Personal Information

Greetings! I am a PhD candidate under Eric P. Xing at Carnegie Mellon University's Machine Learning Department, which i joined in 2009.

My research focuses on statistical models for network analysis, particularly latent space models for visualization, user personalization and interest prediction. I also maintain an interest in social media analysis, particularly hyperlinked documents with text and network data. The techniques I use include graphical models, Nonparametric Bayes, MCMC, and constrained optimization. Recently, I have taken an interest in systems work for large scale, distributed Machine Learning.

You may contact me at qho AT cs DOT cmu DOT edu.

Projects


Large-scale Distributed Machine Learning with Petuum: http://petuum.org/

Publications

  • A. Kumar, A. Beutel, Q. Ho and E. P. Xing, Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014). [pdf coming soon]
  • Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. Neural Information Processing Systems, 2013 (NIPS 2013). [pdf] [appendix] [slides]
  • J. Yin, Q. Ho and E. P. Xing, A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks. Neural Information Processing Systems, 2013 (NIPS 2013). [pdf] [appendix]
  • J. Cipar, Q. Ho, J.-K. Kim, S. Lee, G. R. Ganger, G. Gibson, K. Keeton and E. P. Xing, Solving the straggler problem with bounded staleness. The 14th Workshop on Hot Topics in Operating Systems (HotOS XIV, 2013). [pdf]
  • Q. Ho, J. Yin and E. P. Xing. On Triangular versus Edge Representations - Towards Scalable Modeling of Networks. Neural Information Processing Systems, 2012 (NIPS 2012). [pdf] [appendix] [code]
  • Q. Ho, A. Parikh and E. P. Xing. Multiscale Community Blockmodel for Network Exploration. Journal of the American Statistical Association, 2012 (JASA 2012). [pdf]
  • Q. Ho, J. Eisenstein and E. P. Xing. Document Hierarchies from Text and Links. Proceedings of the International World Wide Web Conference, 2012 (WWW 2012). [pdf] [presentation]
  • Q. Ho, A. Parikh, L. Song and E. P. Xing. Multiscale Community Blockmodel for Network Exploration. Proceedings of the 14th International Conference on Artifical Intelligence and Statistics, 2011 (AISTATS 2011). [pdf] [Supplemental Material]
  • Q. Ho, L. Song and E. P. Xing. Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. Proceedings of the 14th International Conference on Artifical Intelligence and Statistics, 2011 (AISTATS 2011). [pdf] [Supplemental Material]
  • A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. Smola and C. H. Teo. Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTATS 2011). [pdf] [Supplemental Material]
  • A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. Smola and C. H. Teo. Unified Analysis of Streaming News. Proceedings of the International World Wide Web Conference (WWW 2011). [pdf]