431 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: genomics, change-point methods, empirical bayes estimation, model and variable selection, scan statistics, statistical modeling
Links: CV, Lab Website
Dr. Zhang is Professor of Statistics and Data Science in The Wharton School at University of Pennsylvania. Her current research focuses primarily on the development of statistical and computational approaches for the analysis of genetic, genomic, and transcriptomic data. In the field of Genomics, she has developed methods to improve the accuracy of copy number variant and structural variant detection, methods for improved FDR control, and methods for analysis of single-cell RNA sequencing data. In the field of Statistics, she has developed new models and methods for change-point analysis, variable selection, and model selection. Dr. Zhang has also made contributions in the area of tumor genomics, where she has developed analysis methods to improve our understanding of intra-tumor clonal heterogeneity.
Dr. Zhang obtained her Ph.D. in Statistics in 2005 from Stanford University. After one year of postdoctoral training at University of California, Berkeley, she returned to the Department of Statistics at Stanford University as Assistant Professor in 2006. She received the Sloan Fellowship in 2011, before formally moving to University of Pennsylvania in 2012 as tenured Associate Professor. She is the Principal Investigator in multiple independent research awards funded by the National institutes of Health and National Science Foundation. She was rewarded a Medallion Lectureship by the Institute of Mathematical Statistics in 2021. At Penn, she is a member of the Graduate Group in Genomics and Computational Biology and of the Penn Neurodegeneration Genomics Center.
Here are some of Dr. Zhang’s representative publications, categorized by topic (ǂalphabetical ordering, *corresponding author):
For a complete overview of Dr. Zhang’s publications, funded grants, and teaching, mentoring, and service work, see her CV above.
You can find the latest updates on my research on my lab website:
For a complete list of my publications and funded grants, the most trustworthy source is my CV (see link above). The searchable publication list below is only updated once per year.
Somabha Mukherjee, Divyansh Agarwal, Nancy Zhang, Bhaswar B. Bhattacharya (2022), Distribution-free multisample test based on optimal matching with applications to single cell genomics, Journal of the American Statistical Association, 117 (538), pp. 627-638.
Zilu Zhou, Chengzhong Ye, Jingshu Wang, Nancy Zhang (2020), Surface protein imputation from single cell transcriptomes by deep neural networks, Nature Communications, 11 (651), pp. 1-10.
Zilu Zhou, Bihui Xu, Andy Minn, Nancy Zhang (2020), DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing, Genome Biology, 21 (10), pp. 1-15.
Son Nguyen, Claire Deleage, Samuel Darko, Amy Ransier, Duc P. Truong, Divyansh Agarwal, Alberto Sada Japp, Vincent H. Wu, Leticia Kuri-Cervantes, Mohamed Abdel-Mohsen, Perla M. Del Rio Estrada, Yuria Ablanedo-Terrazas, Emma Gostick, James A. Hoxie, Nancy Zhang, Ali Naji, Gustavo Reyes-Teran, Jacob D. Estes, David A. Price, Daniel C. Douek, Steven G. Deeks, Marcus Buggert, Michael R. Betts (2019), Elite control of HIV is associated with distinct functional and transcriptional signatures in lymphoid tissue CD8+ T cells, Science Translational Medicine , 11(523): eaax4077.
Divyansh Agarwal and Nancy Zhang (2019), Semblance: An empirical similarity kernel on probability spaces, Science Advances, 5(12): eaau9630.
Diana Pauly, Divyansh Agarwal, Nicholas Dana, Nicole Schafer, Josef Biber, Kirsten A. Wunderlich, Yassin Jabri, Tobias Straub, Nancy Zhang, Avneesh K. Gautam, Bernhard H.F. Weber, Stefanie M. Hauck, Mijin Kim, Christine A. Curcio, Dwight Stambolian, Mingyao Li, Antje Grosch (2019), Cell-Type-Specific Complement Expression in the Healthy and Diseased Retina, Cell Reports, 29 (9), pp. 2835-2848.
Nancy Zhang and Mo Huang (Working), Reply to “Issues arising from benchmarking single-cell RNA sequencing imputation methods”.
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, Nancy Zhang (2019), Data denoising with transfer learning in single-cell transcriptomics, Nature Methods, 16, pp. 875-878.
Joseph L. Benci, Lexus R. Johnson, Ruth Choa, Yuanming Xu, Jingya Qiu, Zilu Zhou, Bihui Xu, Darwin Ye, Katherine L. Nathanson, Carl H. June, John Wherry, Nancy Zhang, Hemant Ishwaran, Matthew D. Hellmann, Jedd D. Wolchok, Taku Kambayashi, Andy J. Minn (2019), Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade, Cell, 178 (4), pp. 933-948.
Qingyuan Zhao, Jingshu Wang, Zhen Miao, Nancy Zhang, Sean Hennessy, Dylan Small, Daniel J. Rader, The role of lipoprotein subfractions in coronary artery disease: A Mendelian randomization study.
Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.
Study under the direction of a faculty member.
Lab rotation
Pre-dissertation lab research
Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.
The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
Written permission of instructor and the department course coordinator required to enroll.
A new program in Wharton’s Department of Statistics and Data Science offers advanced coursework and research experience for students who hope to earn a PhD but need additional preparation for admission to a statistics doctoral program. The Bridge to a Doctorate Program in Statistics and Data Science is a two-year…
Wharton Stories - 09/13/2023