To reveal and understand physical laws of nature, scientists sometimes create novel experimental systems in their laboratories, which they then capture with video cameras. To fully realize what these systems are showing, however, scientists must also often create new, bespoke computational data analysis pipelines to extract key quantitative parameters from their video image data. This project seeks to discover underlying commonality between different forms of experimental scientific data analyses, and then exploit these shared properties with a combination of machine learning methods (data augmentation and active learning). Ultimately, it seeks to create a visual, interactive data analysis system, based on a neural network for image processing and visualization for display of extracted features, that will lower the human cost of creating new ways to study nature.
Associate Professor, Computer Science
Discovery Doctoral Fellow