Ling Huang is an AI researcher, entrepreneur, and technology leader whose career has spanned academic research, large-scale AI engineering, and financial technology. He earned his Ph.D. in Computer Science at University of California at Berkeley, affiliated with RadLab. Ling served as a Senior Research Scientist at Intel Labs Berkeley, where he worked on scalable machine learning, graph mining, AI security, distributed anomaly detection, and early research in adversarial machine learning. He has authored more than 60 papers with over 13,000 citations.

Over the past decade, Ling has focused on turning advanced AI research into real-world systems. He was a founding member and the Director of Data Science at DataVisor, Inc . and later founded AHI Fintech, Inc, where he led the development of AI-powered anti-fraud and anti-money-laundering platforms used by dozens of financial institutions. The company raised two RMB 100 million-scale financing rounds.

Ling also serves as Chief AI Scientist at Bairong, Inc., where he leads research on LLM post-training, agentic reinforcement learning, and self-evolving agents and turns it into production systems for financial marketing, customer service, and compliance.

Ling can be reached by huang.ling at gmail.com.
Research Ling has been associated with the following projects:

2026 Sell More, Play Less: Benchmarking LLM Realistic Selling Skill. Xuanbo Su, Wenhao Hu, Le Zhan, Yuting Xie, Kailin Lyu, Kaijie Chen, Ziwei Li, Haibo Su, Yunzhang Chen, Ling Huang. In Proceedings of EMNLP 2026. [pdf] [Project Homepage]
Mistake Notebook Learning: Batch-Clustered Failures for Training-Free Agent Adaptation. Xuanbo Su, Yingfang Zhang, Hao Luo, Xiaoteng Liu, Ling(Leo) Huang. In Proceedings of ACL 2026 Findings. [pdf] [Project Homepage]
SPDZCoder: Combining Expert Knowledge with LLMs for Generating Privacy-Computing Code. Xiaoning Dong, Peilin Xin, Ling Huang, Yanlin Wang, Jia Li, Wei Xu. In ACM Transactions on Software Engineering and Methodology (TOSEM) 2026. [pdf]
2023 Importance Guided Query Focused Long-Input Summarization. Shuaiyao Ning, Caixia Yuan, Xiaojie Wang, Ling Huang. In Proceedings of CCIS 2023. [pdf]
Manipulating Multi-Agent Navigation Task Via Emergent Communications. Han Yu, Wenjie Shen, Ling Huang, Caixia Yuan. In Proceedings of CCIS 2023. [pdf]
2020 Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework. Han Zhang, Wenhao Zheng, Charley Chen, Kevin Gao, Yao Hu, Ling Huang and Wei Xu. In Proceedings of The Web Conference (WWW) 2020. [pdf]
FDHelper: Assist Unsupervised Fraud Detection Experts with Interactive Feature Selection and Evaluation. Jiao Sun, Yin Li, Charley Chen, Jiahe Lee, Xin Liu, Zhongping Zhang, Ling Huang, Lei Shi and Wei Xu. In Proceedings of CHI 2020. [pdf]
2019 DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction. Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu and Wei Xu. In Proceedings of ACL 2019. [pdf]
2018 Do Not Pull My Data for Resale: Protecting Data Providers using Data Retrieval Pattern Analysis. Guosai Wang, Shiyang Xiang, Yitao Duan, Ling Huang and Wei Xu. In Proceedings of SIGIR 2018 (Short paper). [pdf]
2016 Reviewer Integration and Performance Measurement for Malware Detection. B. Miller, A. Kantchelian, S. Afroz, R. Bachwani, R. Faizullabhoy, L. Huang, V. Shankar, M.C. Tschantz, T. Wu, G. Yiu, A.D. Joseph, J.D. Tygar. In 13th Conference on Detection of Intrusions, Malware & Vulnerability Assessment (DIMVA), July 2016. [pdf]
2015 CloudKeyBank: Privacy and Owner Authorization Enforced Key Management Framework. XiuXia Tian, Ling Huang, Tony Wu, Xiaoling Wang, Aoying Zhou. In IEEE Trans. Knowl. Data Eng. 27(12): 3217-3230, December 2015 [pdf]
What You Submit is Who You Are: A Multi-Modal Approach for Deanonymizing Scientific Publications. Mathias Payer, Ling Huang, Neil Gong, Kevin Borgolte, Mario Frank. In IEEE Transactions on Information Forensics and Security (TIFS), January 2015 [pdf]
2014 Large-Margin Convex Polytope Machine. A. Kantchelian, M. C. Tschantz, L. Huang, P. L. Bartlett, A. D. Joseph, J. D. Tygar. In Neural Information Processing Systems (NIPS) 2014, December 2014. [pdf]
Adversarial Active Learning. Brad Miller, Alex Kantchelian, Sadia Afroz, Rekha Bachwani, Edwin Dauber, Ling Huang, Michael Tschantz, Anthony Joseph, Doug Tygar. In ACM Workshop on Artificial Intelligence and Security (AISec), November 2014 [pdf]
I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis. Brad Miller, Ling Huang, Anthony Joseph, Doug Tygar. In Privacy Enhancing Technologies Symposium (PETS), July 2014 [pdf][Project Homepage] Best Student Paper Award!
SAFE: Secure Authentication with Face and Eyes. Arman Boehm, Dongqu Chen, Mario Frank, Ling Huang, Cynthia Kuo, Tihomir Lolic, Ivan Martinovic, Dawn Song. In Proceedings of PRISMS, July 2014 [pdf]
Joint Link Prediction and Attribute Inference using a Social-Attribute Network. Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song. In ACM Transactions on Intelligent Systems and Technology (TIST), 5(2) July 2014 [pdf]
2013 Approaches to Adversarial Drift. A. Kantchelian, S. Afroz, L. Huang, A. C. Islam, B. Miller, M. C. Tschantz, R. Greenstadt, A. D. Joseph, J. D. Tygar. In ACM Workshop on Artificial Intelligence and Security (AISec), November 2013 [pdf]
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 and Petros Maniatis In Proceedings of USENIX Annual Technical Conference (USENIX), August 2013. [pdf]
2012 Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+. Neil Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, Dawn Song. In Proceedings of ACM/USENIX Internet Measurement Conference (IMC) , November 2012. [pdf]
Robust Detection of Comment Spam Using Entropy Rate. Alex Kantchelian, Justin Ma, Ling Huang, Sadia Afroz, Anthony Joseph, Doug Tygar. In ACM Workshop on Artificial Intelligence and Security (AISEC), October 2012. [pdf]
Juxtapp: A Scalable System for Detecting Code Reuse Among Android Applications(Link). Steve Hanna, Ling Huang, Edward Wu, Saung Li, Charles Chen, Dawn Song. In Proceedings of the 9th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA), July 2012. [pdf]
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. Benjamin Rubinstein, Peter Barlett, Ling Huang, Nina Taft In Journal of Privacy and Confidentiality, July 2012. [pdf]
2011 Adversarial Machine Learning. Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein and J. D. Tygar. In Proceedings of the 4th Workshop on Artificial Intelligence and Security (AISec), October 2011. [pdf]
2010 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. To appear 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. To appear 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. To appear 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. To appear 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] Best paper award!
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]
2009 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]
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]
2008 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]
Branislav Kveton and Milos Hauskrecht. 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].
2007 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].
Pre-2007 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]
Linear Program Approximations for Factored Continuous-State Markov Decision Processes.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].
Technical Reports 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]