After completing my Ph.D. in 2019 in Computer Science at Harvard, I worked in the Department of Epidemiology to bring deep learning research to the Harvard medical community, and the medical field, more broadly. Toward this end, I developed a series of new approaches for understanding, analyzing, and updating neural networks and their associated datasets. Prospectively, these approaches will eventually underpin the reliable deployment of neural networks for medical applications. Most recently, my colleague Danielle Rasooly, Ph.D., and I have been working on approaches for uncertainty quantification for deep neural networks (arXiv:2205.14310), and yet more things to come at our startup Reexpress AI, Inc.
I have uploaded some recent presentation materials that provide an overview of my current line of research, and the images included at the bottom of this page (or here as a single PDF) summarize the key aspects.
Uncertainty is but a distance to what is known...
