Nancy R. Zhang

You can find the latest updates on my research on my lab website and my google scholar page.

• 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.
• All Research from Nancy R. Zhang »

Current Courses

• STAT9915 - Sem In Adv Appl Of Stat: Advanced Topics In Biomedical Data Science

  This seminar-based course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups on a data science problem of interest to a community organization.

  STAT9915301 ( Syllabus )

Past Courses

• AMCS5999 - Independent 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.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• GCB6990 - Lab Rotation

  Lab rotation

• GCB8990 - Pre-Dissertation Research

  Pre-dissertation lab research

• GCB9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• STAT4050 - Stat Computing with R

  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.

• STAT7050 - Stat Computing with R

  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.

• STAT9610 - Statistical Methodology

  This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.

• STAT9910 - Sem in Adv Appl of Stat

  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.

• STAT9915 - Sem in Adv Appl of Stat

  This seminar-based course provides students with the opportunity to hone their data science skills and gain practical experience by working with a community organization on a data science problem of interest to the organization. Students will gain skills in problem formulation, collaboration with community organizations and communication of data science results. Students will work in groups on a data science problem of interest to a community organization.

• STAT9950 - Dissertation

  Dissertation

• STAT9999 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll.

• IMS Medallion Lecture, 2021
• Sloan Fellowship, 2011
• New World Silver Medal for Best PhD Thesis in Mathematical Sciences, 2007

Knowledge @ Wharton

• Does Financial Literacy Decline with Age?, Knowledge @ Wharton - 04/01/2025
• Retirement and the Changing Workforce | Peter Cappelli, Knowledge @ Wharton - 04/01/2025
• How Tariffs Will Impact the U.S. Auto Industry, Knowledge @ Wharton - 03/31/2025
• How to Prevent Student Loan Delinquency, Knowledge @ Wharton - 03/31/2025
• Jeremy Siegel on Stock Market Uncertainty, Knowledge @ Wharton - 03/28/2025
• NBA Analytics with Nate Duncan, Knowledge @ Wharton - 03/26/2025
• The Impact of Tariffs on North America’s Auto Industry, Knowledge @ Wharton - 03/26/2025
• Should We Compare Bitcoin to Gold? Lessons From the Last Financial Crisis, Knowledge @ Wharton - 03/25/2025
• What’s Causing Corporate Layoffs?, Knowledge @ Wharton - 03/25/2025
• International Taxation and U.S. Businesses | Daniel Garrett, Knowledge @ Wharton - 03/25/2025