My research interest is in the broad area of machine learning. The focus of my doctoral work is on learning Mixed Membership Latent Variable Models (sometimes known as topic modeling) that can characterize complex high-dimensional observations including text documents (e.g., Twitter streaming, news articles, etc.), user preferences (pairwise comparisons over products, movies, etc.). Our focus is to develop provable algorithms with computation and sample complexity guarantees under an inevitable separable condition.





  • W. Ding, P. Ishwar, and V. Saligrama, “A Topic Modeling Approach to Ranking,”  in Proc. International Conference in Artificial Intelligence and Statistics (AISTATS’15), San Diego, CA, USA, 7-9, May., 2015. [arXiv version]
  • W. Ding, P. Ishwar, and V. Saligrama, “Most Large Topic Models are Approximately Separable,”  in Proc. 10th IEEE International Workshop on Information Theory and Applications (ITA)San Diego, CA, 1-6, Feb., 2015.
  • W. Ding, P. Ishwar, and V. Saligrama, “Learning Shared Rankings From Mixtures of Noisy Pairwise Comparisons,”  in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’15), Apr., 2015.