Professor Liang, Yingyu

PhD Georgia Tech
Associate Professor of Department of Computer Science and HKU Musketeers Foundation Institute of Data Science
Institute of Data Science Scholar

Tel: (+852) 3910 2332
Email: yingyul@hku.hk

Dr Yingyu Liang co-hosted at the Department of Computer Science is an Associate Professor in the Musketeers Foundation Institute of Data Science at The University of Hong Kong. Before becoming a faculty member at HKU, he held the position of Associate Professor at the Department of Computer Sciences in the University of Wisconsin-Madison. Before that, he was a postdoc at Princeton University. He received his Ph.D. in 2014 from Georgia Tech, and M.S. (2010) and B.S. (2008) from Tsinghua University. He is a recipient of the NSF CAREER award.

His research group aims at providing theoretical foundations for modern machine learning models and designing efficient algorithms for real world applications. Recent focuses include optimization and generalization in deep learning, robust machine learning, and their applications.

Research Interests

Machine learning; Optimization and generalization in deep learning; Robust machine learning, and their applications.

Selected Publications

• Zhenmei Shi, Jenny Wei, Yingyu Liang. “Provable Guarantees for Neural Networks via Gradient Feature Learning.” Neural Information Processing Systems (NeurIPS), 2023.
• Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha. “Stratified Adversarial Robustness with Rejection.” International Conference on Machine Learning (ICML), 2023.
• Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha. “The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning.” International Conference on Learning Representations (ICLR), 2023.
• Zhenmei Shi, Jenny Wei, Yingyu Liang. “A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features.” International Conference on Learning Representations (ICLR), 2022.
• Siddhant Garg, Yingyu Liang. “Functional Regularization for Representation Learning: A Unified Theoretical Perspective.” Neural Information Processing Systems (NeurIPS), 2020.
• Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang. “Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers.” Neural Information Processing Systems (NeurIPS), 2019.
• Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang. “N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules.” Neural Information Processing Systems (NeurIPS), 2019.
• Yuanzhi Li, Yingyu Liang. “Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data.” Neural Information Processing Systems (NeurIPS), 2018.
• Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, Andrej Risteski. “Linear Algebraic Structure of Word Senses, with Applications to Polysemy.” Transactions of the Association for Computational Linguistics (TACL), 2018.
• Sanjeev Arora, Yingyu Liang, Tengyu Ma. “A Simple but Tough-to-Beat Baseline for Sentence Embedding.” International Conference on Learning Representations (ICLR), 2017.

Professor Liu, Qi

MS National University of Singapore; PhD Oxford
BASc(FinTech) Programme Director; Assistant Professor

Fax: (+852) 2559 8447
Email: liuqi@cs.hku.hk
Homepage: http://leuchine.github.io/

Qi Liu is an assistant professor at the Department of Computer Science, the University of Hong Kong. His research interests include natural language processing, machine learning, and FinTech. He has been serving on the program committees for leading machine learning conferences and journals like NeurIPS, ICML, ICLR, ACL, EMNLP, and NAACL.

Research Interests

Natural Language Processing, Machine Learning, FinTech

Selected Publications

• Qi Liu, Dani Yogatama, Phil Blunsom. Relational Memory Augmented Language Models. In Transactions of the Association for Computational Linguistics 2022 (TACL).
• Jean Kaddour, Yuchen Zhu, Qi Liu, Matthew J. Kusner, Ricardo Silva. Causal Effect Inference for Structured Treatments. In Conference on Neural Information Processing Systems 2021 (NeurIPS).
• Qi Liu, Lei Yu, Laura Rimell, Phil Blunsom. Pretraining the Noisy Channel Model for Task-Oriented Dialogue. In Transactions of the Association for Computational Linguistics 2021 (TACL).
• Qi Liu, Matt J. Kusner, Phil Blunsom. Counterfactual Data Augmentation for Neural Machine Translation. In Annual Conference of the North American Chapter of the Association for Computational Linguistics 2021 (NAACL).
• Qi Liu, Maximilian Nickel, Douwe Kiela. Hyperbolic Graph Neural Networks. In Conference on Neural Information Processing Systems 2019 (NeurIPS).
• Shuai Zhang, Yi Tay, Lina Yao, Qi Liu. Quaternion Knowledge Graph Embedding. In Conference on Neural Information Processing Systems 2019 (NeurIPS).
• Jiatao Gu, Qi Liu, Kyunghyun Cho. Insertion-based Decoding with Automatically Inferred Generation Order. In Transactions of the Association for Computational Linguistics 2019 (TACL).
• Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt. Constrained Graph Variational Autoencoders for Molecule Design. In Conference on Neural Information Processing Systems 2018 (NeurIPS).
• Yue Zhang, Qi Liu, Linfeng Song. Sentence-State LSTM for Text Representation. In Annual Meeting of the Association for Computational Linguistics 2018 (ACL).
• Qi Liu, Yue Zhang, Jiangming Liu. Learning Domain-specific Representations for Multi-Domain Sentiment Classification. In Annual Conference of the North American Chapter of the Association for Computational Linguistics 2018 (NAACL).

Professor Zhao, Qi

PhD Tsinghua University
Assistant Professor

Tel: (+852) 2857 8448
Fax: (+852) 2559 8447
Email: zhaoqi@cs.hku.hk
Homepage: https://qizhao-quantum.github.io/

Research Interests

Quantum simulations, quantum computing, quantum resource theories, and self-testing quantum information

Selected Publications

• Q. Zhao, Y. Zhou, A. F. Shaw, T. Li, and A. M. Childs, Hamiltonian simulation with random inputs, arXiv:2111.04773 (2021), QIP 2022 contributed talk
• D. Wu, Q. Zhao*, X.-M. Gu, H.-S. Zhong, Y. Zhou, L.-C. Peng, J. Qin, Y.-H. Luo, K. Chen, L. Li, N.-L. Liu, C.-Y. Lu, and J.-W. Pan, Robust self-testing of multiparticle entanglement, Phys. Rev. Lett., vol. 127, p. 230503, Dec. 2021
• Q. Zhao and X. Yuan, Exploiting anticommutation in Hamiltonian simulation, Quantum, vol. 5, p.534, Aug. 2021
• X. Yuan, J. Sun, J. Liu, Q. Zhao, and Y. Zhou, Quantum simulation with hybrid tensor networks, Phys. Rev. Lett., vol. 127, p. 040501, Jul 2021.
• Z.-D. Li, Q. Zhao*, R. Zhang, L.-Z. Liu, X.-F. Yin, X. Zhang, Y.-Y. Fei, K. Chen, N.-L. Liu, F. Xu, Y.-A. Chen, L. Li, and J.-W. Pan, Measurement-device-independent entanglement witness of tripartite entangled states and its applications, Phys. Rev. Lett., vol. 124, p. 160503, Apr 2020.
• Q. Zhao, Y. Liu, X. Yuan, E. Chitambar, and A.Winter, One-shot coherence distillation: Towards completing the picture, IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6441–6453, 2019.
• Q. Zhao, G. Wang, X. Yuan, and X. Ma, Efficient and robust detection of multipartite greenberger-horne-zeilinger-like states, Phys. Rev. A, vol. 99, p. 052349, May 2019.
• Y. Zhou, Q. Zhao, X. Yuan, and X. Ma, Detecting multipartite entanglement structure with minimal resources, npj Quantum Information, vol. 5, p. 83, 2019.
• Y. Liu, Q. Zhao, M.-H. Li, J.-Y. Guan, Y. Zhang, B. Bai, W. Zhang, W.-Z. Liu, C. Wu, X. Yuan, H. Li, W. J. Munro, Z. Wang, L. You, J. Zhang, X. Ma, J. Fan, Q. Zhang, and J.-W. Pan, Device-independent quantum random-number generation, Nature, vol. 562, no. 7728, p. 548, 2018.
• H. Lu, Q. Zhao*, Z-D. Li, X.-F. Yin, X. Yuan, J.-C. Hung, L.-K. Chen, L. Li, N.-L. Liu, C.-Z. Peng, Y.-C. Liang, X. Ma, Y.-A. Chen, and J.-W. Pan, Entanglement structure: Entanglement partitioning in multipartite systems and its experimental detection using optimizable witnesses, Phys. Rev. X, vol. 8, p. 021072, Jun 2018.
• Q. Zhao, Y. Liu, X. Yuan, E. Chitambar, and X. Ma, One-shot coherence dilution, Phys. Rev. Lett., vol. 120, p. 070403, Feb 2018.
• H.-L. Huang, Q. Zhao, X. Ma, C. Liu, Z.-E. Su, X.-L.Wang, L. Li, N.-L. Liu, B. C. Sanders, C.-Y. Lu, and J.-W. Pan, Experimental blind quantum computing for a classical client, Phys. Rev. Lett., vol. 119, p.050503, Aug 2017.
• Q. Zhao, X. Yuan, and X. Ma, Efficient measurement-device-independent detection of multipartite entanglement structure, Phys. Rev. A, vol. 94, p. 012343, Jul 2016.

Dr. Qin, Shengzhi

PhD HKU
Lecturer
 

 

Dr. Shengzhi Qin (Brian) received his Ph.D. in Cyber Security from The University of Hong Kong (HKU) in 2024, supervised by Dr. K.P. Chow, and his Bachelor of Engineering in Information Security from the University of Electronic Science and Technology of China (UESTC) in 2018. He is currently a Lecturer in the School of Computing and Data Science at HKU (Shanghai).

Dr. Qin’s research focuses on digital forensics and cybersecurity. His work includes anomaly detection in cyber-physical systems using transformer-based models, imbalanced malware detection, vulnerability knowledge graph reasoning, blockchain forensics, and cryptocurrency tracing. He serves as the organizing committee member for the International Digital Forensics Challenge (IDFC) and a full member of the Information Security and Forensics Society (ISFS).

Research Interests

Digital Forensics and Investigation, Internet of Things (IoT) Security, AI-Driven Security Data Analytics,  Blockchain Forensics

Selected Publications

• Qin, Shengzhi, and Kam-Pui Chow. “Improving Android Malware Detection in Imbalanced Data Scenarios.” IFIP International Conference on Digital Forensics, 2024.
• Shengzhi Qin, Yubo Lang, and K.P. Chow. “Traceable Transformer Based Anomaly Detection on Water Treatment System.” IFIP International Conference on Digital Forensics, 2023.
• Qin, Shengzhi, Qiaokun Wen, and Kam-Pui Chow. “Predicting the Locations of Unrest Using Social Media.” IFIP International Conference on Digital Forensics, 2021.
• Qin, Shengzhi, and K. P. Chow. “Automatic Analysis and Reasoning Based on Vulnerability Knowledge Graph.” Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. Springer, 2019.