Associate Professor Jingrui He has been awarded a three-year, $500,000 grant from the National Science Foundation (NSF) to develop explainable techniques to detect and track rare categories. For her project, "RareXplain: A Computational Framework for Explainable Rare Category Analysis," she will focus on real-world problems where underrepresented, rare (abnormal) examples play critical roles, such as defective silicon wafers resulting from a new semiconductor manufacturing process and rare but severe complications (e.g., kidney failure) among diabetes patients.
"This problem of explainable rare category analysis was motivated by my collaboration with IBM Research and Mayo Clinic Arizona," said He. "In both semiconductor manufacturing and healthcare, the targets of interest are rare but of great importance. Existing models in this area are mostly black box in nature, making them difficult to be comprehended by domain experts with limited knowledge in AI."
According to He, the new project will bridge the gap between the imminent need to analyze complex rare categories and the inability of state-of-the-art techniques to address this problem in an effective, efficient, and explainable way.
He's general research theme is to design, build, and test a suite of automated and semi-automated methods to explore, understand, characterize, and predict real-world data by means of statistical machine learning. She received her PhD in machine learning from Carnegie Mellon University.