset, and detecting locally homogeneous structures within the noisy background.
Cancer microenvironment and epigenetic regulation. Towards this goal, our specific aims are to understand: the regulatory effect of epigenome on transcriptome particularly regarding stress responses, and how the epigenome are aggregated in a way to cope with the cellular stresses.
Titles & Appointments
Assistant Professor of Biostatistics & Health Data Science
Adjunct Assistant Professor, School of Public Health
Adjunct Assistant Professor, School of Informatics & Computing
Key Publications
1. Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao*, Chi Zhang*. Geometric all-way Boolean tensor decomposition. Advances in Neural Information Processing Systems 33 (NeurIPS) (2020). In press.
2. Wennan Chang, Changlin Wan, Yong Zang, Chi Zhang, and Sha Cao*. Supervised clustering of high dimensional data using regularized mixture modeling. Briefings in Bioinformatics. (2020) In press.
3. Xiaoyu Lu, Szu-wei Tu, Wennan Chang, Changlin Wan, Yifan Sun, Baskar Ramdas, Xin Lu, Shannon Hawkins, Reuben Kapur, Xiongbin Lu*, Sha Cao*, Chi Zhang*. SSMD: A semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data. Briefings in Bioinformatics. (2020) In press.