News
Lingyang Chu
Ph.D.
I am an assistant professor at McMaster University. Before joining McMaster University, I was a postdoc fellow at Simon Fraser University. I obtained my Ph.D. degree in Computer Science from University of Chinese Academy of Sciences.
Research Interest
My research interest lies broadly in the areas of data mining, machine learning and statistics. My recent research focuses on trustworthy artificial intelligence (AI), which includes privacy, interpretability, security, robustness, and fairness of modern machine learning models, especially, deep neural networks. I am also an expert in federated learning, graph based machine learning, and scalable data mining on large graphs. Many of my previous research outcomes have been successfully deployed as services and products of Huawei Cloud. For example, a personalized federated learning system was deployed as a core service on tens of millions of smart phones running Harmony OS.Recruiting
I am recruiting highly motivated students to conduct cutting-edge research on interpreting deep models, defending against adversarial attacks and building next-generation reliable computing frameworks.
The competition has been intense due to the large number of applicants each year. We keep a high standard in recruiting new students.
• 1 Ph.D. candidate position available starting from Jan. 2025.
• 2 intership positions for undergraduate students at McMaster.
• 1 position for CSC supported visiting graduate students.
• Please send me your transcript(s) and CV if interested.
• 1 Ph.D. candidate position available starting from Jan. 2025.
• 2 intership positions for undergraduate students at McMaster.
• 1 position for CSC supported visiting graduate students.
• Please send me your transcript(s) and CV if interested.
Selected Publications
Research is a life-long journey, with problems to solve, knowledges to gain, but most of all, experiences to enjoy.
•
Xin Che, Mohammad Akbari, Shaoxin Li, David Yue, Yong Zhang and Lingyang Chu
"Primary Key Free Watermarking for Numerical Tabular Datasets in Machine Learning".
(ICPR'24)
•
Huanzhang Zhu, Shaoxin Li and Lingyang Chu
"Multifaceted Anchor Nodes Attack on Graph Neural Networks: A Budget-efficient Approach".
(ICPR'24)
•
Jianbin Cui and Lingyang Chu
"Interpretable Deep Graph-level Clustering: A Prototype-based Approach".
(ICPR'24)
•
Yihao Zheng, Haocheng Xia, Junyuan Pang, Jinfei Liu, Kui Ren, Lingyang Chu, Yang Cao and Li Xiong
"TabularMark: Watermarking Tabular Datasets for Machine Learning".
(CCS'24)
•
Shaoxin Li, Xiaofeng Liao, Xin Che, Xintong Li, Yong Zhang and Lingyang Chu
"Cocktail Universal Adversarial Attack on Deep Neural Networks".
(ECCV'24)
•
Yu Zhang, Zhe Xue, Shilong Ou, Lingyang Chu, Junping Du and Yunfei Long
"Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction".
(AAAI'24)
•
Yunfei Long, Zhe Xue, Lingyang Chu, Tianlong Zhang, Junjiang Wu, Yu Zang and Junping Du
"FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data".
(ACM Multimedia'23, PDF)
•
Qiying Pan, Yifei Zhu and Lingyang Chu
"Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices".
(ICDE'23)
•
Laurent Charette*, Lingyang Chu*, Yizhou Chen, Jian Pei, Lanjun Wang and Yong Zhang
"Cosine Model Watermarking Against Ensemble Distillation".
(AAAI'22)
(* means equal contribution)
•
Peter Cho-Ho Lam*, Lingyang Chu*, Maxim Torgonskiy, Jian Pei, Yong Zhang and Lanjun Wang
"Finding Representative Interpretations on Convolutional Neural Networks".
(ICCV'21)
(* means equal contribution)
[PDF, Python Notebook on Huawei Cloud]
•
Mohit Bajaj*, Lingyang Chu*, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam and Yong Zhang
"Robust Counterfactual Explanations on Graph Neural Networks".
(Accepted by NeurIPS'21)
(* means equal contribution)
[PDF, CODE]
•
Zirui Zhou, Lingyang Chu, Changxin Liu, Lanjun Wang, Jian Pei and Yong Zhang.
"Towards Fair Federated Learning".
(KDD 2021 Tutorial)
[Homepage]
•
Xia Hu, Lingyang Chu, Jian Pei, Jiang Bian, Weiqing Liu.
"Deep Learning Model Complexity: Concepts and Approaches".
(SDM 2021 Tutorial)
[Homepage]
•
Zicun Cong, Lingyang Chu, Yu Yang and Jian Pei.
"Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test".
(Accepted by VLDB'21)
[PDF, CODE]
•
Yutao Huang*, Lingyang Chu*, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei and Yong Zhang.
"Personalized Cross-Silo Federated Learning on Non-IID Data".
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'21), 2021.
(* means equal contribution)
[PDF, SLIDES, CODE]
•
Prithu Banerjee, Lingyang Chu, Yong Zhang, Laks V.S. Lakshmanan and Lanjun Wang.
"Stealthy Targeted Data Poisoning Attack on Knowledge Graphs".
IEEE International Conference on Data Engineering (ICDE'21), 2021.
[PDF]
•
Lingyang Chu, Yanyan Zhang, Yu Yang, Lanjun Wang and Jian Pei.
"Online density bursting subgraph detection from temporal graphs".
Proceedings of the VLDB Endowment (VLDB'20), 2020.
[PDF]
•
Mingtao Lei, Xi Zhang, Lingyang Chu, Zhefeng Wang, Philip S. Yu and Binxing Fang.
"Finding Route Hotspots in Large Labeled Networks".
IEEE Transactions on Knowledge and Data Engineering (TKDE'20), 2020. [PDF]
•
Zicun Cong, Lingyang Chu, Lanjun Wang, Xia Hu and Jian Pei.
"Exact and consistent interpretation of piecewise linear models hidden behind APIs: A closed form solution".
IEEE International Conference on Data Engineering (ICDE'20).
[PDF]
•
Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang and Enhong Chen.
"Finding theme communities from database networks".
Proceedings of the VLDB Endowment (VLDB'19), 12(10): 1071-1084, 2019.
[PDF]
•
Zijin Zhao*, Lingyang Chu*, Dacheng Tao and Jian Pei.
"Classification with label noise: A Markov Chain sampling framework".
Data Mining and Knowledge Discovery (DMKD'19), 33(5): 1468-1504, 2019. (* means equal contribution)
[PDF]
•
Mingtao Lei, Lingyang Chu, Zhefeng Wang, Jian Pei, Caifeng He, Xi Zhang and Binxing Fang.
"Mining top-k sequential patterns in transaction database graphs".
World Wide Web (WWW'19), 1-28, 2019.
[PDF]
•
Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang and Jian Pei.
"Exact and consistent interpretation for piecewise linear neural networks: A closed form solution".
ACM SIGKDD Conferences on Knowledge Discovery and Data Mining (KDD'18), 1244-1253, 2018.
[PDF, CODE]
•
Yu Yang, Lingyang Chu, Yanyan Zhang, Zhefeng Wang, Jian Pei and Enhong Chen.
"Mining density contrast subgraphs".
IEEE International Conference on Data Engineering (ICDE'18), 221-232, 2018.
[PDF]
•
Zhefeng Wang, Yu Yang, Jian Pei, Lingyang Chu and Enhong Chen.
"Activity maximization by effective information diffusion in social networks".
IEEE Transactions on Knowledge and Data Engineering (TKDE'17), 29(11): 2374-2387, 2017.
[PDF]
•
Lingyang Chu, Zhefeng Wang, Jian Pei, Jiannan Wang, Zijin Zhao and Enhong Chen.
"Finding gangs in war from signed networks".
ACM SIGKDD Conferences on Knowledge Discovery and Data Mining (KDD'16), 1505-1514, 2016.
[PDF, CODE]
•
Lingyang Chu, Shuhui Wang, Siyuan Liu, Qingming Huang and Jian Pei.
"ALID: Scalable dominant cluster detection".
Proceedings of the VLDB Endowment (VLDB'15), 8(8): 826-837, 2015.
[PDF, CODE]
•
Lingyang Chu, Yanyan Zhang, Guorong Li, Shuhui Wang, Weigang Zhang and Qingming Huang.
"Effective multi-modality fusion framework for cross-media topic detection".
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT'14), 26(3): 556-569, 2014.
[PDF]
•
Lingyang Chu, Shuhui Wang, Yanyan Zhang, Shuqiang Jiang and Qingming Huang.
"Graph density based visual word vocabulary for image retrieval".
International Conference on Multimedia & Expo (ICME'14), 1-6, 2014.
[PDF]
•
Lingyang Chu, Shuqiang Jiang, Shuhui Wang, Yanyan Zhang and Qingming Huang.
"Robust spatial consistency graph model for partial duplicate image retrieval".
IEEE Transactions on Multimedia (TMM'13), 15(8): 1982-1996, 2013.
[PDF]
•
Yanyan Zhang, Guorong Li, Lingyang Chu, Shuhui Wang, Weigang Zhang and Qingming Huang.
"Cross media topic detection: a multi-modality fusion framework".
International Conference on Multimedia & Expo (ICME'13), 1-6, 2013. ( Best paper candidate)
[PDF]
•
Shuang Wang, Yunfeng Xue, Lingyang Chu, Yuhao Jiang and Shuqiang Jiang.
"ObjectSense: a scalable multi-objects recognition system based on partial duplicate image retrieval".
International Conference on Multimedia Retrieval (ICMR'13), 317-318, 2013. (Best demo award)
[PDF]
•
Lingyang Chu, Shuqiang Jiang and Qingming Huang.
"Fast common visual pattern detection via radiate geometric model".
International Conference on Image Processing (ICIP'11), 2465-2468, 2011.
[PDF]
Students & Interns
"The dream begins, most of the time, with a teacher who believes in you, who tugs and pushes and leads you on to the next plateau, sometimes poking you with a sharp stick called truth."— Dan Rather
•
Zicun Cong (Ph.D. | Data Science Manager at Zscaler)
•
Zhefeng Wang (Ph.D. | Now at Huawei, China)
•
Mingtao Lei (Ph.D. | Now at Huawei, China)
•
Zijin Zhao (M.Sc. | Now at Amazon, Canada)
•
Yanyan Zhang (M.Sc. | Now at CitiBank, Canada)
•
Fasil Cheema (Ph.D. Candidate at McMaster | 2023 - 2026)
•
Qiqi Zhang (M.Sc. at McMaster 2021-2023 | Ph.D. Candidate at McMaster 2023 - 2026)
•
Xing Li (M.Sc. Candidate at McMaster 2022-2023 | Ph.D. Candidate at McMaster 2023 - 2026)
•
Shaoxin Li (Ph.D. Candidate at Chongqing University | Visiting during 2022-2024)
•
Xin Che (M.Sc. Candidate at McMaster 2021-2022 | Ph.D. Candidate at McMaster 2022 - 2025)
•
Siyang Zhang (Ph.D. at BUPT | Visiting during 2021-2022)
•
Yuanqi Xue (M.Sc. Candidate at McMaster | 2024 - 2026)
•
Xinyu Ma (M.Sc. Candidate at McMaster | 2023 - 2025)
•
Hao Zhang (M.Sc. Candidate at McMaster | 2023 - 2025)
•
Huanzhang Zhu (M.Sc. Candidate at McMaster 2022-2024)
•
Catherine Li (M.Eng. Candidate at McMaster | 2024 - 2026)
•
Chunjie Lu (M.Eng. Candidate at McMaster | 2024 - 2026)
•
Jianbin Cui (M.Eng. Candidate at McMaster 2022-2024)
•
Xintong Li (M.Sc. Candidate at University of Toronto | Internship Jan.~April 2022)
•
Zicheng Guo (B.Sc. at McMaster | Internship Jan.~April 2022)
•
Prithu Banerjee (Ph.D. at the University of British Columbia)
•
Yutao Huang (Ph.D. at Simon Fraser University)
•
Yongwei Wang (Ph.D. at the University of British Columbia)
•
Xinglu Wang (Ph.D. candidate at Simon Fraser University)
•
Chirong Zhang (Ph.D. candidate at Simon Fraser University)
•
Yuanqi Xue (B.Sc. candidate at McMaster | Internship April 2023 - April 2024)
•
Qi Zhao (B.Sc. candidate at McMaster | Internship April 2023 - April 2024)
•
Ziyu Xue (Undergraduate student at University of British Columbia)
Academic Services
I am honored to constantly serve the following roles in service to the academic community.
•
The Conference on Neural Information Processing Systems
•
The ACM SIGMOD/PODS Conference
•
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
•
The ACM SIGMOD/PODS Conference
•
The SIGKDD Conference on Knowledge Discovery and Data Mining
•
The IEEE/CVF Conference on Computer Vision and Pattern Recognition
•
International Conference on Very Large Data Bases
•
The Web Conference (previously known as WWW).
•
The ACM International Conference on Multimedia.
•
The International ACM SIGIR Conference on Research and Development in Information Retrieval.
•
The AAAI Conference on Artificial Intelligence.
•
SIAM International Conference on Data Mining
•
The ACM International Conference on Information and Knowledge Management.
•
The ACM International Conference on Web Search and Data Mining
•
The International Conference on Database Systems for Advanced Applications.
•
The Pacific-Asia Conference on Knowledge Discovery and Data Mining.
•
IEEE International Conference on Multimedia and Expo 2019
•
IEEE Transactions on Knowledge and Data Engineering
•
IEEE Transactions on Multimedia
•
ACM Transactions on Knowledge Discovery from Data
•
Data Mining and Knowledge Discovery
•
Knowledge and Information Systems
•
Journal of Data and Information Quality
•
ACM Transactions on Knowledge Discovery from Data (Associate Editor)
•
The Research Grants Council (RGC) of Hong Kong.
Last modified on August 31, 2024.