Researcher | Research Overview
The rapid growth of cell biology data from new developments of microscopy (3D, super-resolution) as well as single-cell technology reveals much more heterogeneity than we have imagined before, presenting the next “big data” challenge for biomedical research. AI (Artificial Intelligence) is making tremendous progress and has shown that machines can outperform humans in the analysis of heterogeneous and high-dimensional big datasets. Therefore, we need AI to make progress in understanding basic cell biology and diseases mechanisms. Current AI applications in cell biology, however, focused on static datasets such as single-cell RNA-seq and immunofluorescence images. AI has not been extensively used for dynamic information from high-resolution live cell images. To fill these scientific and technological voids, we are focusing on developing an AI platform that identifies subtle or unknown dynamic phenotypes in live cell movies that cannot be detected by the human eye. Using this platform, we will unravel the phenotypic heterogeneity of cellular morphodynamics/motility in angiogenesis and cancer, and develop precision cancer diagnosis/therapeutics.
Researcher | Research Background
Dr. Kwonmoo Lee was trained in Marc Kirschner’s lab at Harvard Medical School as a graduate student in MIT Physics Ph.D. program. He did his post-doctoral training as an NIH post-doctoral fellow in Gaudenz Danuser’s lab at Harvard Medical School. He held an assistant professor position in the Department of Biomedical Engineering at Worcetster Polytechnic Institute. He joined Vascular Biology Program at Boston Children's Hostpital and Department of Surgery at Harvard Medical School in 2020. He is focusing on developing machine learning methods for cell biology and mechanobiology. Particularly, he is interested in how subcellular morphodynamic heterogeneity governs cell motility in healthy and pathological conditions.