Dr Debo Cheng

Research Fellow UniSA STEM

tel +61 8 830 20741

email Debo.Cheng@unisa.edu.au

Research Degree Supervisor

About me

Graphical Causal Modelling
Instrumental Variables
Latent Confounders
Counterfactual Inference
Discrimination Detection

Dr Debo Cheng is a postdoctoral researcher and a data scientist at the STEM, University of South Australia. He received a Ph.D. degree in Causal Inference from the University of South Australia in 2021. His research interests focus on Data Mining, Machine Learning, Causal Inference, and Causal Machine Learning.

Currently, his research falls in Artificial Intelligence, Machine Learning, and Data Analytics, focusing on developing data-driven causal inference techniques to support evidence-based decision-making and solve real-world problems. Dr Cheng has published over 40 papers in top-tier conferences and journals, including TKDE, TNNLS, ACM TIST, DMKD, IJCAI, AAAI, ICDM, ECAI, ECML-PKDD, etc. He has been invited... Read more

UniSA STEM

Harnessing connections across science, technology, engineering and mathematics, UniSA STEM creates innovative solutions and groundbreaking developments across teaching, learning and research.

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Research

Research focus

Artificial Intelligence and Image Processing
Information Systems
Cognitive Sciences
Computer Software
Data Format

Highlights

Data Mining
Machine Learning
Causal Inference
Causal Machine Learning

Research

Outputs

Research outputs for the last seven years are shown below. Some long-standing staff members may have older outputs included. To see earlier years visit ORCID, ResearcherID or Scopus

Open access indicates that an output is open access.

Highlights

Year	Output
2023	Cheng, D, Li, J, Liu, L, Yu, K, Le, TD & Liu, J 2023, 'Discovering ancestral instrumental variables for causal inference from observational data', IEEE Transactions on Neural Networks and Learning Systems, online, pp. 1-11. Open access
2023	Cheng, D, Li, J, Liu, L, Yu, K, Le, TD & Liu, J 2023, 'Toward unique and unbiased causal effect estimation from data with hidden variables', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6108-6120. 7 5 1
2023	Cheng, D, Li, J, Liu, L, Zhang, J, Liu , J & Le, TD 2023, 'Local search for efficient causal effect estimation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 8823-8837. Open access 2
2022	Cheng, D, Li, J, Liu, L, Zhang, J, Le, TD & Liu, J 2022, 'Ancestral instrument method for causal inference without complete knowledge', in Luc De Raedt (ed.), Thirty-First International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, pp. 4843-4849. Open access

Chapters

Year	Output
2023	Li, C, Cao, Y, Zhu, Y, Zhang, J, Annisa, A, Cheng, D, Tang, H, Jiang, S, Maruyama, K & Morimoto, Y 2023, 'An enhanced distributed algorithm for area skyline computation based on Apache Spark', in Z Jin et al. (eds), Knowledge Science, Engineering and Management : 16th International Conference, KSEM 2023, Guangzhou, China, August 16–18, 2023, Proceedings, Part IV, Springer Nature Switzerland AG,, Cham, Switzerland :, ch. 4, pp. 35-43.
2023	Luo, J, Zhang, Y, Wang, Y, Mayer, W, Ding, N, Li, X, Quan, Y, Cheng, D, Zhang, HY & Feng, Z 2023, 'A reinforcement learning-based approach for continuous knowledge graph construction', in Z Jin et al. (eds), Knowledge Science, Engineering and Management : 16th International Conference, KSEM 2023, Guangzhou, China, August 16–18, 2023, Proceedings, Part IV, Springer Nature Switzerland AG,, Cham, Switzerland :, ch. 34, pp. 418-429.
2023	Xu, T, Zhang, Y, Li, J, Liu, L, Xu, Z, Cheng, D & Feng, Z 2023, 'A data-driven approach to finding K for K nearest neighbor matching in average causal effect estimation', in F Zhang et al. (eds), Web Information Systems Engineering –WISE 202324th International Conference Melbourne, VIC, Australia, October 25–27, 2023 Proceedings, Springer, Singapore, ch. 55, pp. 723-732.
2023	Xu, Z, Liu, J, Cheng, D, Li, J, Liu, L & Wang, K 2023, 'Disentangled representation with causal constraints for counterfactual fairness', in H Kashima, T Ide & W Peng (eds), Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2023: Advances in Knowledge Discovery and Data Mining, Springer, Switzerland, pp. 471-482.
2023	Zhou, G, Kwashie, S, Zhang, Y, Bewong, M, Nofong, VM, Hu, J, Cheng, D, He, K, Liu, S & Feng, Z 2023, 'FastAGEDs: Fast approximate graph entity dependency discovery', in F Zhang et al. (eds), Web Information Systems Engineering –WISE 202324th International Conference Melbourne, VIC, Australia, October 25–27, 2023 Proceedings, Springer, Singapore, ch. 34, pp. 451-465. Open access
2016	Zhang, S, Li, Y, Cheng, D & Deng, Z 2016, 'Hypergraph spectral clustering via sample self-representation', Fuzzy Systems and Data Mining II, IOS Press, Netherlands, pp. 334-340.
2014	Cheng, D, Zhang, S, Zong, M, Sun, K & Liu, Y 2014, 'Efficient kNN algorithm based on graph sparse reconstruction', in X Luo, J Xu Yu & Z Li (eds), Advanced Data Mining and Applications, Springer, Switzerland, pp. 356-369. 22
2014	Yan, J, Cheng, D, Zong, M & Deng, Z 2014, 'Improved spectral clustering algorithm based on similarity measure', in X Luo, J Xu Yu & Z Li (eds), Advanced Data Mining and Applications, Springer, Germany, pp. 641-654. 12

Journal Articles

Year	Output
2024	Zhang, G, Cheng, D, Yuan, G & Zhang, S 2024, 'Learning fair representations via rebalancing graph structure', Information Processing and Management, vol. 61, no. 1, article no. 103570, pp. 1-15.
2023	Cheng, D, Li, J, Liu, L, Yu, K, Le, TD & Liu, J 2023, 'Discovering ancestral instrumental variables for causal inference from observational data', IEEE Transactions on Neural Networks and Learning Systems, online, pp. 1-11. Open access
2023	Cheng, D, Li, J, Liu, L, Yu, K, Le, TD & Liu, J 2023, 'Toward unique and unbiased causal effect estimation from data with hidden variables', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6108-6120. 7 5 1
2023	Cheng, D, Li, J, Liu, L, Zhang, J, Liu , J & Le, TD 2023, 'Local search for efficient causal effect estimation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 8823-8837. Open access 2
2022	Cheng, D, Li, J, Liu, L, Le, TD, Liu, J & Yu, K 2022, 'Sufficient dimension reduction for average causal effect estimation', Data Mining and Knowledge Discovery, vol. 36, no. 3, pp. 1174-1196. Open access 4 1 1
2020	Xu, S, Cheng, D, Skinner, W & Brito e Abreu, S 2020, 'Application of ToF-SIMS to predict contact angles of pyrite particles', Minerals Engineering, vol. 147, article no. 106168, pp. 1-10. Open access 10 10 2
2018	Deng, Z, Zhang, S, Yang, L, Zong, M & Cheng, D 2018, 'Sparse sample self-representation for subspace clustering', Neural Computing and Applications, vol. 29, no. 1, pp. 43-49. 6 4
2018	Zhang, S, Cheng, D, Deng, Z, Zong, M & Deng, X 2018, 'A novel kNN algorithm with data-driven k parameter computation', Pattern Recognition Letters, vol. 109, pp. 44-54. 122 75
2018	Zhang, S, Li, Y, Cheng, D, Deng, Z & Yang, L 2018, 'Efficient subspace clustering based on self-representation and grouping effect', Neural Computing and Applications, vol. 29, no. 1, pp. 51-59. 4 3
2017	Cheng, D, Zhang, S, Liu, X, Sun, K & Zong, M 2017, 'Feature selection by combining subspace learning with sparse representation', Multimedia Systems, vol. 23, no. 3, pp. 285-291. 17 9
2017	He, W, Zhu, X, Cheng, D, Hu, R & Zhang, S 2017, 'Low-rank unsupervised graph feature selection via feature self-representation', Multimedia Tools and Applications, vol. 76, no. 9, pp. 12149-12164. 4 4
2017	He, W, Zhu, X, Cheng, D, Hu, R & Zhang, S 2017, 'Unsupervised feature selection for visual classification via feature-representation property', Neurocomputing, vol. 236, pp. 5-13. 14 11
2017	Hu, R, Cao, J, Cheng, D, He, W, Zhu, Y, Xie, Q & Wen, G 2017, 'Self-representation dimensionality reduction for multi-model classification', Neurocomputing, vol. 253, pp. 154-161. 2 2
2017	Hu, R, Cheng, D, He, W, Wen, G, Zhu, Y, Zhang, J & Zhang, S 2017, 'Low-rank feature selection for multi-view regression', Multimedia Tools and Applications, vol. 76, no. 16, pp. 17479-17479. 8 5
2017	Hu, R, Zhu, X, Cheng, D, He, W, Yan, Y, Song, J & Zhang, S 2017, 'Graph self-representation method for unsupervised feature selection', Neurocomputing, vol. 220, pp. 130-137. 141 120
2017	Li, Y, Zhang, S, Cheng, D, He, W, Wen, G & Xie, Q 2017, 'Spectral clustering based on hypergraph and self-re-presentation', Multimedia Tools and Applications, vol. 76, no. 16, pp. 17559-17576. 10 7
2017	Zhang, S, Cheng, D, Hu, R & Deng, Z 2017, 'Supervised feature selection algorithm via discriminative ridge regression', World Wide Web, vol. 21, no. 6, pp. 1545–1562. Open access 12 5
2017	Zhang, S, Li, X, Zong, M, Zhu, X & Cheng, D 2017, 'Learning k for kNN Classification', ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3, article no. 43, pp. 1-19. Open access 413 257
2017	Zhang, S, Yang, L, Deng, Z, Cheng, D & Li, Y 2017, 'Leverage triple relational structures via low-rank feature reduction for multi-output regression', Multimedia Tools and Applications, vol. 76, no. 16, pp. 17461-17477. 4 1
2017	Zhu, Y, Zhang, X, Wen, G, He, W & Cheng, D 2017, 'Double sparse-representation feature selection algorithm for classification', Multimedia Tools and Applications, vol. 76, no. 16, pp. 17525-17539. 5 3
2016	Deng, Z, Zhu, X, Cheng, D, Zong, M & Zhang, S 2016, 'Efficient kNN classification algorithm for big data', Neurocomputing, vol. 195, pp. 143-148. 395 261
2016	Zhang, S, Cheng, D, Zong, M & Gao, L 2016, 'Self-representation nearest neighbor search for classification', Neurocomputing, vol. 195, pp. 137-142. 21 17
2016	Zhu, Y, Zhong, Z, Cao, W & Cheng, D 2016, 'Graph feature selection for dementia diagnosis', Neurocomputing, vol. 195, pp. 19-22. 10 7
2014	Cheng, D, Zhang, S, Deng, Z, Zhu, Y & Zong, M 2014, 'κNN algorithm with data-driven k value', Lecture Notes in Computer Science, vol. 8933, pp. 499-512. 68

Conferences

Year	Output
2023	Cheng, D, Xu, Z, Li, J, Liu, L, Le, TD & Liu, J 2023, 'Learning conditional instrumental variable representation for causal effect estimation', in D Koutra et al. (eds), European Conference, ECML PKDD 2023, Springer, pp. 525-540.
2023	Cheng, D, Xu, Z, Li, J, Liu, L, Liu, J & Le, DT 2023, 'Causal inference with conditional instruments using deep generative models', 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Association for the Advancement of Artificial Intelligence, vol. 37, pp. 7122-7130. Open access
2023	Xu, Z, Cheng, D, Li, J, Liu, J, Liu, L & Wang, K 2023, 'Disentangled representation for causal mediation analysis', 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Association for the Advancement of Artificial Intelligence, vol. 37, 10666-10674. Open access
2022	Cheng, D, Li, J, Liu, L, Zhang, J, Le, TD & Liu, J 2022, 'Ancestral instrument method for causal inference without complete knowledge', in Luc De Raedt (ed.), Thirty-First International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, pp. 4843-4849. Open access
2022	Wu, Z, Zhou, P, Wen, G, Wan, Y, Ma, J, Cheng, D & Zhu, X 2022, 'Information augmentation for few-shot node classification', 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, International Joint Conferences on Artificial Intelligence, pp. 3601-3607. Open access 1
2022	Xu, Z, Xu, Z, Liu, J, Cheng, D, Li, J, Liu, L & Wang, K 2022, 'Assessing classifier fairness with collider bias', Springer, pp. 262-276. Open access
2022	Zhao, H, Zhang, Y, Cheng, D, Li, C & Feng, Z 2022, 'Matching using sufficient dimension reduction for heterogeneity causal effect estimation', 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022, IEEE, pp. 9-16. Open access
2020	Cheng, D, Li, J, Liu, L, Liu, J, Yu, K & Le, TD 2020, 'Causal query in observational data with hidden variables', in G De Giacomo et al. (eds), 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020, IOS Press, pp. 2551-2558. Open access 7

Research

Patents

Details	Registry	Status
Embedded type attribute selection method based on subspace learning and application of embedded type attribute selection method based on subspace learning Cheng, Debo	China	Filed

External engagement & recognition

Collaborations

Highlights

Academic Editor: PLOS ONE since 2023.01

Academic Editor: Scientific Programming since 2021.09

Co-leading two special issues:

Special Issue at Mathematics, "Advances in Data Mining, Machine Learning and Causal Inference and Their Applications"
Special Issue at Pattern Recognition Letters, "Explainable Representation Learning for Multi-view/modal Data, (ERLMD)"

External engagement & recognition

Collaborations

Organisation	Country
Beijing University of Technology	CHINA
Charles Sturt University	AUSTRALIA
Charles Sturt University, Wagga Wagga	AUSTRALIA
China University of Mining and Technology	CHINA
Chinese Academy of Sciences	CHINA
Deakin University	AUSTRALIA
Geographic Information Center of Guangxi	CHINA
George Grant University of Mines and Technology	GHANA
Guangxi Normal University	CHINA
Guangxi Teacher Education University	CHINA
Guangxi University	CHINA
Heifei University of Technology	CHINA
Hiroshima University	JAPAN
Hong Kong Baptist University	HONG KONG
Huazhong Agricultural University	CHINA
Kyushu Institute of Technology	JAPAN
Massey University	NEW ZEALAND
Nagoya University	JAPAN
Nanjing University of Finance and Economics	CHINA
Shaanxi Normal University	CHINA
Simon Fraser University	CANADA
University of Electronic Science and Technology of China	CHINA
University of Queensland	AUSTRALIA
University of South Australia	AUSTRALIA
University of Trento	ITALY
Wuhan University	CHINA
Wuhan University of Technology	CHINA

External engagement & recognition

Engagement/recognition	Year
Invited Speaker 5th South Lake Forum for International Young Scientists, Huazhong Agricultural University	2020
10th China Youth Science and Technology Innovation Award University of Computer Science and Engineering	2016
Best Paper Award 10th International Conference on Advanced Data Mining and Applications	2016
Academic Star - 1st Rank in Top 10 Graduate Technology Festival, Guangxi Normal University	2015

Other

Regular Reviewer of Journals

JCR Q1

IEEE Transactions on Neural Networks and Learning Systems (TNNLS, if 14.255)
IEEE Transactions on Cybernetics (if 19.118)
IEEE Transactions on Knowledge and Data Engineering (TKDE, if 9.235)
IEEE-ACM Transactions on Computational Biology and Bioinformatics (if 4.5)
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS (if 2.9)
Pattern Recognition (PR, if 8.518)
Information Processing and Management (IPM, if 7.466)
Neural Networks (if 7.8)
International Journal of Medical Informatics (if 4.73)
Artificial Intelligence in Medicine (if 7.011)
ACM Transactions on Knowledge Discovery from Data (TKDD, if 4.157)
Wiley interdisciplinary reviews-Data Mining and Knowledge Discovery (if 7.558)
Frontiers in Public Health (if 6.461)
Frontiers in Genetics (if 4.772)
Journal of King Saud University-Computer and Information Sciences (if 8.839)
RELIABILITY ENGINEERING & SYSTEM SAFETY (if 8.1)

JCR Q2

World Wide Web Journal (WWWJ, if 2.638)
Neurocomputing (if 5.779)
Neural Computing and Applications (if 5.102)
Pattern Recognition Letters (if 4.757)
Applied Intelligence (if 5.019)
Multimedia Systems (if 2.603)
Multimedia Tools and Applications (if 2.577)
Journal of Visual Communication and Image Representation (if 2.887)
Mathematics (if 2.592)
Entropy (if 2.738)
Gene (if 3.913)
Algorithms (if 2.3)
Ecological Informatics (if 4.498)
IEEE Access (if 3.476)
BMC Medical Genomics (if 3.622)
Scientific Reports (if 4.996)
Interdisciplinary Sciences-Computational Life Sciences (if 3.492)

Program Committee member of leading AI/machine learning/data mining conferences

CORE Rank A*

ACM International Conference on Knowledge Discovery and Data Mining (KDD, Sub-reviewer) KDD2018, KDD2019, KDD2020
National Conference of the American Association for Artificial Intelligence (AAAI) AAAI2020 (Sub-reviewer), AAAI2021, AAAI2022 (top 15%), AAAI2023, AAAI2024
International Joint Conference on Artificial Intelligence (IJCAI) IJCAI2021, IJCAI2022, IJCAI2023
ACM Multimedia 2021
ICML2022, ICML2023
Neurips2022, Neurips 2023

CORE Rank A

UAI2022, UAI2023
SIAM Conference on Mathematics of Data Science (SDM, Sub-reviewer) SDM2019
ACM International Conference on Information and Knowledge Management (CIKM, Sub-reviewer) CIKM2019, CIKM2022, CIKM2023
European Conference on Artificial Intelligence (ECAI, Sub-reviewer) ECAI2020, ECAI2022
IEEE International Conference on Data Mining (ICDM) ICDM2021, ICDM2022 (Sub-reviewer)
PAKDD2023

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Dr Debo Cheng

About me

About me

UniSA STEM

About me

Professional Associations

Experience

Qualifications

Research

Research focus

Highlights

Projects

Outputs

Highlights

Chapters

Journal Articles

Conferences

Current research & highlights

Patents

External engagement & recognition

Collaborations

Highlights

Collaborations

External engagement & recognition

Invited Speaker

10th China Youth Science and Technology Innovation Award

Best Paper Award

Academic Star - 1st Rank in Top 10

Other

Gallery