Education

• Ph.D. in Computer Science, University of Washington

Work experience

• 2022-present: Assistant Professor of Computer Science
  • University of Maryland
  • Instructor of CMSC 421 Fall 2022-2023: Introduction to Artificial Intelligence
  • Instructor of CMSC 828A Spring 2023: Fantastic Machine Learning Paradigms and Where to Use Them
• 2021-2022: Visiting Research Scientist
  • Google
• 2013-2021: Research Assistant
  • University of Washington
  • Supervisor: Prof. Jeff A. Bilmes
• Spring 2019: Teaching Assistant
  • University of Washington
  • ECE 596A Spring 2019: Advanced introduction to Neural Networks
  • Instructor: Prof. Jeff A. Bilmes
• Winter 2019: Teaching Assistant
  • University of Washington
  • ECE 596A Winter 2019: Advanced introduction to machine learning
  • Instructor: Prof. Jeff A. Bilmes
• Winter 2014: Teaching Assistant
  • University of Washington
  • CSEP 546 Winter 2014: Data Mining/Machine Learning
  • Instructor: Prof. Carlos Guestrin
• Summer 2015: Research Intern
  • Yahoo! Labs, Sunnyvale
  • Supervisor: Dr. Hua Ouyang and Prof. Yi Chang
• Summer 2014: Research Intern
  • Microsoft Research, Redmond
  • Supervisor: Dr. Lin Xiao
• 2011-2013: Research Assistant
  • University of Technology, Sydney (UTS)
  • Supervisor: Prof. Dacheng Tao
• 2009-2011: Research Assistant
  • Nanyang Technological University (NTU)
  • Supervisor: Prof. Dacheng Tao

Selected Publications

A full list of publications can be found here.

Preprints can be found on my Google Scholar page.

• Curriculum Learning
  1. Yucheng Yang, Tianyi Zhou, Lei Han, Meng Fang, Mykola Pechenizkiy, “Automatic Curriculum for Unsupervised Reinforcement Learning”, International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024.
  2. Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu, “A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2024.
  3. Chengkai Hou, Jieyu Zhang, Tianyi Zhou, “When to Learn What: Model-Adaptive Data Augmentation Curriculum”, International Conference on Computer Vision (ICCV), 2023. PDF
  4. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang, “EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning”, International Conference on Machine Learning (ICML), 2022. PDF
  5. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Curriculum Learning by Optimizing Learning Dynamics”, International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. PDF, Appendix
  6. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Robust Curriculum Learning: from clean label detection to noisy label self-correction”, International Conference on Learning Representations (ICLR), 2021. PDF, Slides
  7. Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini and Arvind Krishnamurthy, “AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly”, International Conference on Learning Representations (ICLR), 2021. PDF, Slides, Code
  8. Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang, “CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. PDF, Appendix
  9. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Curriculum Learning with Dynamic Instance Hardness”, Advances in Neural Information Processing Systems 34 (NeurIPS), 2020. PDF, Appendix, Slides, Code
  10. Tianyi Zhou*, Shengjie Wang*, and Jeff A. Bilmes, “Time-Consistent Self-Supervision for Semi-Supervised Learning”, International Conference on Machine Learning (ICML), 2020. PDF, Appendix, Slides+Talk
  11. Meng Fang, Tianyi Zhou, Yali Du, Lei Han, and Zhengyou Zhang, “Curriculum-guided Hindsight Experience Replay”, Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, 2019. PDF, Appendix, Code, Poster
  12. Tianyi Zhou, Shengjie Wang, and Jeff A. Bilmes, “Diverse Ensemble Evolution: Curriculum Data-Model Marriage”, Advances in Neural Information Processing Systems 32 (NeurIPS), Montreal, QC, Canada, 2018. PDF, Appendix, Poster
  13. Tianyi Zhou and Jeff A. Bilmes, “Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity”, Sixth International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, 2018. PDF
  14. Tianyi Zhou, Jeff A. Bilmes and Carlos Guestrin, “Divide-and-Conquer Learning by Anchoring a Conical Hull”, Twenty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2014. PDF
  15. Tianyi Zhou, Wei Bian, and Dacheng Tao, “Divide-and-Conquer Anchoring for Near Separable Nonnegative Matrix Factorization and Completion in High Dimensions”, IEEE International Conference on Data Mining (ICDM), pp., Dallas, TX, USA, 2013. ( Best Student Paper Award ) PDF, Slides
• Reinforcement Learning
  1. Yucheng Yang, Tianyi Zhou, Qiang He, Lei Han, Mykola Pechenizkiy, Meng Fang, “Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning”, International Conference on Learning Representations (ICLR), 2024. ( Spotlight )PDF
  2. Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi, “Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation”, International Conference on Learning Representations (ICLR), 2024. PDF
  3. Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi, “Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023.
  4. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang, “EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning”, International Conference on Machine Learning (ICML), 2022. PDF
  5. Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang, “CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. PDF, Appendix
  6. Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi, “Pareto Policy Pool for Model-based Offline Reinforcement Learning”, International Conference on Learning Representations (ICLR), 2022. PDF
  7. Meng Fang, Tianyi Zhou, Yali Du, Lei Han, and Zhengyou Zhang, “Curriculum-guided Hindsight Experience Replay”, Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, 2019. PDF, Appendix, Code, Poster
  8. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang and Chengqi Zhang, “Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling”, International Joint Conferences on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018. PDF, Code
• Natural Language Processing
  1. Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin, “Alpagasus: Training a Better Alpaca Model with Fewer Data”, International Conference on Learning Representations (ICLR), 2024. PDF
  2. Yibin Lei, Yu Cao, Tianyi Zhou, Tao Shen, Andrew Yates, “Corpus-Steered Query Expansion with Large Language Models”, The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2024.
  3. Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou, “How Many Demonstrations Do You Need for In-context Learning?”, The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2023. PDF
  4. Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao, “Merging Experts into One: Improving Computational Efficiency of Mixture of Experts”, The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023. PDF
  5. Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao, “TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack”, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
  6. Le Hou*, Richard Yuanzhe Pang*, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou, “Token Dropping for Efficient BERT Pretraining”, Annual Meeting of the Association for Computational Linguistics (ACL), 2022. PDF
  7. Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy, “Phrase-level Textual Adversarial Attack with Label Preservation”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL Findings), 2022. PDF
  8. Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang, “Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction”, The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2021. PDF
  9. Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang, “Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion”, The WEB Conference (WWW), 2021. arXiv
  10. Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang, “Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention”, The 28th International Conference on Computational Linguistics (COLING), 2020. PDF, Code
  11. Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo and Jing Jiang, “Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction”, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020. PDF
  12. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang and Chengqi Zhang, “Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together”, Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. PDF, Code
  13. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang and Chengqi Zhang, “Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling”, International Joint Conferences on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018. PDF, Code
  14. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang and Chengqi Zhang, “Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling”, Sixth International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, 2018. PDF, Code
  15. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan and Chengqi Zhang, “DiSAN: Directional self-attention network for rnn/cnn-free language understanding”, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA, 2018. ( Most cited student’s paper, 808 citations ) PDF, Code
• Federated Learning, Decentralized Learning
  1. Zhiwei Li, Guodong Long, Tianyi Zhou, “Federated Recommendation with Additive Personalization”, International Conference on Learning Representations (ICLR), 2024. PDF
  2. Jie Ma, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang, “Structured Federated Learning through Clustered Additive Modeling”, Advances in Neural Information Processing Systems 37 (NeurIPS), 2023. PDF
  3. Shuangtong Li, Tianyi Zhou, Xinmei Tian, and Dacheng Tao. “Structured Cooperative Learning with Graphical Model Priors”, International Conference on Machine Learning (ICML), 2023. PDF
  4. Shuangtong Li, Tianyi Zhou, Xinmei Tian, Dacheng Tao, “Learning to Collaborate in Decentralized Learning of Personalized Models”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. PDF
  5. Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang, “Federated Learning from Pre-Trained Models: A Contrastive Learning Approach”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022.
  6. Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, and Bo Yang, “Dual Personalization on Federated Recommendation”, International Joint Conference on Artificial Intelligence (IJCAI), 2023. PDF
  7. Ravikumar Balakrishnan*, Tian Li*, Tianyi Zhou*, Nageen Himayat, Virginia Smith, Jeff A. Bilmes, “Diverse Client Selection for Federated Learning via Submodular Maximization”, International Conference on Learning Representations (ICLR), 2022. PDF, Code
  8. Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang, “Personalized Federated Learning With Structural Information”, International Joint Conference on Artificial Intelligence (IJCAI), 2022. (long presentation) PDF
  9. Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang, “FedProto: Federated Prototype Learning across Heterogeneous Clients”, The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. PDF, Code
  10. Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, “Multi-Center Federated Learning: clients clustering for better personalization”, World Wide Web Journal (Springer), 2022. PDF
• Data Augmentation and Synthesis
  1. Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha, “Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models”, The 16th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia Pacific (SIGGRAPH Asia), 2023. PDF, CODE
  2. Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes, “Retrospective Adversarial Replay for Continual Learning”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022.
  3. Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao, “Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022.
  4. Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao, “Identity-Disentangled Adversarial Augmentation for Self-supervised Learning”, International Conference on Machine Learning (ICML), 2022. PDF
• Continual Learning, Plasticity-Stability Trade-off
  1. Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. “Does Continual Learning Equally Forget All Parameters?”, International Conference on Machine Learning (ICML), 2023. PDF
  2. Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, and Yuhui Shi. “Continual Task Allocation in Meta-Policy Network via Sparse Prompting”, International Conference on Machine Learning (ICML), 2023. PDF, Code
  3. Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes, “Retrospective Adversarial Replay for Continual Learning”, Advances in Neural Information Processing Systems 36 (NeurIPS), 2022. PDF
• Transfer Learning, Multi-task Learning, Meta-Learning
  1. Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang, “Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023. PDF
  2. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong and Chengqi Zhang, “Isometric Propagation Network for Generalized Zero-shot Learning”, International Conference on Learning Representations (ICLR), 2021. PDF
  3. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Attribute Propagation Network for Graph Zero-shot Learning”, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York, USA, 2020. PDF, Code, Poster
  4. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Learning to Propagate for Graph Meta-Learning”, Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, 2019. PDF, Appendix, Code, Poster, Slides
  5. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao and Chengqi Zhang, “Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph”, International Joint Conferences on Artificial Intelligence (IJCAI), Macau, China, 2019. PDF, Code
  6. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang, “Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy”, IEEE Transactions on Knowledge and Data Engineering (T-KDE), 2020. PDF, Code
  7. Tianyi Zhou and Dacheng Tao, “Multi-task Copula by Sparse Graph Regression”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), NYC, USA, 2014. PDF
• Submodular Optimization
  1. Shengjie Wang*, Tianyi Zhou*, Chandrashekhar Lavania, Jeff A. Bilmes, “Constrained Robust Submodular Partitioning”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. ( Spotlight ) PDF, Appendix
  2. Tianyi Zhou, Hua Ouyang, Jeff A. Bilmes, Yi Chang and Carlos Guestrin, “Scaling Submodular Maximization via Pruned Submodularity Graphs”, Twentyth International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, 2017. PDF, Appendix
  3. Tianyi Zhou and Jeff A. Bilmes, “Stream Clipper: Scalable Submodular Maximization on Stream”, arXiv: 1606.00389, 2016. PDF
• Disentanglement, Matrix Factorization
  1. Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, Dacheng Tao, “Class-Disentanglement and Applications in Adversarial Detection and Defense”, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021. PDF, Appendix
  2. Tianyi Zhou, Wei Bian, and Dacheng Tao, “Divide-and-Conquer Anchoring for Near Separable Nonnegative Matrix Factorization and Completion in High Dimensions”, IEEE International Conference on Data Mining (ICDM), pp., Dallas, TX, USA, 2013. ( Best Student Paper Award ) PDF, Slides
  3. Tianyi Zhou and Dacheng Tao, “Shifted Subspaces Tracking on Sparse Outlier for Motion Segmentation”, International Joint Conferences on Artificial Intelligence (IJCAI), Beijing, China, 2013. PDF
  4. Tianyi Zhou and Dacheng Tao, “Greedy Bilateral Sketch, Completion & Smoothing”, International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research - Proceedings Track, Scottsdale, Arizona, USA, 2013. PDF
  5. Tianyi Zhou and Dacheng Tao, “Multi-label Subspace Ensemble”, International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research - Proceedings Track 22: 1444-1452, La Palma, Canary Islands, Spain, 2012. PDF
  6. Tianyi Zhou and Dacheng Tao, “Bilateral Random Projections”, IEEE International Symposium on Information Theory (ISIT), MIT, Boston, USA, 2012. PDF
  7. Tianyi Zhou and Dacheng Tao, “GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case”, Twenty-Eighth International Conference on Machine Learning (ICML), Bellevue, WA, USA, 2011. ( IEEE TCSC Most Influential Paper Award, Most cited first-author paper, 808 citations ) PDF, Code, Demo Videos, Talk
• Compressed Sensing, Sparse Dimension Reduction
  1. Tianyi Zhou and Dacheng Tao, “k-bit Hamming Compressed Sensing”, IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, 2013. PDF
  2. Tianyi Zhou and Dacheng Tao, “1-bit Hamming Compressed Sensing”, IEEE International Symposium on Information Theory (ISIT), MIT, Boston, USA, 2012. PDF
  3. Tianyi Zhou and Dacheng Tao, “Double Shrinking for Sparse Learning”, IEEE Transactions on Image Processing (T-IP), 22(1): 244-257, 2013. PDF
  4. Tianyi Zhou, Dacheng Tao and Xindong Wu,”Compressed Labeling on Distilled Labelsets for Multi-label Learning”, Machine Learning (Springer) (MLJ) 88(1-2): 69-126, 2012. PDF
  5. Tianyi Zhou, Dacheng Tao and Xindong Wu, “Manifold elastic net: a unified framework for sparse dimension reduction”, Data Mining and Knowledge Discovery (Springer) (DMKD) 22(3): 340-371, 2011. PDF

Teaching

Teaching Assistant of CSEP 546 Winter 2014: Data Mining/Machine Learning

Teaching Assistant of ECE 596A Spring 2019: Advanced introduction to Neural Networks

Teaching Assistant of ECE 596A Winter 2019: Advanced introduction to machine learning

instructor of CMSC 421 Fall 2022: Introduction to Artificial Intelligence

instructor of CMSC 828A Spring 2023: Fantastic Machine Learning Paradigms and Where to Use Them

instructor of CMSC 421 Fall 2023: Introduction to Artificial Intelligence

Service

• Meta-Reviewer for
  • ICPR 2024
  • IJCAI 2023
  • WACV 2023
  • WACV 2022
  • SIGKDD 2022
  • AAAI 2021
• Reviewer for
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
  • Journal of Machine Learning Research (JMLR)
  • Machine Learning (Springer)
  • Neural Computation
  • IEEE Transactions on Knowledge and Data Engineering (T-KDE)
  • IEEE Transactions on Neural Networks and Learning Systems (T-NNLS)
  • IEEE Transactions on Systems, Man, and Cybernetics (T-SMC)
  • IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT)
  • IEEE Communications Letters, Pattern Recognition (Elsevier)
  • Computational Statistics and Data Analysis (CSDA) (Elsevier)
  • Information Sciences (INS) (Elsevier)
  • Neurocomputing (Elsevier)
  • Signal Processing (Elsevier)
  • Knowledge and Information Systems (KAIS)(Springer)
  • Pattern Analysis and Applications (PAAA) (Springer)
  • Neural Processing Letters (NEPL) (Springer)
• Reviewer/Program Committee Member for
  • Annual Conference on Neural Information Processing System (NeurIPS)
  • International Conference on Machine Learning (ICML)
  • International Conference on Learning Representations (ICLR)
  • The Conference on Uncertainty in Artificial Intelligence (UAI)
  • International Conference on Artificial Intelligence and Statistics (AISTATS)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • International Joint Conference on Artificial Intelligence (IJCAI)
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)
  • SIAM International Conference on Data Mining (SDM)
  • IEEE International Conference on Data Mining (ICDM)
  • IEEE International Conference on Multimedia and Expo (ICME)
  • IEEE Pacific-Rim Conference on Multimedia (PCM)
  • ACM Conference on Information and Knowledge Management (CIKM)
  • IEEE International Conference on Computer Vision and Pattern Recognition (CVPR),
  • IEEE International Conference on Computer Vision (ICCV)
  • International Conference on Pattern Recognition (ICPR)
  • ACM Multimedia (MM)

Awards

• 2020: IEEE Computer Society Technical Committee on Scalable Computing (TCSC) Most Influential Paper Award for: Tianyi Zhou and Dacheng Tao, “GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case”, Twenty-Eighth International Conference on Machine Learning (ICML), Bellevue, WA, USA, 2011.
• 2013: International Conference on Data Mining (ICDM) Best Student Paper Award for: Tianyi Zhou, Wei Bian, and Dacheng Tao, “Divide-and-Conquer Anchoring for Near Separable Nonnegative Matrix Factorization and Completion in High Dimensions”, IEEE International Conference on Data Mining (ICDM), Dallas, TX, USA, 2013.
• 2007: The Meritorious winner of Mathematical Contest in Modeling (MCM) of COMAP (Top 10% of teams).