Short Biography

Wei Liu is the Director of Future Intelligence Research Lab, and an Associate Professor in Machine Learning, at the School of Computer Science, the University of Technology Sydney (UTS), Australia. Wei obtained his PhD degree in Machine Learning research at the University of Sydney (USyd). His current research focuses on adversarial machine learning, robustness of AI algorithms, fairness of AI models, multimodal learning, and natural language processing.

Wei's research papers are constantly published in CORE A*/A and Q1 (i.e., top-prestigious) journals and conferences. He has received 3 Best Paper Awards. Besides, one of his first-authored papers received the Most Influential Paper Award in the CORE A Ranking conference PAKDD 2021. He was a nominee for the Australian NSW Premier's Prizes for Early Career Researcher Award in 2017. He has obtained over $5 million in research funding, including ARC, government competitive grants, and industry research projects.

Future Intelligence Research Lab

The Future Intelligence Research Lab produces next-generation AI algorithms that address the following emerging challenges:

• AI Security and Cyber Attacks (See three representative papers from our lab: [1], [2], [3].)

• AI Robustness with Multi-modal Data. (Three representative papers: [4], [5], [6].)

• Model Fairness with Data Imbalance. (Three representative papers: [7], [8], [9].)

Publications

Due to the popularity of Wei's name, the DBLP does not have an accurate record of his publications. His Google Scholar page is here.

  • •Cheng Yongkang, Mingjiang Liang, Shaoli Huang and Wei Liu: ExpGest: Expressive Full-Body Gesture Generation Using Diffusion Model and Hybrid Audio-Text Guidance. In Proceedings of the 2024 IEEE Conference on Multimedia Expo (ICME 2024).

  • •Shilu Yuan, Dongfeng Li, Wei Liu, Meng Chen, Junjie Zhang, and Yongshun Gong: Fine-Grained Urban Flow Inference with Dynamic Multi-scale Representation Learning. In Proceedings of the 29th International Conference on Database Systems for Advanced Applications (DASFAA 2024).

  • • Li-Chiao Wang, Wei Liu and Chung-Shou Liao: Developing Incremental Learning Models with Prototypes. In Proceedings of the 2024 IEEE International Joint Conference on Neural Network (IJCNN 2024).

  • • Tao Zheng and Wei Liu: Orthogonal Synthesis: Data Augmentation for Multimodal Sentiment Analysis. In Proceedings of the 2024 IEEE International Joint Conference on Neural Network (IJCNN 2024).

  • • Mingze Ni and Wei Liu: Reversible Jump Attack to Textual Classifiers with Modification Reduction. In Machine Learning  journal, 2024.

  • • Xiaoyu Li, Yongshun Gong, Wei Liu, Yilong Yin, Yu Zheng, and Liqiang Nie: Dual-track Spatio-temporal Learning for Urban Flow Inference with Adaptive Normalization. In Artificial Intelligence  journal, 2024.

  • • Mingze Ni, Zhensu Sun, and Wei Liu: Fraud’s Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024.

  • • Mingjiang Liang, Shaoli Huang and Wei Liu: Dynamic Semantic Structure Distillation for Low-resolution Fine-grained Recognition. In Pattern Recognition, 2023.

  • • Ruifeng Wang, Yuansheng Liu, Yongshun Gong, Wei Liu, Meng Chen, Yilong Yin, and Yu Zheng: Fine-grained Urban Flow Inference with Unobservable Data via Space-Time Attraction Learning. In Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM 2023).

  • • Yongshun Gong, Zhibin Li, Wei Liu, Xinkai Lu, Xinwang Liu, Ivor Tsang, and Yiling Yin: Missingness-pattern-adaptive Learning with Incomplete Data. In IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2023.

  • • Xin Chen, Dongliang Guo, Li Feng, Bo Chen, and Wei Liu: Compass+Ring: A Multimodal Menu to Improve Interaction Performance and Comfortability in One-handed Scenarios. In Proceedings of the 22nd IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2023)

  • • Xinghao Yang, Yongshun Gong, Weifeng Liu, James Bailey, Dacheng Tao, and Wei Liu: Semantic-Preserving Adversarial Text Attacks. In IEEE Transactions on Sustainable Computing (IEEE T-SUSC), 2023.

  • • Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, and Wanlei Zhou: Adversarial Machine Learning - Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence. Springer Book, Springer International Publishing, 2023.

  • • Linsey Pang, Kexin Xie, Yuxi Zhang, Damian Xu, and Wei Liu: Adversarial Active Learning with Guided BERT Feature Encoding. In Proceedings of The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023).

  • • Xinghao Yang, Weifeng Liu, and Wei Liu: Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition. In Proceedings of The 39th IEEE International Conference on Data Engineering (ICDE 2023).

  • • Mingze Ni, Zhensu Sun and Wei Liu: Fraud’s Bargain Attacks to Textual Classifiers via Metropolis-Hasting Sampling. In Proceedings of The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).

  • • Jiyao Li and Wei Liu: Summarization Attack via Paraphrasing. In Proceedings of The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).

  • • Mingze Ni, Tianqing Zhu, Shui Yu, and Wei Liu: Attacking Neural Machine Translations via Hybrid Attention Learning. In Machine Learning  journal, 2022.

  • • Linsey Pang, Wei Liu, Lingfei Wu, Kexin Xie, Stephen Guo, and Musen Wen: Applied Machine Learning Methods for Time Series Forecasting. In Proceedings of the 31st ACM Conference on Information and Knowledge Management. (CIKM 2022).

  • • Linsey Pang, Wei Liu, Kexin Xie, James Bailey, Longbing Cao, and Yuxi Zhang: Deep Learning for Search and Recommendation. In Proceedings of the 31st ACM Conference on Information and Knowledge Management. (CIKM 2022).

  • • Kam Fataliyev and Wei Liu: MMDL: A Novel Multi-modal Deep Learning Model for Stock Market Prediction. In Proceedings of the 2022 IEEE International Conference on Data Science and Advanced Analytics. (DSAA 2022).

  • • Mingze Ni, Tianqing Zhu, Shui Yu, and Wei Liu: Attacking Neural Machine Translations via Hybrid Attention Learning. In Proceedings of the 2022 IEEE International Conference on Data Science and Advanced Analytics. (DSAA 2022).

  • • Linsey Pang, Wei Liu, Keng-Hao Chang, and Moumita Bhattacharya: Deep Search Relevance Ranking in Practice. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (KDD 2022).

  • • Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Wei Liu, and Suju Rajan: AdKDD: Knowledge Discovery from Advertisement Data. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (KDD 2022).

  • • Tao Zhang, Sunny Verma, and Wei Liu: Interpretable Binaural Ratio for Visually Guided Binaural Audio Generation. In Proceedings of the 2022 International Joint Conference on Neural Networks. (IJCNN 2022).

  • • Mingjiang Liang, Shaoli Huang, Shirui Pan, Mingming Gong, and Wei Liu: Learning Multi-level Weight-centric Features for Few-shot Learning. In Pattern Recognition, 2022.

  • • Huan Tian, Tianqing Zhu, Wei Liu, and Wanlei Zhou: Image Fairness in Deep Learning: Problems, Models, and Challenges. In Neural Computing and Applications, 2022.

  • • Hanyun Zhang, Dongliang Guo, Wei Liu, and Junlan Nie: An Improved Algorithm for Video Quality Assessment. In Multimedia Systems, 2022.

  • • Lianbo Zhang, Shaoli Huang, and Wei Liu: Enhancing Mixture-of-Experts by Leveraging Attention for Fine-Grained Recognition. In IEEE Transactions on Multimedia, 2021.

  • • Lei Hu, Shaoli Huang, Wei Liu, and Jifeng Ning: Do We Really Need Frame-by-Frame Annotation Datasets for Object Tracking? In Proceedings of The 29th ACM International Conference on Multimedia (MM 2021).

  • • Lianbo Zhang, Shaoli Huang, and Wei Liu: Learning Sequentially Diversified Representations for Fine-grained Categorization. In Pattern Recognition, 2021.

  • • Xinghao Yang, Weifeng Liu, Dacheng Tao, and Wei Liu: BESA: BERT-based Simulated Annealing for Adversarial Text Attacks. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).

  • • Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, and Yu Zheng: Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.

  • • Ali Braytee and Wei Liu: Learning Discriminative Features using Multi-label Dual Space. In Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021).

  • • Sunny Verma, Chen Wang, Liming Zhu, and Wei Liu: Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion. In IEEE Transactions on Cybernetics, 2021. (IF: 11.079).

  • • Changwei Sung, Xinghao Yang, Chungshou Liao, and Wei Liu: IntRoute: An Integer Programming based Approach for Best Bus Route Discovery. In Proceedings of the 26th International Conference on Database Systems for Advanced Applications (DASFAA 2021).

  • • Xinghao Yang, Weifeng Liu, James Bailey, Dacheng Tao, and Wei Liu: Bigram and Unigram Based Text Attack via Adaptive Monotonic Heuristic Search. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021).

  • • Xinghao Yang, Weifeng Liu, Shengli Zhang, Wei Liu, and Dacheng Tao: Targeted Attention Attack on Deep Learning Models in Road Sign Recognition. In IEEE Internet of Things Journal (IoT-J), 2021. IF: 9.936.

  • • Xinghao Yang, Weifeng Liu, and Wei Liu: Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.

  • • Lianbo Zhang, Shaoli Huang, Wei Liu: Intra-class Part Swapping for Fine-Grained Image Classification. In Proceedings of the 2021 IEEE Winter Conference on Applications in Computer Vision (WACV 2021).

  • • Sunny Verma, Fan Jin, Yang Wang, Fang Chen, and Wei Liu: Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis. In Proceedings of the 20th IEEE International Conference on Data Mining (ICDM 2020).

  • • Yongshun Gong, Jian Zhang, and Wei Liu: A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).

  • • Xinghao Yang and Wei Liu: Population Location and Movement Estimation through Cross-domain Data Analysis. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).

  • • Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, and Yu Zheng: Online Spatio-temporal Crowd Flow Distribution Prediction for Complex Metro System. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

  • • Aneesh Chivukula, Xinghao Yang, Tianqing Zhu, Wanlei Zhou, and Wei Liu: Game Theoretical Adversarial Deep Learning with Variational Adversaries. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

  • • Xinghao Yang, Wei Liu, Wenfeng Liu, Dacheng Tao: A Survey on Canonical Correlation Analysis. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

  • • Xinghao Yang, and Wei Liu: Cyber Attack to AI Systems: Targeted Attention-based Attacks to Road Sign Recognition Models. In Proceedings of the 2020 Cyber Defence Next Generation Technology Conference (CDNG 2020).

  • • Yongshun Gong, Wei Liu, Zhibin Li, and Jian Zhang: Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).

  • • Aneesh Chivukula, Xianghao Yang, and Wei Liu: Adversarial Deep Learning with Stackelberg Games. In Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019).

  • • Lianbo Zhang, Shaoli Huang, Wei Liu, and Dacheng Tao: Learning a Mixture of Gradually Enhanced Experts for Fine-Grained Categorization. In Proceedings of the 2019 International Conference on Computer Vision (ICCV 2019).

  • • Quan Do, Wei Liu, Jin Fan, and Dacheng Tao: Unveiling Hidden Implicit Similarities for Cross-Domain Recommendation. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.

  • • Sunny Verma, Chen Wang, Liming Zhu, and Wei Liu: Towards Effective Data Augmentation via Unbiased GAN Utilization. In Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019).

  • • Quan Do, Sunny Verma, and Wei Liu: Multiple Knowledge Transfer for Cross Domain Recommendation. In Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019).

  • • Sunny Verma, Chen Wang, Liming Zhu, and Wei Liu: DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

  • • Sunny Verma, Chen Wang, and Wei Liu: A Compliance Checking Framework for DNN Models. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

  • • Ali Braytee, Wei Liu, and Paul Kennedy: Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2019. 5-year Impact Factor: 10.47.

  • • Zhizhou Yin, Sanjay Chawla, and Wei Liu: Adversarial Attack and Defense with Deep Learning Frameworks. Book Chapter in Deep Learning for Cyber Security, Springer.

  • • Yongshun Gong, Wei Liu, Jian Zhang, and Yu Zheng: Network-wide Crowd Flow Prediction of Sydney Trains via customized Online Non-negative Matrix Factorization. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018).

  • • Aneesh Chivukula and Wei Liu: Discovering Granger-causal Features from Deep Learning Networks. In Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018).

  • • Sunny Verma, Chen Wang, Liming Zhu, and Wei Liu: Improving Deep Learning Networks via Integrating Two Views of Images. In Proceedings of the 25th International Conference on Neural Information Processing (ICONIP 2018).

  • • Aneesh Chivukula and Wei Liu: Adversarial Deep Learning Models with Multiple Adversaries, In IEEE Transactions on Knowledge and Data Engineering (TKDE). 2018. Impact Factor: 4.935

  • • Quan Do and Wei Liu: Scalable Multimodal Factorization for Learning from Very Big Data, Book Chapter in Multimodal Analytics for Next-Generation Big Data Technologies and Applications, Springer

  • • Pengyi Yang, John Ormerod, Wei Liu, and Albert Zomaya: AdaSampling for Positive-Unlabeled and Label Noise Learning with Bioinformatics Applications. In IEEE Transactions on Cybernetics. 2018. Impact Factor: 11.079.

  • • Zhizhou Yin, Fei Wang, Wei Liu, and Sanjay Chawla: Sparse Feature Attacks in Adversarial Learning. In IEEE Transactions on Knowledge and Data Engineering (TKDE). 2018. Impact Factor: 3.438.

  • • Shoujin Wang, Liang Hu, Longbing Cao, and Wei Liu: Attention-based Transactional Context Embedding for Next-Item Recommendation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018).

  • • Ali Braytee, Wei Liu, Daniel Catchpoole and Paul Kennedy: Multi-Label Feature Selection using Correlation Information. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017).

  • • Pengyi Yang, Wei Liu, and Jean Yang: Positive Unlabelled Learning via Wrapper-based Adaptive Sampling. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).

  • • Quan Do, Wei Liu, Fang Chen: Discovering both Explicit and Implicit Similarities for Cross-Domain Recommendations. In Proceedings of the 2017 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017).

  • • Aneesh Chivukula, and Wei Liu: Adversarial learning games with deep learning models. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN 2017).

  • • Ali Braytee, Wei Liu, and Paul Kennedy: Supervised context-aware non-negative matrix factorization to handle high-dimensional high-correlated imbalanced biomedical data. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN 2017).

  • • Sunny Verma, Wei Liu, Chen Wang, and Liming Zhu: Extracting Highly Effective Features for Supervised Learning via Simultaneous Tensor Factorization. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017).

  • • Ali Braytee, Wei Liu, Daniel Catchpoole, Paul Kennedy: Balanced Supervised Non-Negative Matrix Factorization for Childhood Leukaemia Analysis. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016).

  • • Prasad Cheema, Khoa Nguyen, Wei Liu, et. al.: On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016).

  • • Quan Do, Thanh Pham, Wei Liu, and Kotagiri Ramamohanarao: WTEN: An Advanced Coupled Tensor Factorization Strategy for Learning from Imbalanced Data. In Proceedings of the 17th International Conference on Web Information Systems Engineering (WISE 2016).

  • • Lida Rashidi, Andrey Kan, Wei Liu, James Bailey, Rao Kotagiri, et. al.: Node Re-Ordering for Anomaly Detection in Time Evolving Graphs. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2016).

  • • Wei Liu: Factorization of Multiple Tensors for Supervised Feature Extraction. In Proceedings of the 23rd International Conference on Neural Information Processing. (ICONIP 2016).

  • • Ali Braytee, Wei Liu, Paul Kennedy: A Cost-Sensitive Learning Strategy for Feature Extraction from Imbalanced Data. In Proceedings of the 23rd International Conference on Neural Information Processing. (ICONIP 2016).

  • • Ling Luo, Wei Liu, Irena Koprinska, Fang Chen: A Discrimination-Aware Association Rule Classifier for Decision Support. In Transactions on Large Scale Data and Knowledge Centered Systems (Invited paper).

  • • Quan Do and Wei Liu: ASTEN: an Accurate and Scalable Approach to Coupled Tensor Factorisation. In Proceedings of the 2016 International Joint Conference in Neural Networks (IJCNN 2016).

  • • Shoujin Wang, Wei Liu, and Paul Kennedy: Training Deep Networks on Imbalanced Data Sets. In Proceedings of the 2016 International Joint Conference in Neural Networks (IJCNN 2016).

  • • Hoang Nguyen, Wei Liu, Paul Rivera, and Fang Chen: TrafficWatch: Real-time Traffic Incident Detection and Monitoring Using Social Media. In Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2016).

  • • Hoang Nguyen, Wei Liu, and Fang Chen: Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data. Accepted in IEEE Transactions on Big Data.

  • • Jingyu Shao, Junfu Yin, Wei Liu, and Longbing Cao: Mining Actionable Combined Patterns Satisfying both Utility and Frequency Criteria. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015).

  • • Qianqian Chen, Liang Hu, Jia Xu, Wei Liu, and Longbing Cao: Document Similarity Analysis via Involving both Explicit and Implicit Sematic Relatedness. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015).

  • • Ling Luo, Wei Liu, Irena Koprinska, and Fang Chen: Discrimination-Aware Association Rule Mining for Unbiased Data Analytics. Book chapter in Big Data Analytics and Knowledge Discovery, 2015.

  • • Ling Luo, Irena Koprinska and Wei Liu: Discrimination-Aware Classifiers for Student Performance Prediction. In Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015).

  • • Khoa Nguyen, Wei Liu, Fang Chen, Peter Runcie, et al.: Using Tensor Analysis for Damage Identification in Civil Structures. In Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015).

  • • Xinxin Jiang, Wei Liu, Longbing Cao, Guodong Long: Coupled Collaborative Filtering for Context-aware Recommendation. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015).

  • • Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao: Actionable Combined High Utility Itemset Mining. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015).

  • • Fei Wang, Wei Liu, Sanjay Chawla: On Sparse Feature Attacks in Adversarial Learning. In Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM 2014). (Acceptance rate: 19%)

  • • Ronnie Taib, David Yee, Fang Chen, and Wei Liu: Improved incident management through anomaly detection in historical records. In 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World.

  • • Wei Liu, Dong Lee, and Rao Kotagiri: Using Local Information to Significantly Improve Classification Performance. In proceedings of the 23rd ACM Conference on Information and Knowledge Management (CIKM 2014).

  • • Victor Chu, Wei Liu, Raymond Wong, Fang Chen, and Charles Perng: Traffic Analysis as a Service via a Unified Model. In Proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014).

  • • Victor Chu, Wei Liu, Raymond Wong, and Fang Chen: Causal Structure Discovery for Spatio-temporal Data. In Proceedings of the 19th International Conference on Database Systems for Advanced Applications (DASFAA 2014).

  • • Jeffrey Chan, Wei Liu, James Bailey, Christopher Leckie, and Rao Kotagiri: Structure-aware Distance Measures for Comparing Clusterings in Graphs. In Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014).

  • • Wei Liu, James Bailey, Christopher Leckie, Fang Chen, and Rao Kotagiri: A Bayesian Classifier for Learning from Tensorial Data. In Proceedings of the 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013).

  • • Wei Liu, James Bailey, Christopher Leckie, Fang Chen, and Rao Kotagiri: A Bayesian Classifier for Learning from Tensorial Data. In Proceedings of the 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013).

  • • Timothy C. Havens, James C. Bezdek, Christopher Leckie, Wei Liu, James Bailey, and Kotagiri Ramamohanarao: Discrimination-aware classification for imbalanced datasets. In Proceedings of the 2013 IEEE International Conference on Fuzzy Systems. (FUZZ-IEEE 2013).

  • • Jeffrey Chan, Wei Liu, James Bailey, Christopher Leckie, and Rao Kotagiri: Discovering Latent Blockmodels in Sparse and Noisy Graphs using Non-negative Matrix Factorisation. In Proceedings of the 22nd ACM Conference on Information and Knowledge Management (CIKM 2013).

  • • Wei Liu, James Bailey, Christopher Leckie, Rao Kotagiri: Mining Labelled Tensors by Discovering both their Common and Discriminative Subspaces. In Proceedings of the 2013 SIAM Conference on Data Mining (SDM 2013).

  • • Fei Wang, Wei Liu, and Sanjay Chawla: Tikhonov or Lasso Regularization: Which is Better and When. In Proceedings of the 2013 IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2013).

  • • Goce Ristanoski, Wei Liu, and James Bailey: Time-Dependent Loss Enhanced SVMs for Time Series Regression. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2013).

  • • Wei Liu, Sanjay Chawla, James Bailey, Christopher Leckie and Rao Kotagiri: An Efficient Adversarial Learning Strategy for Constructing Robust Classification Boundaries. In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (AI 2012). This paper won the best paper award.

  • • Wei Liu, Jeffrey Chan, James Bailey, Christopher Leckie and Rao Kotagiri: Utilizing Common Substructures to Speedup Tensor Factorization for Mining Dynamic Graphs. In Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM 2012).

  • • Wei Liu, Andrey Kan, James Bailey, Christopher Leckie, Jian Pei and Rao Kotagiri: On Compressing Weighted Time-evolving Graphs. In Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM 2012).

  • • Linsey Pang, Sanjay Chawla, Wei Liu and Yu Zheng: On Detection of Emerging Anomalous Traffic Patterns Using GPS Data. Data & Knowledge Engineering journal, vol. 87, pages 357-373, 2013.

  • • Goce Ristanoski, Wei Liu, James Bailey: Time Series Forecasting using Distribution Enhanced Linear Regression. In Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013).

  • • Pengyi Yang, Wei Liu, Bingbing Zhou, Sanjay Chawla, Albert Zomaya: Ensemble-based wrapper methods for feature selection and class imbalance learning. In Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013).

  • • Wei Liu and Sanjay Chawla: Mining Adversarial Patterns via Regularized Loss Minimization. Machine Learning, vol. 81, num. 1, pages 69-83.

  • • Jeffrey Chan, Wei Liu, Christopher Leckie, James Bailey and Rao Kotagiri: SeqiBloc: Mining Multi-time Spanning Blockmodels in Dynamic Graphs. In Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2012), pages 651–659.

  • • Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan and Xing Xie: Discovering Causal Interactions among Spatio-Temporal Outliers from Traffic Data Streams. In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), pages 1010–1018.

  • • Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu and Yu Zheng: On Mining Emerging Patterns on Traffic Networks. In Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA 2011). This paper won the best paper award.

  • • Wei Liu and Sanjay Chawla: Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. In Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Part II, pages 345–356.

  • • Wei Liu and Sanjay Chawla: A Quadratic Mean based Supervised Learning Model for Handling Data Skewness. In Proceedings of the 2011 SIAM International Conference on Data Mining (SDM 2011), pages 188–198.

  • • Elizabeth Wu, Wei Liu, and Sanjay Chawla: Spatio-Temporal Outlier Detection in Precipitation Data. In Knowledge Discovery from Sensor Data, Springer, 2010.

  • • Wei Liu and Sanjay Chawla: Mining Adversarial Patterns via Regularized Loss Minimization. In Proceedings of 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010) .

  • • Wei Liu, Sanjay Chawla, David Cieslak and Nitesh Chawla: A Robust Decision Tree Algorithm for Imbalanced Data Sets. In Proceedings of the Tenth SIAM International Conference on Data Mining (SDM 2010), pages 766-777.

  • • Wei Liu and Sanjay Chawla: A Game Theoretical Model for Adversarial Learning. In Proceedings of the 2009 IEEE ICDM Workshops 2009, pages 25-30.

Code Release

Our Lab releases open-source code corresponding to the AI theories and algorithms we have developed, such as:

Research Grants

Wei has been leading a number of nationally competitive grants and contract research projects funded by government agencies and industry organisations, spanning information security, digital media, retail, insurance, finance, transportation, and regulation sectors:

  • • "Foragecaster", Chief Investigator; Project funder: Agriwebb and Food Agility CRC, 2024 - 2027.

  • • "Deep Learning Attacks and Active Defences: A Cybersecurity Perspective", Chief Investigator; Project funder: ARC Discovery Project, 2023 - 2026. This project received the special highlight in the ARC Media Release.

  • • "Machine Learning for Agricultural Growth Prediction", Chief Investigator; Project funder: Agriwebb and Food Agility CRC, 2023.

  • • "Knowledge Graphs for Process Monitoring and Control", Sole Chief Investigator; Project funder: AVEVA Software Australia Pty Ltd, 2022 - 2025.

  • • "Skill and CV Data Analytics", Chief Investigator; Project funder: Reejig Pty Ltd, 2021 - 2022.

  • • "Adversarial Learning in the Physical World", Sole Chief Investigator; Project funder: NSW Department of Industry, 2021 - 2022.

  • • "Product Recommendation Systems with Multimodal Content Analysis", Sole Chief Investigator; Project funder: PINSPACE, 2021 - 2024.

  • • "Defending AI Models again Adversarial Attacks", Sole Chief Investigator; Project funder: CSIRO's Data61, 2020 - 2023.

  • • "Adversarial Learning for Feature Obfuscation", Sole Chief Investigator; Project funder: NSW Department of Industry, 2020 - 2021.

  • • "Trajectory Data Mining", Sole Chief Investigator; Project funder: SUSTech, 2020-2021.

  • • "Deep Learning in Finance", Sole Chief Investigator; Project funder: The RoZetta Institute, 2019 - 2021.

  • • "Visual Computing and Big Data Research - Extension", Sole Chief Investigator; Project funder: HDU, 2019.

  • • "Next Best Action Recommendations", Sole Chief Investigator; Project funder: FlightSpeed Tech, 2019.

  • • "Learning from Multi-modal and Multi-source Data", Sole Chief Investigator; Project funder: SIEF, 2019.

  • • "Product Recommendation Systems with Multimodal Content", Sole Chief Investigator; Project funder: Blackwoods, 2018.

  • • "Visual Computing and Big Data Research - Extension", Sole Chief Investigator; Project funder: HDU, 2018.

  • • "Visual Analytics for Digital Media", Sole Chief Investigator; UTS FEIT Data Arena Research Grant, 2018.

  • • "Ad Recommender and Visualisation Tool", Lead Chief Investigator; Project funder: Assembly Media, 2017.

  • • "Visual Computing and Big Data Research", Sole Chief Investigator; Project funder: HDU, 2017.

  • • "Advanced Analytics to Reveal Novel Insights into ‘Worth of Water’", Chief Investigator; Project funder: NSW DPI, 2016.

  • • "Advanced Data Analytics Platforms", Sole Chief Investigator; Project funder: National ICT Australia, 2016 – 2019.

  • • "Data Analytics Models for Stock Market Surveillance", Sole Chief Investigator; Project funder: NASDAQ OMX and CMCRC, 2016 - 2019.

  • • "Analytics Model to Support Strategic Planning", Sole Chief Investigator; Project funder: NSW Department of Fair Trading, 2015.

  • • "Discovering Deep Insights Into Customer Retention", Chief Investigator; Project funder: Colonial Mutual, 2015.

  • • "Transport Data Science and Advanced Analytics", Sole Chief Investigator; Project funder: National ICT Australia, 2015 – 2018.

  • • "Large-scale Data Science Algorithms", Lead Chief Investigator; UTS Early Career Researcher Grant, 2015.

  • • “Traffic Watch for Transport Control Service”, Chief Investigator; Project funder: Transport Management Centre, May 2013 – June 2014.

  • • “Congestion Propagation and Hotspot Detection in Sydney CBD”, Chief Investigator; Project funder: NSW RMS; Aug – Dec 2013.

  • • “Data Fusion Technologies for Comprehensive Transport Data Analysis in Melbourne”, Chief Investigator; Project funder: VicRoads; Jun – Sep 2013.

  • • “Time of Arrival Estimations using HD Vehicle Trajectories”, Chief Investigator; Project funder: Tomtom. Jan 2013 – March 2013.

  • • “Early Detection of Road Traffic Incidents using Social Media”, Chief Investigator; Project funder: the Transport Management Centre; Oct – Dec 2012.

  • • “Causal Inference for Sequential Traffic Congestion", Chief Investigator; Project funder: Microsoft Research Asia; Nov 2010 – Mar 2011.

  • • “Abnormal Claim Detection from Worker’s Compensations”, Chief Investigator; Project funder: CGU Insurance; Mar 2010 – Jun 2011.

  • • “Data Integration for Cross-Market Capital Trading Systems”, Chief Investigator; Project funder: the SMARST Group, Jun 2008 – Dec 2009.

Research Team

Postdoc:

  • • Dr Mingze Ni

Current Students under my Principal Supervision:

  • • Da Cheng Gu, PhD student, Adversarial Attacks to Large Language Models

  • • Akhilesh Singh, PhD student, Stocks Price Prediction using Deep Learning based Information Fusion

  • • Huining Cui, PhD student, Safety Benchmarks for Large Language Models

  • • Zhen Zhao, PhD student, Knowledge Graphs Learning for Process Monitoring and Control

  • • Yunce Zhao, PhD student, Adversarial Machine Learning

  • • Mingjiang Liang, PhD student, Few-Shot Image Classification

  • • Jiyao Li, MPhil student, Multimodal Sentiment Analysis

  • • Tao Zheng, PhD student, Multimodal Transfer Learning

  • • Kam Fataliyev, PhD student, Machine Learning for Finance

  • • George Qiao, PhD student, Multimodal Machine Learning

Graduated Students:

  • • Mingze Ni, PhD student, Adversarial Attacks via Word-Level Manipulation on NLP Models (First Job: Postdoc at UTS)

  • • Xinghao Yang, PhD student, Adversarial Machine Learning on AI Model Attacks (First Job: Associate Professor at China University of Petroleum)

  • • Lianbo Zhang, PhD student, Improved Fine-Grained Representation Learning with Data Transformation

  • • Maral Nalbandian, Honours student, AI-enabled Chatbot Development (First Job: Software Engineer at Commonwealth Bank)

  • • Yongshun Gong, PhD student, Multiview Urban Computing (First Job: Associate Professor at Shandong University, China)

  • • Xiaocai Zhang, PhD student, Deep Learning for Traffic Time Series Data Analysis (First Job: Postdoc Researcher at UTS)

  • • Aneesh Chivukula, PhD student, Game theoretical adversarial deep learning algorithms (First Job: Assistant Professor at BITS, India)

  • • Yuansheng Liu, PhD student, Data mining for high performance compression of genomic sequences (First Job: Postdoc Researcher at UTS)

  • • Sunny Verma, PhD student, Feature extraction by data fusion (First Job: Postdoc Researcher at UTS)

  • • Shoujin Wang, PhD student, Learning Complex Relations for Session-based Recommendations (First Job: Research Fellow at Macquarie University)

  • • Quan Do, PhD student, Tensor-based data fusion analysis (First Job: Data Scientist at The ICONIC)

  • • Ali Braytee, PhD student, Robust classification of high dimensional unbalanced single and multi-label datasets (First Job: Postdoc at UTS)

  • • Ling Luo, PhD student, Causal inference for spatio-temporal data (First Job: Postdoc at CSIRO's Data61)

  • • Xinxin Jiang, PhD student, on context-aware recommendation systems (First Job: Research Assistant at UTS)

  • • Victor Chu, PhD student, causal structure discovery from road traffic data (First Job: Senior Research Engineer at NTU, Singapore)

  • • Fei Wang, PhD student, on game theory for machine learning (First Job: Data Scientist at the CMCRC)

  • • Goce Ristanoski, PhD student, on time series regression (First Job: Postdoc at CSIRO's Data61)

  • • Mohammad Mazraehshahi, Master student, on soft-cut decision trees

  • • DongHawn Lee, Master student, on lazy learning methods

  • • Jinheng Liu, Master student, ensembles for outlier detection

  • • Emma Wang, Master student, feature creation for Bagging

  • • Chun Yuan Lee, Summer intern student, Causality Models for Understanding Road Traffic Changes

  • • Amogh Sarda, Summer intern student, Incident impact analysis by earning historical road traffic data

  • • Han Zhou, Summer intern student, Mining social networks for early detection of events

  • • Zeyang Yu, Summer intern student, on Twitter-based road incident detection.

  • • Mingxuan Li, Summer intern student (winner of NICTA summer scholar prize), on mining dynamic road traffics.

Wei's Academic Ancestry


我的学术祖普:

Source: www.genealogy.ams.org