Mr Zhen Zhang
Grant-Funded Researcher (C)
School of Computer Science and Information Technology
College of Engineering and Information Technology
Zhen Zhang is currently a Postdoctoral Researcher at the University of Adelaide, working with Prof. Qinfeng (Javen) Shi. Previously, he was a postdoctoral research fellow in the Department of Computer Science at the National University of Singapore under the supervision of Prof. Lee Wee Sun. He received a PhD in Computer Science from Northwestern Polytechnical University (Xi’an, China) under the supervision of Prof. Yanning Zhang. During Nov. 2012 to Dec. 2014, he was a visiting student at Australian Centre for Visual Technologies, University of Adelaide, under the supervision of Prof. Anton van den Hengel and Prof. Qinfeng (Javen) Shi. He completed his bachelor degree in Computer Science in 2010 at School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China, working with Prof. Yanning Zhang and Dr. Zhonghua Fu.
Zhen Zhang is currently a Postdoctoral Researcher at the University of Adelaide, working with Prof. Qinfeng (Javen) Shi, on causality, reinforcement learning and computer vision. Previously, he was a postdoctoral research fellow in the Department of Computer Science at the National University of Singapore under the supervision of Prof. Lee Wee Sun. He received a PhD in Computer Science from Northwestern Polytechnical University (Xi’an, China) under the supervision of Prof. Yanning Zhang. During Nov. 2012 to Dec. 2014, he was a visiting student at Australian Centre for Visual Technologies, University of Adelaide, under the supervision of Prof. Anton van den Hengel and Prof. Qinfeng (Javen) Shi. He completed his bachelor degree in Computer Science in 2010 at School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China, working with Prof. Yanning Zhang and Dr. Zhonghua Fu.
| Language | Competency |
|---|---|
| Chinese (Mandarin) | Can read, write, speak, understand spoken and peer review |
| English | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2010 - 2017 | Northwestern Polytechnical University | China | Ph.D |
| Year | Citation |
|---|---|
| 2025 | Ghiasi, A., Zhang, Z., Zeng, Z., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2025). Generalization of anomaly detection in bridge structures using a vibration-based Siamese convolutional neural network. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 40(18), 18 pages. Scopus4 WoS4 |
| 2025 | Jin, S., Li, X., Yang, G., Zhang, Z., Shi, J. Q., Liu, Y., & Zhao, C. -X. (2025). Active Learning-Based Prediction of Drug Combination Efficacy. ACS Nano, 19(18), 17929-17940. Scopus1 WoS1 |
| 2025 | Zhang, X., Wei, W., Zhang, Z., & Zhang, L. (2025). Leapfrog Polymorphic Neural Ordinary Differential Equation. IEEE Signal Processing Letters, 32, 1-5. Scopus1 WoS1 |
| 2025 | Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2025). Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. Transactions on Machine Learning Research, 2025-April. Scopus1 |
| 2024 | Liu, M., Zhang, Z., Ma, N., Gu, M., Wang, H., Zhou, S., & Bu, J. (2024). Structure enhanced prototypical alignment for unsupervised cross-domain node classification. Neural Networks, 177, 106396. Scopus7 |
| 2024 | Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A. V. D., & Shi, J. Q. (2024). InvariantStock: Learning Invariant Features for Mastering the Shifting Market. Transactions on Machine Learning Research, 2024. |
| 2024 | Yin, Z., Zhang, Z., Gong, D., Albrecht, S. V., & Shi, J. Q. (2024). Highway Graph to Accelerate Reinforcement Learning. Transactions on Machine Learning Research, 2024. |
| 2024 | Luo, B., Zhang, Z., Wang, Q., Ke, A., Lu, S., & He, B. (2024). AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective. ACM Computing Surveys, 57(4), 1-38. Scopus10 |
| 2024 | Tan, Z., Li, X., Zhao, Y., Zhang, Z., Shi, J. Q., & Li, H. (2024). Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction. ChemCatChem, 16(22), e202400470-1-e202400470-8. Scopus9 WoS9 |
| 2024 | Chen, L., Zhang, Y., Song, Y., Zhang, Z., & Liu, L. (2024). A Causal Inspired Early-Branching Structure for Domain Generalization. International Journal of Computer Vision, 132(9), 4052-4072. Scopus6 WoS5 |
| 2024 | Zhang, X., Zhang, Z., Chinnici, A., Sun, Z., Shi, J. Q., Nathan, G. J., & Chin, R. C. (2024). Physics-informed data-driven unsteady Reynolds-averaged Navier-Stokes turbulence modeling for particle-laden jet flows. Physics of Fluids, 36(5), 23 pages. Scopus1 WoS1 |
| 2024 | Li, H., Li, X., Wang, P., Zhang, Z., Davey, K., Shi, J. Q., & Qiao, S. -Z. (2024). Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism. Journal of the American Chemical Society, 146(32), 22850-22858. Scopus40 WoS43 Europe PMC10 |
| 2023 | Zhao, Y., Li, H., Shan, J., Zhang, Z., Li, X., Shi, J. Q., . . . Li, H. (2023). Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis. Journal of Physical Chemistry Letters, 14(49), 11058-11062. Scopus7 WoS7 Europe PMC1 |
| 2023 | Zhang, Z., Dupty, M. H., Wu, F., Shi, J. Q., & Lee, W. S. (2023). Factor Graph Neural Networks. Journal of Machine Learning Research, 24. Scopus5 |
| 2023 | Zhang, Z., Liu, M., Li, Z., & Bu, J. (2023). Self-supervised end-to-end graph level anomaly detection. Scientia Sinica Informationis, 53(11), 2202-2213. Scopus2 |
| 2023 | Yan, Q., Gong, D., Wang, P., Zhang, Z., Zhang, Y., & Shi, J. Q. (2023). SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring. IEEE Transactions on Image Processing, 32, 2857-2866. Scopus30 WoS23 Europe PMC3 |
| 2023 | Meng, J., Wang, Z., Ying, K., Zhang, J., Guo, D., Zhang, Z., . . . Chen, S. (2023). Human Interaction Understanding with Consistency-Aware Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 11898-11914. Scopus6 WoS4 Europe PMC2 |
| 2023 | Li, X., Li, H., Zhang, Z., Shi, J. Q., Jiao, Y., & Qiao, S. -Z. (2023). Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction. Nano Energy, 115, 108695-1-108695-9. Scopus10 WoS9 |
| 2023 | Zhang, X., Wei, W., Zhang, Z., Zhang, L., & Li, W. (2023). Milstein-driven neural stochastic differential equation model with uncertainty estimates. Pattern Recognition Letters, 174, 71-77. Scopus5 WoS4 |
| 2022 | Zhou, S., Wang, X., ster, M., Li, B., Ye, C., Zhang, Z., . . . Bu, J. (2022). Direction-Aware User Recommendation Based on Asymmetric Network Embedding. ACM Transactions on Information Systems, 40(2), 1-23. Scopus8 |
| 2022 | Li, Z., Liu, Y., Zhang, Z., Pan, S., Gao, J., & Bu, J. (2022). Cyclic label propagation for graph semi-supervised learning. World Wide Web, 25(2), 703-721. Scopus6 |
| 2021 | Hu, Y., Zhang, Z., Yao, Y., Huyan, X., Zhou, X., & Lee, W. S. (2021). A bidirectional graph neural network for traveling salesman problems on arbitrary symmetric graphs. Engineering Applications of Artificial Intelligence, 97, 104061-1-104061-9. Scopus39 WoS32 |
| 2020 | Li, Z., Wang, H., Ding, D., Hu, S., Zhang, Z., Liu, W., . . . Zhang, J. (2020). Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation. Complexity, 2020, 1-13. Scopus3 |
| 2020 | Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning deep gradient descent optimization for image deconvolution. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5468-5482. Scopus97 WoS80 Europe PMC13 |
| 2019 | Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D. C., & Qinfeng Shi, J. (2019). An adaptive markov random field for structured compressive sensing. IEEE Transactions on Image Processing, 28(3), 1556-1570. Scopus9 WoS8 |
| 2018 | Wang, Z., Jin, J., Liu, T., Liu, S., Zhang, J., Chen, S., . . . Shao, Z. (2018). Understanding human activities in videos: A joint action and interaction learning approach. Neurocomputing, 321, 216-226. Scopus13 |
| 2017 | Yao, R., Xia, S., Zhang, Z., & Zhang, Y. (2017). Real-Time Correlation Filter Tracking by Efficient Dense Belief Propagation with Structure Preserving. IEEE Transactions on Multimedia, 19(4), 772-784. Scopus41 WoS36 |
| 2014 | Zhang, Z., & Zhang, Y. (2014). Variable kernel density estimation based robust regression and its applications. Neurocomputing, 134, 30-37. Scopus9 WoS8 |
| 2013 | Zhang, Z., Shi, Q., Zhang, Y., Shen, C., & Hengel, A. V. D. (2013). Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations. |
| 2011 | Sun, J. Q., Zhou, J., Zhang, Z., & Zhang, Y. P. (2011). Centroid location for space targets based on energy accumulation. Guangxue Jingmi Gongcheng Optics and Precision Engineering, 19(12), 3043-3048. Scopus21 |
| 2011 | Sun, J. Q., Zhou, J., Zhu, Y., & Zhang, Z. (2011). Smear removal of CCD camera in astronomic observation. Guangxue Jingmi Gongcheng Optics and Precision Engineering, 19(10), 2526-2532. Scopus13 |
| Year | Citation |
|---|---|
| 2025 | Wang, Z., Zhou, S., Chen, J., Zhang, Z., Hu, B., Feng, Y., . . . Wang, C. (2025). Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding. In WSDM 2025 - Proceedings of the 18th ACM International Conference on Web Search and Data Mining (pp. 60-69). Hannover, Germany: ACM. DOI Scopus4 |
| 2024 | Liu, M., Zhang, Z., Tang, J., Bu, J., He, B., & Zhou, S. (2024). Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation. In Advances in Neural Information Processing Systems Vol. 37 (pp. 29 pages). Vancouver, Canada: Neural information processing systems foundation. Scopus5 |
| 2024 | Luo, B., Zhang, Z., Wang, Q., & He, B. (2024). Multi-Chain Graphs of Graphs: A New Approach to Analyzing Blockchain Datasets. In Advances in Neural Information Processing Systems Vol. 37. Vancouver, Canada: Neural information processing systems foundation. Scopus4 |
| 2024 | Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2024). IDENTIFIABLE LATENT POLYNOMIAL CAUSAL MODELS THROUGH THE LENS OF CHANGE. In 12th International Conference on Learning Representations, ICLR 2024. Online: ICLR. Scopus9 |
| 2024 | Cai, Y., Liu, Y., Zhang, Z., & Shi, J. Q. (2024). CLAP: Isolating Content from Style Through Contrastive Learning with Augmented Prompts. In Lecture Notes in computer science Vol. 15079 (pp. 130-147). Milan, Italy: Springer Nature Switzerland. DOI Scopus3 |
| 2024 | Zhang, Z., & He, B. (2024). Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation. In WWW 2025 - Proceedings of the ACM Web Conference (pp. 4420-4431). Sydney, Australia: ACM. DOI Scopus2 |
| 2024 | Jiang, J., Zhang, Z., Luo, B., He, B., Chen, M., Wang, W., & Chen, J. (2024). Spade: A Real-Time Fraud Detection Framework. In Proceedings of the VLDB Endowment Vol. 17 (pp. 4253-4256). Guangzhou: Association for Computing Machinery (ACM). DOI |
| 2024 | Wang, Q., Zhang, Z., Liu, Z., Lu, S., Luo, B., & He, B. (2024). EX-GRAPH: A PIONEERING DATASET BRIDGING ETHEREUM AND X. In 12th International Conference on Learning Representations Iclr 2024. Scopus6 |
| 2024 | Zhang, Z., Liu, M., Wang, A., Chen, H., Li, Z., Bu, J., & He, B. (2024). Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation. In WWW 2024 - Proceedings of the ACM Web Conference (pp. 664-675). Singapore: ACM. DOI Scopus13 |
| 2024 | Liu, M., Fang, Z., Zhang, Z., Gu, M., Zhou, S., Wang, X., & Bu, J. (2024). Rethinking Propagation for Unsupervised Graph Domain Adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 13963-13971). Vancouver, Canada: Association for the Advancement of Artificial Intelligence (AAAI). DOI Scopus28 |
| 2023 | Zhang, Z., Luo, B., Lu, S., & He, B. (2023). Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT. In Advances in Neural Information Processing Systems Vol. 36 (pp. 25 pages). New Orleans: Neural information processing systems foundation. Scopus9 |
| 2022 | Yan, Q., Zhang, S., Chen, W., Liu, Y., Zhang, Z., Zhang, Y., . . . Gong, D. (2022). A Lightweight Network for High Dynamic Range Imaging. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 823-831). Online: IEEE. DOI Scopus12 WoS9 |
| 2022 | Perez-Pellitero, E., Catley-Chandar, S., Shaw, R., Leonardis, A., Timofte, R., Zhang, Z., . . . Park, C. Y. (2022). NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 1008-1022). Online: IEEE. DOI Scopus32 WoS15 |
| 2022 | Zhang, Z., Ng, I., Gong, D., Liu, Y., Abbasnejad, E. M., Gong, M., . . . Shi, J. Q. (2022). Truncated Matrix Power Iteration for Differentiable DAG Learning. In Advances in Neural Information Processing Systems Vol. 35 (pp. 13 pages). Online: Neural information processing systems foundation. Scopus18 |
| 2021 | Gu, C., Bu, J., Zhang, Z., Yu, Z., Ma, D., & Wang, W. (2021). Image Search with Text Feedback by Deep Hierarchical Attention Mutual Information Maximization. In MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (pp. 4600-4609). New York, USA: ACM. DOI Scopus31 |
| 2021 | Gong, D., Zhang, Z., Shi, J. Q., & van den Hengel, A. (2021). Memory-augmented Dynamic Neural Relational Inference. In Proceedings 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021 (pp. 11823-11832). Los Alamitos, CA, USA: IEEE. DOI Scopus12 WoS9 |
| 2020 | Zhang, Z., Wu, F., & Lee, W. S. (2020). Factor graph neural network. In Thirty-fourth Conference on Neural Information Processing Systems, NeurIPS 2020 Vol. 2020-December (pp. 1-11). online: NIPS. Scopus33 WoS1 |
| 2020 | Dupty, M. H., Zhang, Z., & Lee, W. S. (2020). Visual relationship detection with low rank non-negative tensor decomposition. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence Vol. 34 (pp. 10737-10744). online: AAAI. Scopus7 WoS5 |
| 2020 | Su, X., Lee, W. S., & Zhang, Z. (2020). Multiplicative Gaussian Particle Filter. In S. Chiappa, & R. Calandra (Eds.), 23rd International Conference on Artificial Intelligence and Statistics (AISTAT) VOL 108 Vol. 108 (pp. 1-9). online: ADDISON-WESLEY PUBL CO. Scopus1 |
| 2019 | Zhang, Z., & Lee, W. S. (2019). Deep graphical feature learning for the feature matching problem. In Proceedings: 2019 International Conference on Computer Vision Vol. 2019-October (pp. 5086-5095). Los Alamitos, California: IEEE. DOI Scopus62 WoS60 |
| 2018 | Li, C., Zhang, Z., Lee, W. S., & Lee, G. H. (2018). Convolutional Sequence to Sequence Model for Human Dynamics. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 5226-5234). online: IEEE. DOI Scopus329 WoS273 |
| 2017 | Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., Yao, R., & Van Den Hengel, A. (2017). Solving constrained combinatorial optimization problems via MAP inference without high-order penalties. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3804-3810). San Francisco: AAAI. Scopus1 WoS1 |
| 2017 | Zhang, Z., McAuley, J., Li, Y., Wei, W., Zhang, Y., & Shi, Q. (2017). Dynamic programming bipartite belief propagation for hyper graph matching. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) Vol. 0 (pp. 4662-4668). online: AAAI Press. DOI Scopus7 WoS5 |
| 2016 | Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., & Van Den Hengel, A. (2016). Pairwise matching through max-weight bipartite belief propagation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) Vol. 2016 (pp. 1202-1210). Las Vegas, NV: IEEE. DOI Scopus49 WoS30 |
| 2016 | Rezatofighi, S., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) Vol. 2016-December (pp. 136-145). Las Vegas, NV: IEEE. DOI Scopus25 WoS20 |
| 2016 | Zhang, Z., Zhang, Y., Sun, J., & Sun, L. (2016). Image matching via higher order structures. In Proceedings of 2015 International Conference on Orange Technologies, ICOT 2015 (pp. 105-108). online: IEEE. DOI |
| 2015 | Tan, M., Shi, Q., Van Den Hengel, A., Shen, C., Gao, J., Hu, F., & Zhang, Z. (2015). Learning graph structure for multi-label image classification via clique generation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4100-4109). Boston, MA: IEEE. DOI Scopus44 WoS35 |
| 2015 | Rezatofighi, S., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2015). Joint Probabilistic Data Association Revisited. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 3047-3055). Santiago, CHILE: IEEE. DOI Scopus307 WoS232 |
| 2015 | Wang, Z., Zhang, Z., & Geng, N. (2015). A message passing algorithm for MRF inference with unknown graphs and its applications. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 9006 (pp. 288-302). Springer International Publishing. DOI Scopus1 |
| 2012 | Zhang, Z., Zhang, Y., Yao, R., Li, H., & Zhu, Y. (2012). Generalized kernel density estimation based robust estimator and its application. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7202 LNCS (pp. 112-121). Springer Berlin Heidelberg. DOI |
| 2012 | Yao, R., Duan, F., Zhang, Z., & Zhang, Y. (2012). Faint moving object detection in optical astronomical image using a 2D-1D IUWT. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 7202 LNCS (pp. 442-449). Springer Berlin Heidelberg. DOI |
| 2011 | Gao, J., Zhang, Z., Yao, R., Sun, J., & Zhang, Y. (2011). Smear radiometric correction algorithm in star images based on kernel density estimation. In Proceedings of SPIE the International Society for Optical Engineering Vol. 8194 (pp. 819421). SPIE. DOI Scopus1 |
| 2011 | Gao, J., Zhang, Z., Yao, R., Sun, J., & Zhang, Y. (2011). A robust smear removal method for inter-frame Charge-Coupled Device star images. In Proceedings 2011 7th International Conference on Natural Computation Icnc 2011 Vol. 3 (pp. 1805-1808). IEEE. DOI Scopus7 |
| Year | Citation |
|---|---|
| 2022 | Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., Hengel, A. V. D., . . . Shi, J. Q. (2022). Identifying Weight-Variant Latent Causal Models. |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2025 | Co-Supervisor | Improving Machine Learning Models on the Out-Of-Distribution Generalization | Doctor of Philosophy | Doctorate | Full Time | Miss Seyedeh Mahdieh Mirmahdi |
| 2025 | Co-Supervisor | Causal Reinforcement Learning for Interpretable Chain-of-Thought Reasoning in Large Language Models | Doctor of Philosophy | Doctorate | Full Time | Mr Jiayu Huang |
| 2025 | Co-Supervisor | Personalized Cancer Detection and Treatment via Casual Reinforcement Learning with Hyperspectral Imaging | Doctor of Philosophy | Doctorate | Full Time | Mr Meisam Mahmoodi |
| 2025 | Principal Supervisor | Large Language Model Application in Biology and Health | Doctor of Philosophy | Doctorate | Full Time | Mr Yijia Song |
| 2024 | Co-Supervisor | Causal discovery and Out-of-Distribution generalization: sampling from posterior over causal graphs | Doctor of Philosophy | Doctorate | Full Time | Mr Nadhir Hassen |
| 2023 | Co-Supervisor | Causal Discovery on Videos for Scene Graph Generation | Doctor of Philosophy | Doctorate | Full Time | Mr Hamed Damirchi |
| 2023 | Co-Supervisor | Leveraging Causality for Robust Multi-Source Domain Adaptation | Doctor of Philosophy | Doctorate | Full Time | Miss Tianjiao Jiang |
| 2023 | Co-Supervisor | Domain Adaptation via Causal Representation Learning | Doctor of Philosophy | Doctorate | Full Time | Mr Yichao Cai |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2024 - 2025 | Co-Supervisor | Efficient Deep Neural Network Pruning: From Convolutional Networks to Large Language Models | Doctor of Philosophy | Doctorate | Full Time | Ms Hongrong Cheng |
