Personal Information

Greetings! I am a scientist at the Institute for Infocomm Research, A*STAR, Singapore. I am also an adjunct assistant professor at the Singapore Management University School of Information Systems.

I received my PhD in 2014, under Eric P. Xing at Carnegie Mellon University's Machine Learning Department.

My research focuses on statistical models for large-scale network analysis, particularly latent space models for visualization, community detection, user personalization and interest prediction. I also maintain an interest in social media analysis, particularly hyperlinked documents with text and network data. From a Machine Learning perspective, the techniques I use include graphical models, Nonparametric Bayes, MCMC, and constrained optimization. Because analysis at scale often requires distributed computing, I also work on general-purpose systems to enable large-scale distributed Machine Learning.

You may contact me at hoqirong AT gmail DOT com.

Projects


Petuum, a project to enable large-scale distributed Machine Learning: http://petuum.org/

Publications

* = joint first authors.
  • E. P. Xing, Q. Ho, W. Dai, J.-K. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, Y. Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. Accepted to SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015).
  • Z. Hu, Q. Ho, A. Dubey, E. P. Xing. Large-scale Distributed Dependent Nonparametric Trees. Accepted to International Conference on Machine Learning 2015 (ICML 2015).
  • J. Yuan, F. Gao, Q. Ho, W. Dai, J. Wei, X. Zheng, E. P. Xing, T.-Y. Liu, W.-Y. Ma. LightLDA: Big Topic Models on Modest Compute Clusters. Accepted to International World Wide Web Conference 2015 (WWW 2015).
  • W. Dai, A. Kumar, J. Wei, Q. Ho, G. Gibson, and E. P. Xing. High-Performance Distributed ML at Scale through Parameter Server Consistency Models. AAAI Conference on Artificial Intelligence (AAAI 2015). [pdf] [appendix]
  • S. Lee, J.-K. Kim, X. Zheng, Q. Ho, G. Gibson, and E. P. Xing. On Model Parallelism and Scheduling Strategies for Distributed Machine Learning. Neural Information Processing Systems, 2014 (NIPS 2014). [pdf] [appendix]
  • A. Dubey*, Q. Ho*, S. Williamson and E. P. Xing, Dependent nonparametric trees for dynamic hierarchical clustering. Neural Information Processing Systems, 2014 (NIPS 2014). [pdf] [appendix]
  • W. Neiswanger, C. Wang, Q. Ho and E. P. Xing, Modeling Citation Networks using Latent Random Offsets. Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014). [pdf]
  • H. Cui, G. Ganger, J. Cipar, Q. Ho, J.-K. Kim, S. Lee, A. Kumar, P. B. Gibbons, G. Gibson, E. P. Xing, Exploiting bounded staleness to speed up Big Data analytics. USENIX Annual Technical Conference (ATC 2014). [pdf]
  • A. Kumar, A. Beutel, Q. Ho and E. P. Xing, Fugue: 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]
  • 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. A 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 Artificial 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 Artificial 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 Artificial 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]
  • Q. Ho, W. Yu and H. K. Lee. Region Graph Spectra as Global Geometric Image Features. 5th International Symposium on Visual Computing (ISVC 2009).
  • Q. Ho and C. Geyer. The Conditionalizing Identity Management Bayesian Filter (CIMBal). Carnegie Mellon University Technical Report (2008), CMU-RI-TR-08-47.

Thesis

Q. Ho, Modeling Large Social Networks in Context. PhD Thesis, School of Computer Science, Carnegie Mellon University, 2014. [pdf]

Book Chapters

  • Qirong Ho and Eric P. Xing. Analyzing Time-Evolving Networks using a Evolving Cluster Mixed Membership Stochastic Blockmodel. Handbook of Mixed Membership Models and its Applications (Chap 22), edited by E.M. Airoldi, D.M. Blei, E.A. Erosheva, and S.E. Fienberg, 2014.