Dr. Qian Li 李倩

Her research interests include:

• Causal Reasoning for Machine Learning

• Topological Data Analysis

• Riemannian Optimization

• Optimal Transport

• Computer Vision and Data Science

Office: Building 314.347,  Kent St, Bentley WA 6102            

E-mail:  qli@curtin.edu.au

aNews: 

• [29-Oct 2023] Our paper titled "Achieving counterfactual fairness with imperfect structural causal model" has been accepted by Journal of Expert Systems With Applications (Impact factor 8.5).

• [3-Oct 2023] Our paper titled “Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation”  has accepted by ACM Transactions on Recommender Systems.

• [1-Oct 2023] Our paper titled “Counterfactual Explainable Conversational Recommendation” has been accepted by Transactions on Knowledge and Data Engineering (TKDE) (CORE A*).

• [5-Aug 2023] Our paper titled “Causality-guided Graph Learning for Session-based Recommendation”  has accepted by The Conference on Information and Knowledge Management (CIKM, CORE A)

• [23-Feb 2023] Two papers titled "Toward Explainable Recommendation Via Counterfactual Reasoning" and "CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows" are accepted by the conference Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2023, CORE A), Congratulations to my PhD students Haiyang and Duong.

• [31-Jan 2023 ] Our paper titled "Deconfounded Recommendation via Causal Intervention" is accepted by Journal Neurocomputing  (JCR-Q1, IF: 4.072), 2023.  Congratulations to my PhD students Dianer and Xiangmeng. 

• [25-Jan 2023 ] Our paper titled "Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning" is accepted by The Web Conference (CORE A*).

• [19- Oct 2022] Our paper titled "Explainable Hyperbolic Temporal Point Process for User-Item Interaction Sequence Generation" is accepted by IEEE Transactions on Information Systems (CORE A*).

• [1 - Sep 2022] Our paper titled "Task-level Relations Modelling for Graph Meta-learning" is accepted by ICDM 2022 (CORE A*) .

• [16-June 2022] Our paper titled "Privileged Multi-Task Learning for Attribute-Aware Aesthetic Assessment" is accepted by top journal Pattern Recognition (CORE A*) Congratulations to Yangyang!

• [16-May 2022] I was invited to be the speaker for the PAKDD (CORE A) tutorial on Recent Advances on Causality-based Recommender System http://pakdd.net/tutorials.html, the video and slides will be online soon!

• [26-April 2022] My research paper titled "Be Causal: De-biasing Social Network Confounding in Recommendation" is accepted by top Journal ACM Transaction Knowledge Discovery from Data (CORE A*).

• [1-April 2022] Our research paper titled "MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations" is accepted by top conference SIGIR2022 (CORE A*), Congratulations to my PhD students!

• [7-Mar 2022] Our research paper titled "Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation" is accepted by top journal IEEE Transactions on Knowledge and Data Engineering (TKDE, CORE A*), Congratulations to my PhD students Xiangmeng and Dianer!

• [18-Jan 2022] Our research paper titled "Semantics-Guided Disentangled Learning for Recommendation" is accepted by the conference Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022, CORE A), Congratulations to my PhD student Dianer!

• [14-Jan 2022] Our research paper titled "Off-policy learning over heterogeneous information for recommendation" is accepted by top conference The Web Conference 2022 (CORE A*), Congratulations to my PhD students Xiangmeng and Dianer!

• [27-Dec 2021] Our research paper titled "Stochastic Intervention for Causal Inference via Reinforcement Learning" is accepted by top journal Neurocomputing (JCR-Q1), Congratulations to my PhD student Duong!

• [4-Dec 2021] Our research paper titled "Deep Treatment-Adaptive Network for Causal Inference" is accepted by top journal VLDB (CORE A*). 

• [2-Nov 2021] Our research paper titled "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: a Semantic Evidence View" is accepted by AAAI2022 (CORE A*).

• [1-Oct 2021] Our research paper titled "Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment" is accepted by top journal IEEE Transactions on Multimedia (CORE A*), Congratulations to Yangyang! 

• [27-September 2021] Our research paper titled "Causal Optimal Transport for Treatment Effect Estimation" is accepted by top journal IEEE Transactions on Neural Networks and Learning Systems (CORE A*).

• [17 August 2021] I was invited as a program committee for AAAI2022.

• [9 August 2021] Our research paper on causal reasoning is accepted by the top conference - CIKM 2021 (CORE A).

• [15 July 2021] Our research paper titled "Stochastic Intervention for Causal Effect Estimation" is accepted by IJCNN 2021 (CORE A), Congratulations to my PhD student Duong.

• [9 March 2021] Our research paper titled "Hilbert Sinkhorn Divergence for Optimal Transport" is accepted by the top conference - CVPR 2021 (CORE A*).

• [7 Feb 2021] Our paper titled "V-SVR+: Support Vector Regression with Variational Privileged Information" is accepted by top journal IEEE Transactions on Multimedia, Congratulations to Yangyang!

Selected Publications （Full List）: 

1. Dianer Yu, Qian Li*, Hongzhi Yin, Guandong Xu*, Causality-guided Graph Learning for Session-based Recommendation, The 32th ACM International Conference on Information and Knowledge Management (CIKM'23) (Ranking: CORE A).

2. Dianer Yu, Qian Li*, Xiangmeng Wang, Qing Li, Guandong Xu*, Counterfactual Explainable Conversational Recommendation, IEEE Transactions on Knowledge and Data Engineering (TKDE) (Ranking: CORE A*).

3. Xiangmeng Wang, Qian Li*, Dianer Yu, Qing Li, Guandong Xu*,Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware Recommendation, ACM Transactions on Recommender Systems (Ranking: CORE A*).

4. Tri Dung Duong, Qian Li*, Guandong Xu, Achieving counterfactual fairness with imperfect structural causal model, Journal of Expert Systems With Applications (Impact factor 8.5).

5. Dianer Yu, Qian Li* , Xiangmeng Wang, and Guandong Xu*, Deconfounded Recommendation via Causal Intervention, Neurocomputing, 2023 (JCR-Q1, IF: 4.072)

6. Y. Shu, Qian Li*, S. Liu, G. Xu, Privileged multi-task learning for attribute-aware aesthetic assessment . Pattern Recognition (CORE A*).

7. Qian Li*, Xiangmeng Wang, Zhichao Wang, Guandong Xu*. Be Causal: De-biasing Social Network Confounding in Recommendation, ACM Transactions on Knowledge Discovery from Data (CORE A*, TKDD), https://dl.acm.org/doi/abs/10.1145/3533725.

8. Xiangmeng Wang, Qian Li*, Dianer Yu, Zhichao Wang, Guandong Xu*. MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations, The SIGIR2022 (CORE A*, Corresponding author and contributing equally with the first author), Accepted.

9. Xiangmeng Wang, Qian Li*, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu*. Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation, IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE, CORE A*, Corresponding author and contributing equally with the first author), Accepted. 

10. Dianer Yu, Qian Li* , Xiangmeng Wang, Zhichao Wang, Yanan Cao, and Guandong Xu*. Semantics-Guided Disentangled Learning for Recommendation,  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022, CORE A, Corresponding author), Accepted.

11. Xiangmeng Wang, Qian Li*, Dianer Yu, Guandong Xu*. Off-policy learning over heterogeneous information for recommendation, The Web Conference 2022 (CORE A*, Corresponding author and contributing equally with the first author), Accepted.

12. TD.Duong, Qian Li, Guandong Xu. Stochastic Intervention for Causal Inference via Reinforcement Learning, Neurocomputing, 2022 (JCR-Q1), Accepted.

13. Qian Li, Zhichao Wang, S. Liu, G. Li, G. Xu, Deep Treatment-Adaptive Network for Causal Inference, The International Journal on Very Large Data Bases (VLDB), 2021 (CORE A*), Accepted.

14.  Y. Shu, Qian Li, S. Liu, G. Xu, Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment, IEEE Transactions on Multimedia, 2021 (JCR-Q1, IF: 5.452, CORE A*), Accepted

15. Li, R., Cao, Y., Zhu, Q., Bi, G., Fang, F., Liu, Y. and Li, Q., 2021. How Does Knowledge Graph Embedding Extrapolate to Unseen Data: a Semantic Evidence View. The Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022. (CORE A*)

16. Qian. Li, Zhichao Wang, Gang Li, Guandong Xu. Causal Optimal Transport for Treatment Effect Estimation. IEEE Transactions on Neural Networks and Learning Systems. 2021. (IF 10.45, CORE A*). Accepted.

17. Qian Li, TD.Duong, Z. Wang, S. Liu, D. Wang, G. Xu. Causal-Aware Generative Imputation for Automated Underwriting, The 30th ACM International Conference on Information and Knowledge Management (CIKM'21)  (Ranking: CORE A).

18. Qian Li, Z. Wang, G. Li, J. Pang, G. Xu. Hilbert Sinkhorn Divergence for Optimal Transport, IEEE Conference on Computer Vision and Pattern Recognition 2021 (CVPR’21) (Ranking: CORE A*).

19. Y. Shu, Qian Li, S. Liu, G. Xu, V-SVR+: Support Vector Regression with Variational Privileged Information, IEEE Transactions on Multimedia, 2021 (JCR-Q1, IF: 5.452, CORE A*)

20. TD. Duong, Qian Li, G. Xu. Stochastic Intervention for Causal Effect Estimation, The International Joint Conference on Neural Networks (IJCNN’21), Virtual, 2021, (Ranking: CORE A)

21. TD. Duong, Qian Li, G. Xu. Prototype-based Counterfactual Explanation for Causal Classification, arXiv preprint arXiv:2105.00703 (2021).

22. C. Sun, R. Wang, Qian Li and X. Hu, Reward Space Noise for Exploration in Deep Reinforcement Learning, International Journal of Pattern Recognition and Artificial Intelligence, 2021 (JCR-Q3, CORE B)

23. X. Wang, Qian Li, W. Zhang , G. Xu, S. Liu , W. Zhu. Joint Relational Dependency Learning for Sequential Recommendation, In: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’20), (Ranking: CORE A)

24. Y. Shu, Qian Li, S. Liu, G. Xu, Learning with Privileged Information for Photo Aesthetic Assessment, Neurocomputing, 2020 (JCR-Q1, IF: 4.072)

25. Li, X., Cao, Y., Qian Li, Shang, Y., Li, Y., Liu, Y., Xu, G. RLINK: Deep reinforcement learning for user identity linkage. World Wide Web, 2020, 1-19. (JCR-Q2, CORE A)

26. G Xu, TD Duong, Qian Li, S Liu, X Wang, Causality Learning: A New Perspective for Interpretable Machine Learning. The IEEE Intelligent Informatics Bulletin Vol.20 No.1 (2020)

27. J. Yin, Qian Li, S. Liu, G. Xu, Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection, Neurocomputing 2020 (JCR-Q1, IF: 4.072)

28. Qian Li, Z. Wang, G. Li, G. Xu. Polynomial Representation for Persistence Diagram, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, USA, 2019, pp. 6123-6132 (Ranking: CORE A*)

29. Z. Fang, Y. Cao, Qian Li, D. Zhang, Z. Zhang, Y. Liu: Joint Entity Linking with Deep Reinforcement Learning. International World Wide Web Conference(WWW’19), San Francisco, USA, 2019, pp. 438-447 (Ranking: CORE A*)

30. Qian Li, Z. Wang. Riemannian Submanifold Tracking on Low-Rank Algebraic Variety, Proceedings of 31st AAAI Conference on Artificial Intelligence (AAAI’17), San Francisco, USA, 2017, pp.2196-2202 (Ranking: CORE A*)

31. Qian Li, Z. Wang, G. Li, et al. Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold. Proceedings of the International Conference on Computational Science (ICCS’17), Zurich, Switzerland, 2017, pp.285-294 (Ranking: CORE A)

32. Qian Li, G. Li, W. Niu, et al. Boosting imbalanced data learning with Wiener process oversampling. Frontiers of Computer Science 11(5): 836-851 (2017) (JCR-Q4)

33. Qian Li, W. Niu, G. Li, et al. Riemannian optimization with subspace tracking for low-rank recovery. In: Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN’16), Vancouver, Canada, 2016, pp.3280-3287 (Ranking: CORE A)

34. Qian Li, W. Niu, G. Li, et al. Lingo: Linearized Grassmannian Optimization for Nuclear Norm Minimization. ACM International Conference on Information and Knowledge Management (CIKM’15), Australia, 2015, pp.801-809 (Ranking: CORE A)

35. Qian Li, W. Niu, G. Li, et al. Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks. Journal of Network and Systems Management 23(3): 474-501 (2015) ( JCR-Q4, CORE B)

Projects & Fundings:

Research on the frontier topic of trustworthy AI technology and topological data analysis, with numerous potential applications in smart healthcare, manufacturing and insurance.

1. Chief Investigator, The General Program of National Natural Science Foundation, China, 2020-2024. 

• Secured a $120k research grant from the General Program of National Natural Science Foundation (China).  Due to Covid-19, the grant rate decreases to 15.8%.

2. Data scientist, Department of Industry / Providence Asset Group, Australia, 2019-2023, 

• Worked as a data scientist in this project (jointly with UNSW) to applies AI technology to empower solar farms with advanced energy storage. 

• Supervised research students on tasks including electricity price forecasting and predicting solar power generation.

3. Chief Investigator,  National Natural Science Foundation of China. 2013-2017

• Worked as a Chief Investigator, to design context-aware sensor network architecture for healthcare monitoring. 

• Develop the fault detection and recovery mechanism for the sensor network architecture.  

Ph.D. Supervision:

Current Ph.D.:

1.  Xiangmeng Wang (Shanghai University Master), 2019-Current

2. Tri Dung Duong (Ho Chi Minh City University of Technology (HCMUT), Bachelor): 2020-Current

3. Dianer Yu (UTS Master), 2021-Current

4. Shaofei Ye (UQ Master), 2022-Current

5. Yaofang Liu, (Ocean University of China, Bachelor), 2021-Current

Graduates:

1. Yangyang Shu, Privileged Machine Learning for Prediction (PhD 2018.07-2021.07, Research Associate Adelaide University)

2. Jun Yin, Misinformation and misbehaviour detection (UTS PhD 2017.8 – 2021.11)

Teaching:  

• 2022-2023: Intelligent Agent (COM2009, Curtin University)

• 2021-2022: AI/Analytics Capstone Project (41004, Co-Lecturer, University of Technology Sydney) 

• 2019-2020: Modern Data Science (SIT742, Guest Lecturer, Deakin University)

• 2019: Recommender Systems and its Research Topics (Guest Lecturer,  UTS-Beijing Jiaotong University) 

• 2019: Algorithm Design and Analysis (Guest Lecturer, UTS-University of Chinese Academy of Sciences) 

• 2016: An Introduction to Machine Learning (Guest Lecturer,  University of Chinese Academy of Sciences) 

• 2011: Java Programming (Tutor, Shandong University)

• 2009: Data Structure & Algorithms (Tutor, Shandong University). 

Professional Services:

Reviewer:

1. SIGKDD'22: ACM SIGKDD Conference on Knowledge Discovery and Data Mining

2. ACM TKDD: The ACM Transactions on Knowledge Discovery from Data 

3. IEEE TKDE: IEEE Transactions on Knowledge and Data Engineering

4. IEEE TNNLS: IEEE Transactions on Neural Networks and Learning Systems

5. AAAI'22: Conference on Artificial Intelligence 

6. ICCV’21: International Conference on Computer Vision

7. CVPR’21: IEEE/CVF Conference on Computer Vision and Pattern Recognition

8. IJCAI’21: International Joint Conferences on Artificial Intelligence

9. ICLR’21: International Conference on Learning Representations 

10. ICML’20-21: International Conference on Machine Learning 

11. NeurIPS’20: Conference on Neural Information Processing Systems 

12. AAAI’20: Conference on Artificial Intelligence 

13. IEEE Transactions on Neural Networks and Learning Systems. 

14. PAKDD’19-21: Pacific-Asia Conference on Knowledge Discovery and Data Mining 

15. ICONIP’20: International Conference on Neural Information Processing 

16. KSEM’20: Conference on Knowledge Science, Engineering and Management

17. IEEE Transactions on Neural Networks and Learning Systems 

18. Journal of World Wide Web 

19. The Journal of Concurrency and Computation Practice and Experience

Talks:

1. Invited Talk at School of Mathematics and Statistics, University of New South Wales: “Discriminative Representation for Topological Data Analysis”, May, 2021, 

2. Invited Talk at School of Computer Information Systems, University of Melbourne: “Discriminative Representation for Topological Data Analysis”, Feb, 2021. 

3. Invited Tutorial Speaker: “Causal Inference for Recommendation Systems”, 2019 Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macao, China, May. 2019. 

4. Conference Presentation: CVPR2021, CVPR2019, AAAI2017, ICCS2017, IJCNN2016, CIKM2015, CSCWD2012.

Honor & Awards:

1. 2016 Chinese National Graduate Scholarship, Ministry of Education of China (Top 1%) 

2. 2015 Director Scholarship, Institute of Information and Engineering (Top 1%)

3. 2014 Merit Student, Chinese Academy of Sciences (Top 3%)

4. 2013 Excellent Student Scholarship (Top 2%)

5. 2010 Luxembourg Student Scholarship (Top 2%)

6. 2009 Outstanding Graduate of Shandong Province (Top 3%)

7. 2008 Chinese National Scholarship, Ministry of Education of China (Top 1%) 

8. 2005-2009 First-class Scholarship for four successive years (Top 3%)