Tianzhou (Charles) Ma
Dr. Tianzhou (Charles) Ma is an Assistant Professor in Biostatistics at the Department of Epidemiology and Biostatistics of the School of Public Health at University of Maryland. He received his PhD in Biostatistics from the University of Pittsburgh in 2018 and a MS in Biostatistics from Yale University in 2013. Dr. Ma’s research focuses on statistical methods in genomics and bioinformatics, meta-analysis and omics data integration, statistical machine learning, Bayesian analysis, high-dimensional variable selection, as well as their application in cancer, psychiatry (aging and depression) and epidemiology fields. His work has appeared in Journal of the Royal Statistical Society, Statistica Sinica, Bioinformatics, Journal of Computational Biology, Proceedings of the National Academy of Sciences, Nucleic Acid Research, Clinical Cancer Research, Oncogene, Biological Psychiatry, Obstetrics & Gynecology. He is open-minded and actively seeking for collaborations with researchers from various fields, to understand the objectives of their projects, provide consultation in study design and help analyze the data.
2013-2018 Ph.D. in Biostatistics, University of Pittsburgh
2011-2013 M.S. in Biostatistics, Yale University
2007-2010 B.S. in Genetics and Biotechnology, University of Toronto
EPIB 661: Applied Multivariate Data Analysis
Delta Omega Membership, Delta Omega Honorary Society in Public Health, 2018
Student Paper Award, American Statistical Association Section on Bayesian Statistical Science (SBSS), 2017
Student of the Year, American Statistics Association (ASA) Pittsburgh chapter, 2017
Best Paper Award, Dahshu Data Science Symposium: Computational Precision Health, 2017
Best Student Presentation Award, Department of Biostatistics, University of Pittsburgh, 2017
Outstanding Graduate Student Researcher Award, Department of Biostatistics, University of Pittsburgh, 2016
Travel Award to attend "Optimization Opening Workshop" , Statistical and Applied Mathematical Sciences Institute, 2016
Dean's Day Poster Competition Award, Graduate School of Public Health, University of Pittsburgh, 2015
Dean's list, Faculty of Arts and Science, University of Toronto, 2008-2010
University College Scholarship, University College, University of Toronto, 2008-2010
Fang, Z., Ma, T., Tang, G., Zhu, L., Yan, Q., Wang, T., Celedón, J.C., Chen, W., Tseng, G.C. and Hancock, J. (2018). Bayesian integrative model for multi-omics data with missingness. Bioinformatics.
Ma, T.*, Huo, Z.*, Kuo, A.*, Zhu, L., Fang, Z., Zeng, X., Lin, C., Liu. S., Wang, L., Liu, P., Rahman, T., Chang, L., Kim, S., Li, J., Park, Y., Song, C. and Tseng, GC. (2018). MetaOmics - Comprehensive Analysis Pipeline and Web-based Software Suite for Transcriptomic Meta-Analysis. Bioinformatics.
Ma, T., Ren, Z. and Tseng, GC. (2018). Variable screening with multiple studies. Statistica Sinica.
Scifo, E., Pabba, M., Kapadia, F., Ma, T., Lewis, D.A., Tseng, G.C. and Sibille, E. (2018). Sustained molecular pathology across episodes and remission in major depressive disorder. Biological psychiatry, 83(1), pp.81-89.
Linkov, F., Goughnour, S.L., Ma, T., Xu, Z., Edwards, R.P., Lokshin, A.E., Ramanathan, R.C., Hamad, G.G., McCloskey, C. and Bovbjerg, D.H. (2017). Changes in inflammatory endometrial cancer risk biomarkers in individuals undergoing surgical weight loss. Gynecologic oncology, 147(1), pp.133-138.
Pabba, M., Scifo, E., Kapadia, F., Nikolova, Y.S., Ma, T., Mechawar, N., Tseng, G.C. and Sibille, E. (2017). Resilient protein co-expression network in male orbitofrontal cortex layer 2/3 during human aging. Neurobiology of aging, 58, pp.180-190.
Ma, T., Song, C. and Tseng, G.C.(2017) Discussant paper on ‘Statistical contributions to bioinformatics: Design, modelling, structure learning and integration’. Statistical Modelling, 17(4-5), pp.305-315.
Ma, T., Liang, F. and Tseng, G.C. (2017) Biomarker detection and categorization in ribonucleic acid sequencing meta‐analysis using Bayesian hierarchical models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66(4), pp.847-867. (reported on [RNA-Seq Blog])
Ma, T., Liang, F., Oesterreich, S. and Tseng, G.C. (2017) A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data. Journal of Computational Biology, 24(7), pp.647-662.
French, L., Ma, T., Oh, H., Tseng, G.C. and Sibille, E. (2017) Age-related gene expression in the frontal cortex suggests synaptic function changes in specific inhibitory neuron subtypes. Frontiers in Aging Neuroscience, 9, p.162.
Andersen, C.L., Sikora, M.J., Boisen, M.M., Ma, T., Christie, A., Tseng, G., Park, Y.S., Luthra, S., Chandran, U., Haluska, P. and Mantia-Smaldone, G. (2017) Active estrogen receptor-alpha signaling in ovarian cancer models and clinical specimens. Clinical Cancer Research, pp.clincanres-1501.
Zhang, L., Ma, T., Brozick, J., Babalola, K., Budiu, R., Tseng, G., & Vlad, A. M. (2016). Effects of Kras activation and Pten deletion alone or in combination on MUC1 biology and epithelial-to-mesenchymal transition in ovarian cancer. Oncogene, 35(38), 5010.
Chen, C. Y., Logan, R. W., Ma, T., Lewis, D. A., Tseng, G. C., Sibille, E., & McClung, C. A. (2016). Effects of aging on circadian patterns of gene expression in the human prefrontal cortex. Proceedings of the National Academy of Sciences, 113(1), 206-211. (High Attention Paper, 99th
percentile, [News on National Public Radio (NPR)])
Sanei-Moghaddam, A., Ma, T., Goughnour, S.L., Edwards, R.P., Lounder, P.J., Ismail, N., Comerci, J.T., Mansuria, S.M. and Linkov, F. (2016). Changes in hysterectomy trends after the implementation of a clinical pathway. Obstetrics & Gynecology, 127(1), pp.139-147.
Liao, S., Hartmaier, R.J., McGuire, K.P., Puhalla, S.L., Luthra, S., Chandran, U.R., Ma, T., Bhargava, R., Modugno, F., Davidson, N.E. and Benz, S. (2015). The molecular landscape of premenopausal breast cancer. Breast Cancer Research, 17(1), p.104. (discussed
in an interview; [Nature, 527: S108-109])
Liu, S., Tsai, W.H., Ding, Y., Chen, R., Fang, Z., Huo, Z., Kim, S., Ma, T., Chang, T.Y., Priedigkeit, N.M. and Lee, A.V. (2015). Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data. Nucleic acids research, 44(5), pp.e47-e47.