Abstract: The emergence and availability of electronic health records can enable disease prevention, streamline clinical decision-making, and improve diagnostic precision. However, one major challenge is how to produce appropriate representations from these high-dimensional data. Tensors, generalizations of matrices to multiway data, are natural structures for capturing higher-order interactions. Factorization of these tensors provides a powerful, data-driven framework for learning representations across a variety of downstream prediction tasks. Unfortunately, tensor factorization is a computationally expensive task.

In this talk, I will present our work on how to accelerate convergence with limited computing resources and how to jointly learn representations across distributed locations while preserving the privacy of patient information. 

Bio: Joyce Ho is an Associate Professor in the Computer Science Department at Emory University. She received a Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin, and an M.A. and B.S. in Electrical Engineering and Computer Science from Massachusetts Institute of Technology.