Machine Learning Center at Georgia Tech (ML@GT) and School of Computational Science and Engineering (CSE) Ph.D. student Xinshi Chen is being recognized for her work in machine learning with a prestigious fellowship. Chen specializes in principled machine learning research with a focus on learning-based algorithm design and deep learning structured data. Her work has garnered the attention of Google and recently received the 2020 Google Ph.D. Fellowship for outstanding graduate research in machine learning. One of her recent co-authored papers aims to create a system that can automatically learn an algorithm from data and apply the learned algorithm to solve new problems. The paper, developed with CSE Associate Professor Le Song, along with Yufei Zhang and Christoph Reisinger of the University of Oxford, will be presented at the Thirty-fourth Conference on Neural Information Processing Systems, which is scheduled for Dec. 6 through 12 Chen said, "Both algorithms and deep learning models aim to solve problems and make predictions for various tasks. Our project investigates the connection between traditional algorithms and deep learning models, and the strengths of these two can be combined to help each other."