Shu Wang
PhD
Qualification
- Massachusetts Institute of Technology, Postdoctoral Associate, 2021-2023.
- Harvard University, PhD in Biophysics, 2014-2021.
- Cornell University, BA in Biology, Chemistry, Physics, and Mathematics, 2010-2014.
MY RESEARCH OVERVIEW
We are broadly interested in mathematically understanding the multi-scale networks underlying biological systems (e.g. protein signaling, cell states, tissue organization), seeking the accuracy and precision that would be needed to reliably treat heterogeneous diseases such as cancer.
To this end, we emphasize both empirically analyzing high-dimensional, high-throughput biological data (e.g. single-cell multiplex imaging or spatial transcriptomics), as well as mathematically analyzing mechanistic models of biological processes, using whatever tools are appropriate case-by-case.
In the past, data analysis techniques have included supervised learning, manifold learning, spatial statistics, and probabilistic graphical models. Past mathematical modeling techniques have included dynamical systems theory, differential and algebraic geometry, and chemical reaction network theory.
Visit Dr. Shu Wang's Discover Research profile to learn more.
SELECT PUBLICATIONS
- Wang S, Sontag ED, Lauffenburger DA. What cannot be seen correctly in 2D visualizations of single-cell ‘omics data? Cell Syst. 2023 Sep 20;14(9):723-731. https://doi.org/10.1016/j.cels.2023.07.00
- Lin JR, Wang S, Coy S, Chen YA, Yapp C, Tyler M, Nariya MK, Heiser CN, Lau KS, Santagata S, Sorger PK. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell. 2023 Jan 19;186(2):363-381.e19. https://doi.org/10.1016/j.cell.2022.12.028
- Wang S, Lin JR, Sontag ED, Sorger PK. Inferring reaction network structure from single-cell, multiplex data, using toric systems theory. PLoS Comput Biol. 2019 Dec 6;15(12):e1007311. https://doi.org/10.1371/journal.pcbi.1007311