Research Interests: Application of Machine Learning to Quantitative Live Cell Imaging
Our research is focused on developing novel methodologies for quantitative live cell imaging using machine/deep learning. Research areas are as follows.
- Integrating high throughput imaging and machine learning to deconvolve morphodynamic heterogeneity of cell protrusion and membrane-bound organelles
- Deconvolution of heterogeneous effects of genetic/pharmacological perturbations at the subcellular level
- Developing deep learning-based image analysis methods
- Mechanobiology and biophysics of cell motility
Research Equipment and Facilities
- Spinning disk confocal microscope for live cell imaging
- High-performance workstations for deep learning
- Advanced image analysis platform for cell morphodynamics, fluorescent speckle imaging, single particle tracking, and traction force reconstruction.
NIH/NIGMS (PI), DoD (PI), NSF/IIS (Co-PI), WPI (PI)
Prof. Yongho Bae, Department of Pathology and Anatomical Sciences, University at Buffalo
Prof. Junsang Doh, Department of Materials Sciences and Engineering, Seoul National University
Prof. Hakho Lee, Center for Systems Biology, Massachusetts General Hospital/Harvard Medical School