Petuum industrializes AI, turning businesses into owners, builders and informed users
We create the standardized building blocks for assembling AI affordably and sustainably. Visit our CASL (Composable, Automatic and Scalable ML) open source project to learn more!
We're humbled and thrilled to be part of the WEF Tech Pioneers 2018, the CB Insights AI 100 list in 2017 and 2018, the Pittsburgh Technology Council AI Innovator of the Year 2018, and the Timmy Awards 2018 Best Tech Startup Finalists. Info and videos:
I've given talks on ML and AI systems (high-performance, big data), and what's going on at Petuum:
I work on distributed software systems for Machine Learning at Big Data and Big Model scales, with a view towards theoretical correctness and performance guarantees, as well as practical needs like robustness, programmability and usability. I've also worked on statistical models for large-scale network analysis and social media, including latent space models for visualization, community detection, user personalization and interest prediction.
Previously, I was Lab Head and Principal Investigator for Distributed Analytics at the Institute for Infocomm Research, A*STAR, Singapore, as well as 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 Google Scholar has the most up-to-date list of publications
* = joint first authors.
Q. Ho*, J. Yin*, E. P. Xing. Latent Space Inference of Internet Scale Networks. Journal of Machine Learning Research (JMLR 2016).
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. IEEE Transactions on Big Data (IEEE BigData 2016).
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]
Refereed Conference Papers
* = joint first authors.
J.-K. Kim, Q. Ho, S. Lee, X. Zheng, W. Dai, G. A. Gibson, E. P. Xing. STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning. Eurosys 2016.
L. Liao, Q. Ho, J. Jiang, E.-P. Lim. SLR: A Scalable Latent Role Model for Attribute Completion and Tie Prediction in Social Networks. IEEE International Conference on Data Engineering (ICDE 2016).
J. Wei, W. Dai, A. Qiao, H. Cui, Q. Ho, G. R. Ganger, P. B. Gibbons, G. A. Gibson, E. P. Xing. Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics. SOCC 2015: ACM Symposium on Cloud Computing (SOCC 2015). Best paper award.
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. 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. 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. 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]
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]
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, 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.
Q. Ho, Modeling Large Social Networks in Context. PhD Thesis, School of Computer Science, Carnegie Mellon University, 2014. [pdf] Winner of 2015 SIGKDD Dissertation Award (Runner-up).
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.