Speaker Profile
Biography
Fei Chen is a core institute member at the Broad Institute of MIT and Harvard and an associate professor in the Department of Stem Cell and Regenerative Biology at Harvard University. Chen’s laboratory is building tools that bridge single-cell genomics with space and time, to enable discoveries of where cell types are localized within intact tissues, as well as when relevant transcriptional modules are active. To do this, the lab is developing novel experimental and computational technologies at the intersection of microscopy, genomics, and synthetic biology. His group is applying these tools to learn organizational principles governing tissue development and cellular mechanisms of disorganization during injury and disease.
During his doctoral research, Chen co-invented expansion microscopy, a breakthrough technique that allows for super-resolution imaging of biological samples with conventional light microscopes. As an independent fellow at the Broad Institute, he led a group that continued to pioneer novel tools at the intersection of genomics and microscopy to uniquely illuminate biological pathways and function. These include Slide-seq, a novel technology platform for performing genomic measurements of DNA and RNA within tissues with near-single-cell resolution, and in situ genome sequencing, which allows for de novo sequencing of DNA within cells and tissues.
Chen obtained his Ph.D. in biological engineering from the Massachusetts Institute of Technology, where he worked with Ed Boyden. Chen was a Schmidt Fellow at the Broad Institute. His awards include the National Institutes of Health Director’s Early Independence Award, the Searle Scholars Award, the Burroughs Wellcome CASI Award, the Allen Distinguished Investigator Award, and a Merkin Institute Fellowship.
Session Abstract – PMWC 2027 Silicon Valley
The PMWC 2026 AI Company Showcase will provide a 15-30 minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.




