Ling Huang

Intel Labs and
Intel Science And Technology Center on Secure Computing at UC Berkeley
711 Soda Hall 711
Berkeley, CA 94720-1776, USA
T: +1-510-643-8691
huang.ling@gmail.com

Biography

Ling Huang is a research scientist at Intel Labs. He is currently a member of the Intel Science and Technology Center on Secure Computing at UC Berkeley. Between 2007 and 2011, he was a research scientist at Intel Labs Berkeley. He received 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 CAXA, the No.1 CAD/CAM software company in China.

Research

Ling Huang's research interests are in machine learning, distributed systems and security, with focus on efficient and distributed machine learning methods for large-scale data processing; machine learning for computer vision, distributed systems and security; system modeling, problem detection and diagnosis; etc. He is or has been associated with the following projects:

Previous Projects

Publications

    2013

  1. Approaches to Adversarial Drift

    Alex Kantchelian, Sadia Afroz, Ling Huang, Aylin Caliskan Islam, Brad Miller, Michael Carl Tschantz, Rachel Greenstad, Anthony D. Joseph and J. D. Tygar. To appear in the 6th ACM Workshop on Artificial Intelligence and Security (AISEC) , November 2013.
  2. SAFE: Secure Authentication with Face and Eyes

    Arman Boehm, Dongqu Chen, Mario Frank, Ling Huang, Cynthia Kuo, Tihomir Lolic, Ivan Martinovic and Dawn Song. In Proceedings of PRISMS 2013. [pdf]
  3. Mantis: Automatic Generation of Efficient Performance Predictors for Smartphone Applications

    Yongin Kwon, Sangmin Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-Gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, Yunheung Paek. In Proceedings of USENIX Annual Technical Conference (USENIX 2013). [pdf]
  4. Joint Link Prediction and Attribute Inference using a Social-Attribute Network

    Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine(Runting) Shi and Dawn Song. In ACM Transaction on Intelligent Systems and Technology (ACM TIST), 2013.
  5. 2012

  6. Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+

    Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar and Dawn Song. In Proceedings of ACM/USENIX Internet Measurement Conference (IMC), November 2012. [pdf]
  7. Robust Detection of Comment Spam Using Entropy Rate

    Alex Kantchelian, Justin Ma, Ling Huang, Sadia Afroz, Anthony Joseph and J. D. Tygar. In the 5th ACM Workshop on Artificial Intelligence and Security (AISEC) , October 2012. [pdf]
  8. Smartphones: Not Smart Enough?

    Ian Fischer, Cynthia Kuo, Ling Huang and Mario Frank. In he 2nd ACM CCS Workshop on Security and Privacy in Mobile Devices (SPSM), October 2012. [pdf]
  9. Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)

    Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine(Runting) Shi and Dawn Song. In Proceedings of the Sixth SNA-KDD Workshop (SNA-KDD'12), August 2012. [pdf]
  10. Juxtapp: A Scalable System for Detecting Code Reuse Among Android Applications

    Steve Hanna, Ling Huang, Edward Wu, Saung Li, Charles Chen and Dawn Song. In Proceedings of the 9th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA), July 2012. [pdf] [code]
  11. Query Strategies for Evading 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 Journal of Machine Learning Research, May 2012. [pdf]
  12. 2011

  13. Adversarial Machine Learning

    Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein and J. D. Tygar. To appear in Proceedings of the 4th Workshop on Artificial Intelligence and Security, October 2011.
  14. Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

    Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang and Nina Taft. In Journal of Privacy and Confidentiality, 2011. [pdf]
  15. 2010

  16. Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression

    Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Mayur Naik and Petros Maniatis. In Advances in Neural Information Processing Systems (NIPS) 23, Vancouver, B.C, December 2010. [pdf] [Supplementary]
  17. Experience on Mining Google's Production Console Logs

    Wei Xu, Ling Huang, Armando Fox, David Patterson and Michael Jordan. In the Workshop on Managing Systems via Log Analysis and Machine Learning Techniques (SLAML), October 2010. [pdf]
  18. Classifier Evasion: Models and Open Problems

    Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph and J. D. Tygar. In ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, September 2010. [pdf]
  19. 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]
  20. An Analysis of the Convergence of Graph Laplacians

    Daniel Ting, Ling Huang, Michael I. Jordan. In Proceedings of the 27th International Conference on Machine Learning (ICML), June 2010. [pdf]
  21. Detecting Large-Scale System Problems by Mining Console Logs

    Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael Jordan. In Proceedings of the 27th International Conference on Machine Learning (ICML) (Invited Application Paper), June 2010. [pdf]
  22. 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!
  23. 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]
  24. 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]
  25. 2009

  26. 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]
  27. 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]
  28. 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]
  29. 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]
  30. 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]
  31. 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]
  32. Fast Approximate Spectral Clustering

    Donghui Yan, Ling Huang, and Michael I. Jordan. Technical report, Department of Statistics, UC Berkeley, 2009. [pdf]
  33. 2008

  34. 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]
  35. 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]
  36. 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]
  37. 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].
  38. 2007

  39. 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]
  40. 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].
  41. 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].
  42. 2006

  43. 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]]
  44. 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]
  45. Pre-2006

  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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].
  51. Technical Reports

  52. 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]
  53. 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]
  54. 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]
  55. 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]
  56. Construction of Blending Surfaces

    Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang04, Beijing University of Aeronautics & Astronautics, 2000. [html]
  57. A Practical Algorithm for Surface/Surface Intersection

    Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang03, Beijing University of Aeronautics & Astronautics, 1997. [html]
  58. 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.
  59. 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.