Her research interests lie in developing scalable and parallel algorithms for large-scale data using statistical models. She has worked on non-convex function optimization such as finding tensor decomposition with global convergence guarantee using stochastic gradient descent; developing fast detection algorithm to discover hidden and overlapping user communities in social networks; designing a parallel spectral tensor decomposition algorithm for detecting hidden topics in articles on Map-Reduce frameworks; and learning convolutional sparse coding models using tensor methods for extracting text sequence embeddings and image filter-bank. Beside pure statistical computation, Furong has applied her machine learning techniques to biology.