Ph.D., California Institute of Technology
Postdoctoral Appointment, Columbia University
My group's work combines my interests in cell and computational biology. We apply both experimental and computational methods to the fundamental problem of learning and representing how proteins are organized within eukaryotic cells. For this we particularly use automated microscopy combined with methods from machine learning, pattern recognition and modeling. Much of our recent work focuses on automated learning of generative models of subcellular organization that have the promise to allow information from diverse methodologies to be combined to compactly represent current knowledge and enable predictions about how organization changes during development and disease. A second major focus is on intelligent sampling in very large dimensional experimental spaces, such as in the context of learning the effect of thousands of potential drugs on thousands of potential targets.