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Intel Labs
Intel Labs Pittsburgh
Intel Labs Seattle
Ling Huang
Intel Research Berkeley
2150 Shattuck Ave, Suite 1300
Berkeley, CA 94704-1347 USA
T +1-510-495-3406
F +1-510-495-3049
ling.huang@intel.com

Biography

Ling Huang is a research scientist in Intel Labs Berkeley, and is a member of Everyday Sensing and Perception (ESP) project. His primary research interests are in machine learning, distributed systems, system monitoring and diagnosis, with focus on making computer systems more intelligent in understanding and interacting with human and themselves.

Ling joined Intel Labs Berkeley in October 2007, immediately after getting his Ph.D. from Computer Science at University of California at Berkeley. During his Ph.D. study, he was affiliated with RadLab. Prior to UC Berkeley, he obtained B.S. and M.S. degree from Beijing University of Aeronautics and Astroautics (BUAA) in China, and worked more than three years as a system architect and project manager at Bei Hang Haire CAXA, the No.1 CAD/CAM software company in China.

Research

Ling is or has been associated with the following projects:

Recent Projects

Previous Projects

Publications

    2010

  1. Classifier Evasion: Models and Open Problems

    Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph and J. D. Tygar. To appear in ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, Sept. 2010. [pdf]

  2. Online Semi-Supervised Learning on Quantized Graphs

    Michal Valko, Branislav Kveton, Daniel Ting, Ling Huang. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI) , July 2010. [pdf]

  3. An Analysis of the Convergence of Graph Laplacians

    Daniel Ting, Ling Huang, Michael I. Jordan. To appear in Proceedings of the 27th International Conference on Machine Learning (ICML), June 2010. [pdf]

  4. Detecting Large-Scale System Problems by Mining Console Logs

    Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael Jordan. To appear in Proceedings of the 27th International Conference on Machine Learning (ICML) (Invited Application Paper), June 2010. [pdf]

  5. Online Semi-Supervised Perception: Real-Time Learning without Explicit Feedback

    Branislav Kveton, Michal Valko, Matthai Philipose, Ling Huang. In Proceedings of the 4th IEEE Online Learning for Computer Vision Workshop (OLCV) , 2010. [pdf] Awarded best paper!

  6. Semi-Supervised Learning with Max-Margin Graph Cuts

    Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), May 2010. [pdf]

  7. Near Optimal Evasion of Convex-Inducing Classifiers

    Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven Lee, Satish Rao, Anthony Tran and J. D. Tygar. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), May 2010. [pdf]

    8. 2009

  8. Online System Problem Detection by Mining Patterns of Console Logs

    Wei Xu, Ling Huang, Armando Fox, David Patterson and Michael I. Jordan. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2009) , Miami, December 2009. [pdf]

  9. ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors

    Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft and J. D. Tygar. In Proceedings of 2009 Internet Measurement Conference (IMC'09), Chicago, November 2009. [pdf]

  10. Detecting Large-Scale System Problems by Mining Console Logs

    Wei Xu, Ling Huang, Armando Fox, David Patterson and Michael I. Jordan. In Proceedings of the 22nd ACM Symposium on Operating Systems Principles (SOSP'09), Big Sky, October 2009. [pdf]

  11. Debating IT Monoculture for End Host Intrusion Detection

    Dhiman Barman, Jaideep Chandrashekar, Michalis Faloutsos, Ling Huang, Nina Taft, Frederic Giroire. In Proceedings of SIGCOMM 2009 WREN Workshop (WREN'09), Barcelona, Spain, August 2009. [pdf]

  12. Fast Approximate Spectral Clustering

    Donghui Yan, Ling Huang and Michael I. Jordan. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD'09), Paris, France, June 2009. [pdf]

  13. Compromising and Defending PCA-based Anomaly Detectors for Network-Wide Traffic

    Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft and J. D. Tygar. In Proceedings of the 2009 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS 2009), 2009. [Extended Abstract]

  14. Fast Approximate Spectral Clustering

    Donghui Yan, Ling Huang, and Michael I. Jordan. Technical report, Department of Statistics, UC Berkeley, 2009. [pdf]

    15. 2008

  15. Mining Console Logs for Large-Scale System Problem Detection

    Wei Xu, Ling Huang, Armando Fox, David Patterson and Michael I. Jordan. In Proceedings of the Third Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML) , San Diego, December 2008. [pdf]

  16. Spectral Clustering with Perturbed Data

    Ling Huang, Donghui Yan, Michael I. Jordan and Nina Taft. In Advances in Neural Information Processing Systems (NIPS) 21, Vancouver, B.C, December 2008. [pdf]

  17. Support vector machines, data reduction and approximate kernel matrice

    XuanLong Nguyen, Ling Huang, and Anthony D. Joseph. To appear in Proceedings of European Conference on Machine Learning (ECML), Belgium, September, 2008. [pdf]

  18. Compromising PCA-based Anomaly Detectors for Network-Wide Traffic

    Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft and Doug Tygar. UC Berkeley Technical Report No. UCB/EECS-2008-73 , May 2008. [ pdf].

    19. 2007

  19. Approximate Decision Making in Large-Scale Distributed Systems

    Ling Huang, Minos Garofalakis, Anthony D. Joseph and Nina Taft. In NIPS Workshop: Statistical Learning Techniques for Solving Systems Problems (MLSys). Vancouver, B.C, December 2007. [pdf]

  20. Communication-Efficient Tracking of Distributed Cumulative Triggers

    Ling Huang, Minos Garofalakis, Anthony D. Joseph and Nina Taft. In Proceedings of the International Conference on Distributed Computing Systems (ICDCS'07). Toronto, Canada, June 2007. [pdf].

  21. Communication-Efficient Online Detection of Network-Wide Anomalies

    Ling Huang, XuanLong Nguyen, Minos Garofalakis, Joseph Hellerstein, Anthony D. Joseph, Michael Jordan and Nina Taft. In Proceedings of the 26th Annual IEEE Conference on Computer Communications (INFOCOM'07). Anchorage, Alaska, May 2007. [pdf].

    22. 2006

  22. In-Network PCA and Anomaly Detection

    Ling Huang, XuanLong Nguyen, Minos Garofalakis, Anthony Joseph, Michael Jordan and Nina Taft. In Advances in Neural Information Processing Systems (NIPS) 19. Vancouver, B.C, December 2006. [pdf], [[longer version]]

  23. Toward Sophisticated Detection With Distributed Triggers

    Ling Huang, Minos Garofalakis, Joseph Hellerstein, Anthony D. Joseph and Nina Taft. In SIGCOMM 2006 Workshop on Mining Network Data (MineNet-06). [pdf]

    24. Pre-2006

  24. Rapid Mobility via Type Indirection

    Ben Y. Zhao, Ling Huang, Anthony D. Joseph and John D. Kubiatowicz. In Proceedings of the 3rd International Workshop on Peer-to-Peer Systems (IPTPS), San Diego, CA. Feb. 2004. [pdf]

  25. Tapestry: A Resilient Global-scale Overlay for Service Deployment

    Ben Y. Zhao, Ling Huang, Jeremy Stribling, Sen C. Rhea, Anthony D. Joseph, and John Kubiatowicz. In IEEE Journal on Selected Areas in Communications, January 2004, Vol. 22, No. 1, Pgs. 41-53. [pdf]

  26. Exploiting Routing Redundancy via Structured Peer-to-Peer Overlays

    Ben Y. Zhao, Ling Huang, Jeremy Stribling, Anthony D. Joseph and John D. Kubiatowicz. In Proceedings of the 11th IEEE International Conference on Network Protocols (ICNP'03), November 2003. [pdf]

  27. Approximate Object Location and Spam Filtering on Peer-to-Peer Systems

    Feng Zhou, Li Zhuang, Ben Zhao, Ling Huang, Anthony Joseph and John Kubiatowicz. In Proceedings of ACM/IFIP/USENIX International Middleware Conference (Middleware 2003). [pdf]

  28. Brocade: Landmark Routing on Overlay Networks

    Ben Y. Zhao, Yitao Duan, Ling Huang, Anthony D. Joseph, and John D. Kubiatowicz. In Proceedings of First International Workshop on Peer-to-Peer Systems (IPTPS), Cambridge, MA. March 2002. [pdf].

    29. Technical Reports

  29. Support vector machines, data reduction and approximate kernel matrices

    XuanLong Nguyen, Ling Huang, and Anthony D. Joseph. SAMSI Technical report No. 2008-3, April, 2008. [pdf]

  30. Communication-Efficient Tracking of Distributed Cumulative Triggers

    Ling Huang, Minos Garofalakis, Anthony D. Joseph and Nina Taft. UC Berkeley Technical Report No. UCB/EECS-2006-139, 2006. [pdf]

  31. Probabilistic Data Aggregation in Distributed System

    Ling Huang, Ben Y. Zhao, Anthony D. Joseph and John D. Kubiatowicz. UC Berkeley Technical Report No. UCB/EECS-2006-11, 2006. [pdf]

  32. Exploiting Routing Redundancy Using a Wide-area Overlay

    Ben Y. Zhao, Ling Huang, Anthony D. Joseph and John D. Kubiatowicz. UC Berkeley Technical Report No. UCB/CSD-02-1215, 2002. [pdf]

  33. Construction of Blending Surfaces

    Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang04, Beijing University of Aeronautics & Astronautics, 2000. [html]

  34. A Practical Algorithm for Surface/Surface Intersection

    Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang03, Beijing University of Aeronautics & Astronautics, 1997. [html]

  35. A Surface Interpolating Method for 3D Curves-Nets

    Ling Huang, Jian Feng Zhen, Xinxiong Zhu and LeiYi. Technical Report, HZ-TMSurf-Huang02, Beijing University of Aeronautics & Astronautics, 1996. html.

  36. An Approach for Approximating Arbitrary Curves by NURBS

    Ling Huang, Jian Feng Zhen and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang01, Beijing University of Aeronautics & Astronautics, 1995. html.
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