Weitong Chen

Dr Weitong Chen

Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Dr. Tony Weitong Chen is a lecturer at the University of Adelaide (UoA) and a researcher at the Australian Institute for Machine Learning (AIML), having previously served as an Associate Lecturer and Post-Doc Research Fellow at the University of Queensland. He earned his PhD from the University of Queensland in 2020, after completing both his Master's and Bachelor's degrees at the University of Queensland and at Griffith University respectively. His research primarily focuses on Machine Learning with a special interest in its applications in medical data. Dr. Chen is known for his extensive collaborative efforts with professionals in academia, industry, government, and professional bodies, and his research has been supported by various internal and external grants.

I am interested in topics in areas including time-series data analysis, semi-supervised learning, natural language processing, and Internet of Things applications.

My current research relates to two specific areas:

  • Mining Imbalanced and Annotation-Missing Health Data, and
  • Enhancing Trustworthy by Generalizing from a Few Examples.

  • Appointments
    Date Position Institution name
    2022 - ongoing Researcher Australian Institute for Machine Learning (AIML)
    2022 - ongoing lecturer The University of Adelaide
    2021 - 2022 Associated Lecturer The University of Queensland
    2020 - 2022 Postdoctoral Research Fellow The University of Queensland

  • Language Competencies
    Language Competency
    Chinese (Cantonese) Can read, write, speak, understand spoken and peer review
    Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
    English Can read, write, speak, understand spoken and peer review

  • Education
    Date Institution name Country Title
    2017 - 2020 The University of Queensland Australia PhD
    2011 - 2022 The University of Queensland Australia Master of Computer Science
    2009 - 2022 Griffith University Australia Bachelor of Information System

  • Research Interests
    Information Systems Optimisation

  • Journals
    Year Citation
    2023 Li, B., Huang, Z., Chen, T. W., Dai, T., Zang, Y., Xie, W., . . . Cai, K. (2023). MSN: Mapless Short-Range Navigation Based on Time Critical Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8628-8637.
    DOI Scopus1 WoS1
    2023 Li, B., Dai, T., Chen, W., Song, X., Zang, Y., Huang, Z., . . . Cai, K. (2023). T-PORP: A Trusted Parallel Route Planning Model on Dynamic Road Networks. IEEE Transactions on Intelligent Transportation Systems, 24(1), 1238-1250.
    DOI Scopus3 WoS2
    2023 Qiu, Y., Lin, F., Chen, W., & Xu, M. (2023). Pre-training in Medical Data: A Survey. Machine Intelligence Research, 20(2), 147-179.
    DOI Scopus7 WoS5
    2022 Liu, A. C., Law, O. M. K., & Law, I. (2022). Healthcare.
    2022 Wang, Y., Chen, W., Pi, D., & Yue, L. (2022). Adaptive Multi-Hop Reading on Memory Neural Network with Selective Coverage Mechanism for Medication Recommendation. ACTA ELECTONICA SINICA, 50(4), 943.
    2021 Wang, Y., Chen, W., Pi, D., & Yue, L. (2021). Adversarially regularized medication recommendation model with multi-hop memory network. Knowledge and Information Systems, 63(1), 125-142.
    DOI WoS16
    2021 Zhang, Y., Li, B., Gao, H., Ji, Y., Yang, H., Wang, M., & Chen, W. (2021). Fine-Grained Evaluation of Knowledge Graph Embedding Model in Knowledge Enhancement Downstream Tasks. Big Data Research, 25, 9 pages.
    DOI WoS2
    2021 Li, B., Liang, R., Zhu, D., Chen, W., & Lin, Q. (2021). Blockchain-based trust management model for location privacy preserving in VANET. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3765-3775.
    DOI Scopus65 WoS38
    2021 Yue, L., Shen, H., Wang, S., Boots, R., Long, G., Chen, W., & Zhao, X. (2021). Exploring BCI control in smart environments: intention recognition via EEG representation enhancement learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(5), 1-20.
    DOI WoS8
    2021 Wang, M., Chen, W., Wang, S., Jiang, Y., Yao, L., & Qi, G. (2021). Efficient search over incomplete knowledge graphs in binarized embedding space. Future Generation Computer Systems, 123, 24-34.
    DOI WoS1
    2020 Chen, W., Long, G., Yao, L., & Sheng, Q. Z. (2020). AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction. World Wide Web, 23(5), 2753-2770.
    DOI WoS14
    2020 Yue, L., Tian, D., Chen, W., Han, X., & Yin, M. (2020). Deep learning for heterogeneous medical data analysis. World Wide Web, 23(5), 2715-2737.
    DOI WoS27
    2020 Wang, Y., Chen, W., Pi, D., & Boots, R. (2020). Graph augmented triplet architecture for fine-grained patient similarity. World Wide Web, 23(5), 2739-2752.
    DOI WoS3
    2020 Cai, K., Yue, H., Li, B., Chen, W., & Huang, W. (2020). Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography. IEEE Access, 8, 50171-50179.
    DOI Scopus8 WoS6
    2020 Zhang, A., Li, B., Wang, W., Wan, S., & Chen, W. (2020). MII: a novel text classification model combining deep active learning with bert. Computers, Materials and Continua, 63(3), 1499-1514.
    DOI
    2019 Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617-663.
    DOI WoS181
    2019 Li, Y., Chen, W., Liu, D., Zhang, Z., Wu, S., & Liu, C. (2019). IFFLC: an integrated framework of feature learning and classification for multiple diagnosis codes assignment. IEEE Access, 7, 36810-36818.
    2019 Ma, J., Wen, J., Zhong, M., Chen, W., & Li, X. (2019). MMM: multi-source multi-net micro-video recommendation with clustered hidden item representation learning. Data Science and Engineering, 4(3), 240-253.
    DOI
    2018 Wang, M., Chen, W., Wang, S., Liu, J., Li, X., & Stantic, B. (2018). Answering why-not questions on semantic multimedia queries. Multimedia Tools and Applications, 77(3), 3405-3429.
    DOI WoS3
    2017 Yang, Y., Xu, Y., Han, J., Wang, E., Chen, W., & Yue, L. (2017). Efficient traffic congestion estimation using multiple spatio-temporal properties. Neurocomputing, 267, 344-353.
    2017 Yue, L., Shi, Z., Han, J., Wang, S., Chen, W., & Zuo, W. (2017). Multi-factors based sentence ordering for cross-document fusion from multimodal content. Neurocomputing, 253, 6-14.
    2017 Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W., & Boots, R. (2017). EEG-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks. arXiv preprint arXiv:1708.06578, 1-8.
    2016 Wang, S., Chang, X., Li, X., Sheng, Q. Z., & Chen, W. (2016). Multi-task support vector machines for feature selection with shared knowledge discovery. Signal Processing, 120, 746-753.
    DOI Scopus46 WoS42
    2016 Wang, S., Pan, P., Long, G., Chen, W., Li, X., & Sheng, Q. Z. (2016). Compact representation for large-scale unconstrained video analysis. World Wide Web, 19(2), 231-246.
    DOI Scopus4 WoS2
    2015 Li, X., Zhu, G., Guo, X., & Chen, W. (2015). Spatial and Temporal Word Spectrum of Social Media.

  • Books
    Year Citation
    2022 Chen, W., Yao, L., Taotao, C., Pan, S., & Shen, T. (Eds.) (2022). Advanced Data Mining and Applications
    18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II     (Vol. 13726 LNAI). Springer International Publishing AG.

  • Book Chapters
    Year Citation
    2024 Guo, L., Zhang, W. E., Chen, W., Yang, N., Nguyen, Q., & Vo, T. D. (2024). Oyster Mushroom Growth Stage Identification: An Exploration of Computer Vision Technologies. In T. Liu, L. Yue, G. Webb, & D. Wang (Eds.), Lecture Notes in Computer Science (Vol. 14471 LNAI, pp. 67-78). SPRINGER-VERLAG SINGAPORE PTE LTD.
    DOI
    2023 Yue, L., Zhang, Y., Zhao, X., Zhang, Z., & Chen, W. (2023). Improving Motor Imagery Intention Recognition via Local Relation Networks. In B. Li, L. Yue, C. Tao, X. Han, D. Calvanese, & T. Amagasa (Eds.), Web and Big Data (Vol. 13421 LNCS, pp. 345-356). Switzerland: Springer Nature Switzerland.
    DOI
    2021 Liu, B., Zuccon, G., Hua, W., & Chen, W. (2021). Diagnosis Ranking with Knowledge Graph Convolutional Networks. In D. Hiemstra, M. -F. Moens, J. Mothe, R. Perego, M. Potthast, & F. Sebastiani (Eds.), Advances in Information Retrieval (Vol. 12656, pp. 359-374). New York, NY, USA: Springer, Cham.
    DOI

  • Conference Papers
    Year Citation
    2022 Tran, K. P., Chen, W., & Xu, M. (2022). Improving Traffic Load Prediction with Multi-modality: A Case Study of Brisbane. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13151 LNAI (pp. 254-266). Online: Springer.
    DOI
    2022 Shen, S., Chen, W., & Xu, M. (2022). What Leads to Arrhythmia: Active Causal Representation Learning of ECG Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13728 LNAI (pp. 501-515). Online: Springer International Publishing.
    DOI
    2022 Zhang, C., Zhang, Y., Mao, J., Chen, W., Yue, L., Bai, G., & Xu, M. (2022). Towards Better Generalization for Neural Network-Based SAT Solvers. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 199-210). Online: Springer, Cham.
    DOI
    2022 Qiu, Y., Chen, W., Yue, L., Xu, M., & Zhu, B. (2022). STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition. In International Conference on Advanced Data Mining and Applications Vol. 13087 (pp. 86-100). Online: Springer, Cham.
    DOI WoS1
    2022 Han, K., Chen, W., & Xu, M. (2022). Investigating Active Positive-Unlabeled Learning with Deep Networks. In Australasian Joint Conference on Artificial Intelligence Vol. 13151 (pp. 607-618). Online: Springer, Cham.
    DOI WoS1
    2021 Wang, Y., Chen, W., Pi, D., Yue, L., Wang, S., & Xu, M. (2021). Self-Supervised Adversarial Distribution Regularization for Medication Recommendation.. In IJCAI (pp. 3134-3140).
    2021 Su, G., Chen, W., & Xu, M. (2021). Positive-Unlabeled Learning from Imbalanced Data.. In IJCAI (pp. 2995-3001).
    2021 Liu, C., Yang, Y., Yao, Z., Xu, Y., Chen, W., Yue, L., & Wu, H. (2021). Discovering Urban Functions of High-Definition Zoning with Continuous Human Traces. In PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021 (pp. 1048-1057). Univ Queensland, ELECTR NETWORK: ASSOC COMPUTING MACHINERY.
    DOI
    2021 Wang, Y., Chen, W., Pi, D., Yue, L., Xu, M., & Li, X. (2021). Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation. In PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021 (pp. 2020-2029). Univ Queensland, ELECTR NETWORK: ASSOC COMPUTING MACHINERY.
    DOI WoS2
    2021 Yue, L., Tian, D., Jiang, J., Yao, L., Chen, W., & Zhao, X. (2021). Intention recognition from spatio-temporal representation of EEG signals. In M. Qiao, G. Vossen, S. Wang, & L. Li (Eds.), Databases Theory and Applications. ADC 2021 Vol. 12610 (pp. 1-12). Switzerland AG: Springer, Cham.
    DOI WoS4
    2019 Ma, J., Wen, J., Zhong, M., Liu, L., Li, C., Chen, W., . . . Li, X. (2019). Dbrec: dual-bridging recommendation via discovering latent groups. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19) (pp. 1513-1522). New York, NY, USA: Association for Computing Machinery.
    DOI WoS7
    2019 Chen, W., Yue, L., Li, B., Wang, C., & Sheng, Q. Z. (2019). DAMTRNN: a delta attention-based multi-task RNN for intention recognition. In International Conference on Advanced Data Mining and Applications Vol. LNAI 11888 (pp. 373-388). Dalian, China: Springer, Cham.
    DOI WoS9
    2019 Ma, J., Zhong, M., Wen, J., Chen, W., Zhou, X., & Li, X. (2019). RecKGC: Integrating Recommendation with Knowledge Graph Completion. In J. Li, S. Wang, S. Qin, X. Li, & S. Wang (Eds.), International Conference on Advanced Data Mining and Applications Vol. 11888 (pp. 250-265). Switzerland: Springer, Cham.
    DOI WoS2
    2019 Zhang, Z., Chen, W., Liu, C., Kang, Y., Liu, F., Li, Y., & Wei, S. (2019). Robust feature selection based on fuzzy rough sets with representative sample. In J. Li, S. Qin, S. Wang, S. Wang, & X. Li (Eds.), International Conference on Advanced Data Mining and Applications Vol. 11888 (pp. 151-165). Switzerland: Springer, Cham.
    DOI WoS1
    2019 Shi, Z., Chen, W., Liang, S., Zuo, W., Yue, L., & Wang, S. (2019). Deep interpretable mortality model for intensive care unit risk prediction. In J. Li, S. Wang, S. Qin, X. Li, & S. Wang (Eds.), Advanced Data Mining and Applications. ADMA 2019. Vol. 11888 (pp. 617-631). Dalian, China: Springer, Cham.
    DOI WoS4
    2019 Ma, J., Wen, J., Zhong, M., Chen, W., Zhou, X., & Indulska, J. (2019). Multi-source multi-net micro-video recommendation with hidden item category discovery. In Database Systems for Advanced Applications Vol. 11447 (pp. 384-400). Handle, Switzerland: Springer, Cham.
    DOI WoS5
    2019 Shi, Z., Zuo, W., Chen, W., Yue, L., Hao, Y., & Liang, S. (2019). DMMAM: Deep multi-source multi-task attention model for intensive care unit diagnosis. In Database Systems for Advanced Applications (DASFAA) Vol. 11447 (pp. 53-69). Handel, Switzerland: Springer, Cham.
    DOI WoS5
    2019 Wang, Y., Chen, W., Li, B., & Boots, R. (2019). Learning fine-grained patient similarity with dynamic bayesian network embedded RNNs. In Database Systems for Advanced Applications Vol. 11446 (pp. 587-603). Handel, Switzerland: Springer, Cham.
    DOI WoS3
    2019 Xu, X., Huang, Z., Wu, J., Fu, Y., Luo, N., Chen, W., . . . Yin, M. (2019). Finding the key influences on the house price by finite mixture model based on the real estate data in Changchun. In International Conference on Database Systems for Advanced Applications Vol. 11448 (pp. 378-382). Chiang Mai, THAILAND: Springer, Cham.
    DOI WoS2
    2019 Wang, R., Wang, M., Liu, J., Chen, W., Cochez, M., & Decker, S. (2019). Leveraging knowledge graph embeddings for natural language question answering. In G. Li, J. Yang, J. Gama, J. Natwichai, & Y. Tong (Eds.), Database Systems for Advanced Applications. DASFAA 2019. Vol. 11446 (pp. 659-675). New York, NY, USA: Springer, Cham.
    DOI WoS15
    2018 Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W., Boots, R., & Benatallah, B. (2018). Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. In Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Vol. 32 (pp. 1703-1710). Palo Alto, California, USA: AAAI Press.
    DOI WoS81
    2018 Chen, W., Wang, S., Long, G., Yao, L., Sheng, Q. Z., & Li, X. (2018). Dynamic illness severity prediction via multi-task RNNs for Intensive Care Unit. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2018) (pp. 917-922). Piscataway, NJ, USA: IEEE.
    DOI WoS22
    2018 Chen, W., Wang, S., Zhang, X., Yao, L., Yue, L., Qian, B., & Li, X. (2018). EEG-based motion intention recognition via multi-task RNNs. In Proceedings of the 2018 SIAM International Conference on Data Mining (pp. 279-287). Society for Industrial and Applied Mathematics.
    2017 Chen, H., Yin, H., Li, X., Wang, M., Chen, W., & Chen, T. (2017). People opinion topic model: opinion based user clustering in social networks. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW'17) (pp. 1353-1359). Perth, Australia: Association for Computing Machinery.
    DOI WoS15
    2017 Zheng, Y., Wei, B., Liu, J., Wang, M., Chen, W., Wu, B., & Chen, Y. (2017). Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning. In Advanced Data Mining and Applications 13th International Conference, ADMA 2017, Proceedings Vol. 10604 LNAI (pp. 655-667). Switzerland: Springer, Cham.
    DOI Scopus4
    2017 Shi, Z., Zuo, W., Chen, W., Yue, L., Han, J., & Feng, L. (2017). User relation prediction based on matrix factorization and hybrid particle swarm optimization. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1335-1341). Perth, Australia: ACM Digital.
    DOI WoS10
    2016 Li, B., Zhang, C., Chen, W., Yang, Y., Feng, S., Zhang, Q., . . . Li, D. (2016). Dynamic reverse furthest neighbor querying algorithm of moving objects. In International Conference on Advanced Data Mining and Applications (pp. 266-279). Springer, Cham.
    2016 Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., & Wang, S. (2016). Learning graph-based poi embedding for location-based recommendation. In Proceedings of the 25th ACM international on conference on information and knowledge management (pp. 15-24).

  • Conference Items
    Year Citation
    2018 Utomo, C. P., Li, X., & Chen, W. (2018). Treatment recommendation in critical care: A scalable and interpretable approach in partially observable health states. Poster session presented at the meeting of Proceedings of the International Conference on Information Systems - Bridging the Internet of People, Data, and Things, ICIS 2018. Illinois, USA: Association for Information Systems.

  • Theses
    Year Citation
    - Chen, W. (n.d.). Deep multi-task learning on time-series medical data.

Grants and Funding

  • ARC DP - Principal Investigator 2024 - 2026 Institutional Grants System icon Towards knowledge discovery from imperfect and evolving data (ARC)
  • UoA Sustainability FAME Strategy  Grant 2023 - Investigator 2023 - Sustainable Warriors or Marketing Mirage? (UoA)
  • UoA CMS Research Grants 2023 - 2023 (UoA)
  • ANA Partnership Fund- Principal Investigator 2023 - 2023 AID-MI: Artificial Intelligence Based Diagnosis Using Multimodal Information For Cardiopulmonary Disease (UoA & UoN)
  • UoA START-UP GRANT - Principal Investigator 2022 - 2024 
  • UQ AI-ECR Research Fund - Principal Investigator 2022 - 2022 Towards Positive Emotion: Artificial Intelligence-Based Expression Rephrasing (UQ) 
  • UQ Cyber Security Seed Fund - Principal Investigator 2022 - 2022 Collaborated Learning with Medical Data Making High-Stake Decision Without Information Leakage (UQ & RBWH) 
  • UQ AI Initiative - Investigator 2021 - 2022 AI Monitored ICU Illness Severity Prediction (UQ & RBWH)
  • ITEE Research Support Fund - Principal Investigator 2021 - 2021   Multi-Task Memory-Fused RNNs for Explainable Illness Severity Prediction (UQ)

 

Awards & Achievements 

  • 2023 Early Career Research Excellence Award, The University of Adelaide, Australia
  • 2023 Outstanding Service Award, The 36th Australasian Joint Conference on Artificial Intelligence, Australia
  • 2022 Best Student Paper Award, APWEB-WAIM 2022, China
  • 2022 Best Poster Award, The 34th Australasian Joint Conference on Artificial Intelligence, Australia
  • 2021 Best Presentation Award, 17th International Conference on Advanced Data Mining and Applications, Australia
  • 2021 Best Paper - Highly Commended Award, Australasian Computer Science Week Multiconference, New Zealand
  • 2019 Best Student Paper Award, 15th International Conference on Advanced Data Mining and Applications, China
  • 2018 Student Travel Grant, International Conference on Data Mining 2018 Singapore
  • 2016 Outstanding Service Award, 12th International Conference on Advanced Data Mining and Applications, Australia
  • 2014 Premier‘s Awards for Open Data, Australia
  • 2014 1st Place, Microsoft Startup Q Award for Open Data, Australia

To be considered for a recommendation letter from me for graduate school applications, you must satisfy at least two of the following criteria:

  1. Achieved a grade exceeding 90% in my class.
  2. Completed your MSc project under my supervision with a High Distinction (HD) grade.
  3. Represented the University of Adelaide (UoA) in the world final of the ACM programming contest.
  4. Demonstrated strong preparation for graduate studies, such as contributing to a publication in a reputable conference or journal.

Please note:

  • Writing a recommendation letter is a time-intensive process, so I ask that your request be made with due courtesy. Requests lacking politeness will not be acknowledged.
  • Given the limited number of letters I can write annually, I may have to decline many requests. This decision is not personal. If I agree to consider your request, expect a 30-minute interview where I will assess your computer science knowledge through technical questions.
  • I will only write recommendation letters for up to five graduate schools per student.

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2024 Co-Supervisor Large Language Models for Adaptive Microservice Composition and Orchestration Master of Philosophy Master Full Time Mr Jeffrey Chan
    2024 Principal Supervisor Cutting-Edge Artificial Intelligence in Smart and Digital Agriculture Doctor of Philosophy Doctorate Full Time Ms Lipin Guo
    2024 Principal Supervisor Artificial intelligence-based diagnosis using multimodal information Doctor of Philosophy Doctorate Full Time Mr Liangwei Zheng
    2023 Co-Supervisor Leveraging Knowledge-aware Methodologies for Multi-document Summarization Doctor of Philosophy Doctorate Full Time Miss Yutong Qu
    2023 Principal Supervisor An efficient and robust deep learning framework for multi-scale feature fusion object detection Doctor of Philosophy Doctorate Full Time Mr Yibo Sun
    2023 Co-Supervisor Learning from imperfect and evolving data Doctor of Philosophy Doctorate Full Time Mr Chang Dong
    2023 Co-Supervisor Federated learning for data product generation Doctor of Philosophy Doctorate Full Time Mr Alireza Seyed Shakeri
    2023 Co-Supervisor Can Active Learning and Federated Learning Help Sensitive Information Protection Master of Philosophy Master Full Time Mr Lishan Yang

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2023 - 2024 Co-Supervisor Deep Learning Based Multi-document Summarization Doctor of Philosophy Doctorate Full Time Ms Congbo Ma

  • Other Supervision Activities
    Date Role Research Topic Location Program Supervision Type Student Load Student Name
    2023 - ongoing External Supervisor Domain Adaptation in Causality Views The University of Queensland - Doctorate Full Time Shaofei Shen
    2022 - ongoing Principal Supervisor Deep learning methods for imbalanced medical multivariate time series data University of Queensland Computer Science Doctorate Full Time Yixuan qiu
    2022 - ongoing External Supervisor High-stakes Decision Making with Weakly Supervised Data The University of Queensland Computer Science Doctorate Full Time Yawen Zhao
    2022 - ongoing External Supervisor Distribution-aware Automatic Summary Generalisation from Multi-modal Medical Data The University of Queensland - Doctorate Full Time Hao Gong
    2022 - ongoing Principal Supervisor Fairness-aware Personal Medicine: From Disease Diagnosis to Treatment The University of Qeensland Computer Science Doctorate Full Time Chenhao Zhang
    2022 - ongoing External Supervisor Machine Learning for Cyber Security The University of Queensland Cyber Security Doctorate Full Time Kun Han
  • Position: Lecturer
  • Phone: 83130676
  • Email: weitong.chen@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor Level Four
  • Room: 4.45
  • Org Unit: Computer Science
Connect With Me
External Profiles