My lab is interested in understanding the spatial-contextual and other regulatory mechanisms that shape cellular identity and heterogeneity. We are particularly interested in characterizing heterogeneity in the context of cancer and understanding how this heterogeneity shapes tumor progression, therapeutic resistance, and ultimately clinical outcomes. While heterogeneity within cellular systems has long been widely recognized, only recently have technological advances enabled measurements to be made on a single cell level. Applying traditional bulk analysis methods on single cells has met with varied degrees of success due to the high levels of technical as well as biological stochasticity and noise inherent in single cell measurements. Therefore, we develop machine learning and other statistical methods to harness the power of these new large-scale multi-omic single cell resolution data in addressing basic science and translational research questions. Our methods are available as open-source computational software and accessible to the broader scientific community.
- Postdoctoral Fellowship, Chemistry & Chemical Biology, Physics, Harvard University, 2018-2020
- PhD, Bioinformatics and Integrative Genomics, Harvard Medical School, 2018
- BS, Biomedical Engineering, Applied Math & Statistics, Johns Hopkins University, 2013
February 23, 2021In this interview, Fan discusses her research using imaging and sequencing technologies to study genes, the impact she hopes her work will produce, her non-profit organization that works to encourage young girls to become interested in science, and more.