Biography

Dr. Di Wu is a Lecturer at the School of Mathematics, Physics, and Computing, the University of Southern Queensland and a Visiting Fellow at School of Computer Science, University of Technology Sydney. Prior to that, he was a Researcher at the Australian Institute for Machine Learning (AIML) & School of Computer Science, University of Adelaide, Adelaide, Australia. Previous to this, he was an Associate Research Fellow, Artificial Intelligence at Deakin Blockchain Innovation Lab, School of Information Technology, Deakin University, Melbourne, Australia, and worked as a Postdoc Fellow at the School of Computer Science, University of Technology Sydney (UTS), Sydney, Australia. He has more than 10 years of experience in research & development and academia. He has substantial industry experience in large project management, software development, and large system maintenance experience while working on various projects at China Telecom (Global 500), Shanghai. His research area focuses on applying AI on edge devices and AI applications. He has published papers in high quality refereed books, conferences, and journals including top-tier conferences such as ICLR. He also serves as a reviewer for many high-quality academic conferences and journals, such as CoRL, PR, TETCI, and so on.

He graduated with a Ph.D. from the School of Computer Science & Australian Artificial Intelligence Institute(AAII), University Technology Sydney, Sydney, Australia under the supervision of Prof. Michael Blumenstein (principal), A.Prof. Nabin Sharma (co-), Prior to joining UTS, he was an HDR (Higher Degree Research) candidate at School of ICT, Griffith University, Gold Coast, Australia. Previous to this, he completed his M.Sc. in Information Technology (Professional) and B.Sc. in Information Technology (Honours) as a top honours student under supervision of Prof. Yang Xiang at Deakin University, Melbourne, Australia in 2012 and 2013 respectively.

He worked as a Chief Investigator on the two major projects in collaboration with industry and academia with secured funds of ~AUD 200,000. The project SharkSpotter and Codebots he worked with awarded multiple awards including The Australian Information Industry Association (AIIA) NSW iAwards 2018 in Research and Development Project of the Year, Artificial Intelligence or Machine Learning Innovation of the Year, and Community Service Markets. Additionally, the SharkSpotter Project is also a winner of National iAwards 2018 in the Artificial Intelligence or Machine Learning Innovation of the Year, Merit Award at Asia Pacific ICT Alliance Awards (APICTA) 2018, and Australian Association for Unmanned Systems (AAUS) industry awards 2020. The Codebots projects received the Start-up of The year Queensland State iAward in 2020.

Research Career

Lecturer, School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia 

  School Rsearche Seminar Coordinator (2023-2024)

Grant-Funded Researcher (A), Australian Institute for Machine Learning (AIML), University of Adelaide, Adelaide, Australia

  Successfully developed the demonstration for CRC SmartSat project with the new algorithm for DNN compression and updating from the ground base to the satellite.

Associate Research Fellow, Artificial Intelligence, School of Information Technology, Deakin University, Melbourne, Australia

  Successfully Developed the demonstration industry project for applying the AI recommendation and blockchain into the intelligent marketing system.

Postdoctoral Fellow, School of Computing Science, University of Technology Sydney, Sydney, Australia

  Successfully Developed the first SharkSpotter System with a great team

  Successfully Developed the prototype of transferring the handwriting database structure into codes via simple photos.

Industry Career

Founder & CEO, Novitam Pty Ltd., Melbourne, Australia

Project Manager, China Telecom, Shanghai, China

Customer Support Manager, Guangye Software Pty Ltd, Shanghai, China

Casual Career

Research Assistant, Enterprise AI and Data Analytics Hub, RMIT, Melbourne, Australia

Lecturer, International Design Art College, Inner Mongolia Normal University, Hohhot, China

Lecturer, School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia

Teacher, TAFE, Swinburne University of Technology, Melbourne, Australia

Lecturer, School of Computer Science, University of Technology Sydney, Sydney, Australia

Lecturer, School of Information Technology, Deakin University, Melbourne, Australia

  Deliver Deakin University subjects at Inner Mongolia Normal University

Tutor, School of Computer Science, University of Technology Sydney, Sydney, Australia

Tutor, School of Information and Communication Technology, Griffith University, Gold Coast, Australia

Tutor, School of Information Technology, Deakin University, Melbourne, Australia

Education

Doctor of Philosophy (Ph.D.), University of Technology Sydney, Sydney, Australia

Doctor of Philosophy (Ph.D.), Griffith University, Gold Coast, Australia (Transfer to University of Technology Sydney after first year study)

Master of Information Technology (Professional), Deakin University, Melbourne, Australia

Bachelor of Information Technology (Honours), Deakin University, Melbourne, Australia

Working On Papers

One paper submitted to IEEE Internet of Things Journal (IOT)

One paper submitted to ACM Computing Surveys

One paper submitted to IJCAI 2024

One paper submitted to IEEE Computational Intelligence Magzaine (CIM)

One paper submitted to IEEE Transactions on Big Data (TBD)

One paper submitted to IEEE Internet of Things Journal (IOT)

One paper submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE)

2024

Rao, B., Zhang, J., Wu, D., Zhu, C., Sun, X. and Chen, B., 2024. Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey. IEEE Transactions on Artificial Intelligence.

Wu, D., Bai, J., Song, Y., Chen, J., Zhou, W., Xiang, Y. and Sajjanhar, A., 2023, October. FedInverse: Evaluating Privacy Leakage in Federated Learning. In The Twelfth International Conference on Learning Representations. (CORE-A*)        [CODE]

2023

Zhang, J., Liu, Y., Wu, D., Lou, S., Chen, B. and Yu, S., 2023. VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems. Digital Communications and Networks, 9(4), pp.981-989. (SJR-Q1, IF:7.9)

Wang, N., Chen, J., Wu, D.#, Yang, W., Xiang, Y. and Sajjanhar, A., 2023. Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things. Journal of Information Security and Applications, 75, p.103483 (SJR-Q1, CCF-C, IF:5.6)

2022

Shen, S., Zhu, T., Wu, D., Wang, W. and Zhou, W., 2022. From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation: Practice and Experience, 34(16), p.e6002. (SJR-Q3, CCF-C, IF:2.0)

Chen, J., Guo, Q., Fu, Z., Shang, Q., Ma, H. and Wu, D., 2022, July. Campus Network Intrusion Detection based on Federated Learning. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C)

Zhao, Y., Chen, J., Zhang, J., Wu, D., Blumenstein, M. and Yu, S., 2022. Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks. Concurrency and Computation: Practice and Experience, 34(7), p.e5906. (SJR-Q3, CCF-C, IF:2.0)

Wu, D., Wang, N., Zhang, J., Zhang, Y., Xiang, Y. and Gao, L., 2022, July. A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C)

2021

Xie, Y., Chen, B., Zhang, J. and Wu, D., 2021, December. Defending against Membership Inference Attacks in Federated learning via Adversarial Example. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (pp. 153-160). IEEE. (CCF-C)

Chen, J., Wu, D., Zhao, Y., Sharma, N., Blumenstein, M. and Yu, S., 2021. Fooling intrusion detection systems using adversarially autoencoder. Digital Communications and Networks, 7(3), pp.453-460. (SJR-Q1, IF:7.9)

2020

Zhao, Y., Chen, J., Guo, Q., Teng, J. and Wu, D., 2020, October. Network anomaly detection using federated learning and transfer learning. In Security and Privacy in Digital Economy: First International Conference, SPDE 2020, Quzhou, China, October 30–November 1, 2020, Proceedings (pp. 219-231). Singapore: Springer Singapore.

Zhang, J., Wu, D., Liu, C. and Chen, B., 2020. Defending poisoning attacks in federated learning via adversarial training method. In Frontiers in Cyber Security: Third International Conference, FCS 2020, Tianjin, China, November 15–17, 2020, Proceedings 3 (pp. 83-94). Springer Singapore.

Cheng, Z., Zhang, J., Qian, H., Xiang, M. and Wu, D., 2020. A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing. In Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part I 19 (pp. 345-358). Springer International Publishing. (CORE-B, CCF-C)

Zhao, Y., Chen, J., Zhang, J., Wu, D., Teng, J. and Yu, S., 2020. PDGAN: A novel poisoning defense method in federated learning using generative adversarial network. In Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part I 19 (pp. 595-609). Springer International Publishing. (CORE-B, CCF-C)

2019

Zhao, Y., Chen, J., Wu, D., Teng, J. and Yu, S., 2019, December. Multi-task network anomaly detection using federated learning. In Proceedings of the 10th international symposium on information and communication technology (pp. 273-279).

Zhao, Y., Chen, J., Wu, D., Teng, J., Sharma, N., Sajjanhar, A. and Blumenstein, M., 2019. Network anomaly detection by using a time-decay closed frequent pattern. Information, 10(8), p.262. (SJR-Q2, IF:3.1)

Zhang, J., Chen, J., Wu, D., Chen, B. and Yu, S., 2019, August. Poisoning attack in federated learning using generative adversarial nets. In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 374-380). IEEE. (CORE-B, CCF-C)

Wu, D., Chen, J., Sharma, N., Pan, S., Long, G. and Blumenstein, M., 2019, July. Adversarial action data augmentation for similar gesture action recognition. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C)

Wu, D., Hu, R., Zheng, Y., Jiang, J., Sharma, N. and Blumenstein, M., 2019, July. Feature-dependent graph convolutional autoencoders with adversarial training methods. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C)

2018

Wu, D., Sharma, N. and Blumenstein, M., 2018, December. Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-7). IEEE. (CORE-Australasian B)

Wu, D., Sharma, N. and Blumenstein, M., 2018, November. An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE. (CORE-Australasian B)

Chou, K.P., Prasad, M., Wu, D., Sharma, N., Li, D.L., Lin, Y.F., Blumenstein, M., Lin, W.C. and Lin, C.T., 2018. Robust feature-based automated multi-view human action recognition system. IEEE Access, 6, pp.15283-15296. (SJR-Q1, IF:3.9)

2017

Wu, D., Sharma, N. and Blumenstein, M., 2017, May. Recent advances in video-based human action recognition using deep learning: A review. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2865-2872). IEEE. (CORE-B, CCF-C)

2015

Wen, S., Wu, D., Li, P., Xiang, Y., Zhou, W. and Wei, G., 2015. Detecting stepping stones by abnormal causality probability. Security and Communication Networks, 8(10), pp.1831-1844. (SJR-Q2, IF:1.968)

2014

Wu, D., Chen, X., Chen, C., Zhang, J., Xiang, Y. and Zhou, W., 2014. On addressing the imbalance problem: a correlated KNN approach for network traffic classification. In Network and System Security: 8th International Conference, NSS 2014, Xi’an, China, October 15-17, 2014, Proceedings 8 (pp. 138-151). Springer International Publishing. (CORE-B)

2011

Zhang, L., Yu, S., Wu, D. and Watters, P., 2011, November. A survey on latest botnet attack and defense. In 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 53-60). IEEE. (CORE-B, CCF-C)

Ph.D. Thesis

Wu, D., 2019. Video-based similar gesture action recognition using deep learning and GAN-based approaches (Doctoral dissertation).