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
ling.huang@intel.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:- Fast and Efficient Machine Learning Methods
- Online Semi-Supervised Learning and Face Recognition
- Everyday Sensing and Perception
- Console Log Mining for System Problem Detection
- Decentralized Machine Learning and Efficient Anomaly Detection
Previous Projects
- Communication-Efficient Tracking of Distributed Triggers
- Probabilistic Data Aggregation and Multi-Scale Prediction
- Geometric Modeling for CAD/CAM system
- Fault-resilient Overlay Routing
- Tapestry: Scalable and Resilient Peer-to-Peer network infrastructure
- SpamWatch: A Peer-to-peer Spam Filtering System
Publications
-
Adversarial Machine Learning
Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein and J. D. Tygar. To appear in the Proceedings of the 4th Workshop on Artificial Intelligence and Security, October 2011. -
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] -
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, 2011. [pdf] -
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] -
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] -
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] -
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] -
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] -
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] -
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! -
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] -
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] -
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] -
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] -
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] -
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] -
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] -
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] -
Fast Approximate Spectral Clustering
Donghui Yan, Ling Huang, and Michael I. Jordan. Technical report, Department of Statistics, UC Berkeley, 2009. [pdf] -
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] -
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] -
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] -
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]. -
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] -
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]. -
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]. -
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]] -
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] -
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] -
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] -
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] -
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] -
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]. -
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] -
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] -
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] -
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] -
Construction of Blending Surfaces
Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang04, Beijing University of Aeronautics & Astronautics, 2000. [html] -
A Practical Algorithm for Surface/Surface Intersection
Ling Huang and Xinxiong Zhu. Technical Report, HZ-TMSurf-Huang03, Beijing University of Aeronautics & Astronautics, 1997. [html] -
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. -
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.