Rong Ge

Rong Ge is the Cue Family Associate Professor of Computer Science at Duke University, where she serves as Director of Graduate Studies for the PhD Program. She received her Ph.D. from Princeton University, advised by Sanjeev Arora, and completed a post-doc at Microsoft Research, New England. Her research focuses on theoretical computer science and machine learning, particularly in formalizing hidden structures in data and designing efficient algorithms for tasks such as non-convex optimization and tensor decompositions. Her work explores applications in analyzing text, images, and other data types. She has supervised multiple PhD students and post-docs and has published extensively in top conferences such as ICML, ICLR, and NeurIPS.

Rong Ge is a researcher in Theoretical Computer Science and Machine Learning, focusing on the development of algorithms with provable guarantees for complex learning problems. His work addresses challenges in optimization, generalization, and the analysis of deep learning models. Key areas include tensor decomposition, latent variable models, and the study of nonconvex optimization in machine learning. He has contributed to understanding the landscape of neural networks, the existence of spurious local minima, and the design of efficient algorithms for tasks such as matrix completion and topic modeling. His research bridges theoretical insights with practical applications in machine learning.