Dr. Zhichao Wang
Dr. Zhichao Wang
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Zhichao Wang was a Research Fellow at School of Electrical Engineering and Telecommunications at University of New South Wales (ranked at 19 QS World University Ranking-2024), under supervision of Prof. Victor Solo between 2019-2021. Before that he got the Phd degree from Tsinghua University, department of automation in 2018, China.
Now he is working at the largest general insurance company Insurance Australia Group (IAG) as Data Scientist, Australia, and he is passionate about achieving valuable data outcomes and producing actionable insights.
His papers have been published in the top-tier conferences and journals in the field of machine learning, signal processing and control science, such as CDC, ICASSP, CVPR, AAAI, CIKM, Journal of Process Control, IEEE-TCSVT, IEEE- VLDB, IEEE-TKDE, ACM-TKDD, IEEE-TNNLS etc.
Research interests:
Machine Learning
Autonomous Robotics
Adaptive Signal Processing
Riemannian Optimisation
Stochastic Systems on Riemannian Geometry
E-mail: zchaoking@gmail.com
News:
🌟[4-June-2024] Our research paper titled "HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network" is online by top journal IEEE Transactions on Neural Networks and Learning Systems (TNNLS, CORE A*),
[2 Oct 2023 ]Our research paper titled "Be causal: De-biasing social network confounding in recommendation" is accepted by top journal ACM Transactions on Knowledge Discovery from Data (TKDD, CORE A*), Congratulations to Xiangmeng and Dianer!
I was invited as program committee for NeurIPS2023, ICML2023
[2 March 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 Xiangmeng and Dianer!
I was invited as program committee for CVPR2022, ICML2022
[27 September 2021]One research paper titled "Causal Optimal Transport for Treatment Effect Estimation." is accepted by top journal IEEE Transactions on Neural Networks and Learning Systems (TNNLS, CORE A*).
[5 September 2021]I was invited as a program committee for ICLR2022.
[17 August 2021] I was invited as a program committee for AAAI2022.
[9 August 2021] One research paper on causal reasoning is accepted by the top conference - CIKM 2021 (CORE A).
[20 July 2021] I was invited as a reviewer for Journal of Knowledge based systems.
[15 July 2021] One research paper titled "Convergence of a Tangent Space Numerical Scheme for a Stochastic Differential Equation on a Sphere " is accepted by IEEE CDC 2021.
[9 March 2021] One research paper titled "Hilbert Sinkhorn Divergence for Optimal Transport" is accepted by the top conference - CVPR 2021 (CORE A*).
Selected learned courses:
Selected learned courses:
Mathematical analysis, Functional and Real analysis, Nonlinear programing, Stochastic differential equations, Machine learning, Complex analysis. Riemannian geometry, Abstract algebra, Algebraic topology, Algebraic curve and surface
Selected Teaching:
Selected Teaching:
ICTE4001 Autonomous Robotics. 2025 Semester 2
COMP3010 Machine Learning. 2025, Semester 1.
Networking Foundations. 2024. Spring
Signal Processing. 2019, Semester 1.
Autonomous Control System. 2020, Semester 1.
Research
Optimal Transport for Computer Vision. Algorithms and asymptotic analysis for optimal transport and Fokker–Planck equation on non-Euclidean space such as Hilbert space and matrix manifolds. Apply them to computer vision, deep learning, causal inference and topological data analysis
Qian Li, Zhichao Wang, , Xia, H., Li, G., Cao, Y., Yao, L., & Xu, G.. HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network. IEEE Transactions on Neural Networks and Learning Systems. 2024. (IF 10.45, CORE A*)
Qian Li, Zhichao Wang, G. Li, J. Pang, G. Xu. Hilbert Sinkhorn Divergence for Optimal Transport, IEEE Conference on Computer Vision and Pattern Recognition 2021 (CVPR21). pp. 3835-3844 (Ranking: CORE A*).
Qian. Li, Zhichao Wang, Gang Li, Guandong Xu. Causal Optimal Transport for Treatment Effect Estimation. IEEE Transactions on Neural Networks and Learning Systems. (IF 10.45, CORE A*). 2021. Volume: 34 Issue: 8
Zhichao Wang, Q. Li, G. Xu, G. Li. Polynomial Representation for Persistence Diagram, IEEE Conference on Computer Vision and Pattern Recognition (CVPR19), Long Beach, USA, 2019, pp. 6123-6132. (Ranking: CORE A*).
Causal inference for Explainable AI. Causal inference refers to the process of drawing conclusions about causal relationships between variables or events based on observed data. Causal inference plays a crucial role in fields such as epidemiology, economics, social sciences, and public health.
Li, Qian, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. "Be causal: De-biasing social network confounding in recommendation." ACM Transactions on Knowledge Discovery from Data (IF 4.0, CORE A*), no. 1 (2023): 1-23.
Qian Li, Z. Wang, S. Liu, G. Li and G. Xu. Deep Treatment-Adaptive Network for Causal Inference, The International Journal on Very Large Data Bases (VLDB), CORE A*. The VLDB Journal. 31, no. 5 (2022): 1127-1142.
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*. IF 8.9) 2022 Mar
Qian Li, TD.Duong, Zhichao Wang, S. Liu, D. Wang, G. Xu. Causal-Aware Generative Imputation for Automated Underwriting, The 30th ACM International Conference on Information and Knowledge Management (CIKM21) (Ranking: CORE A). 2021. p. 3916–3924.
Riemannian Optimisation for Machine Learning. Algorithms and convergence analysis for optimization problems on matrix manifolds such as low rank manifold/stiefel manifold/Grassmanian manifold. Apply them to real world applications such as semiconductor lithography process and image recovery of combustion process
Zhichao Wang, M. Liu, M. Dong, L. Wu: Riemannian Alternative Matrix Completion for Image-Based Flame Recognition. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT). 27(11): 2490-2503 (2017)
Q. Li, Zhichao Wang. Riemannian Submanifold Tracking on Low-Rank Algebraic Variety, In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), San Francisco, USA, 2017, pp.2196-2202
Zhichao Wang, M. Liu, M. Dong, L. Wu: Low-rank manifold optimization for overlay variations in lithography process. Journal of Process Control. 62(2): 11–23 (2018)
Q. Li, Zhichao Wang, et al. Low-Rank Approximation for Crowdsourcing on Riemannian Manifold. In: Proceedings of the International Conference on Computational Science (ICCS’17), Zu ̈rich, Switzerland, 2017, pp.285-294
H. Zhang, Zhichao Wang, L Cao. Fast Nystrom for Low Rank Matrix Approximation. In: Proceedings of the 8th International Conference on Advanced Data Mining and Applications (ADMA’12), Nanjing, China, pp.456-464
Stochastic Differential Geometry for Signal Processing and Control. Numerical algorithms and convergence analysis for stochastic differential equations (SDEs) and particle filtering on sphere/Lie group/Stiefel manifold.
Zhichao Wang, V.Solo. Convergence Analysis for Lie Group Particle Filtering. Submitted to IEEE Transactions on Automatic Control (IEEE-TAC). 2021. 16 pages.
Zhichao Wang, V.Solo. Spherical State Estimation via Tangent Space Parametrization. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. Submitted.
V. Solo, Zhichao Wang. Convergence of a Tangent Space Numerical Scheme for a Stochastic Differential Equation on a Sphere. 2021 IEEE 58th Conference on Decision and Control (CDC21). pp. 5814-5819.
Zhichao Wang, V. Solo. Numerical Solution of Stochastic Differential Equations in Stiefel Manifolds via Tangent Space Parametrization. 2021 IEEEInternational Conference on Acoustics, Speech and Signal Processing (ICASSP21). pp.5125-5129
Zhichao Wang, V. Solo. Lie Group State Estimation via Optimal Transport. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP20). pp.5625-5629
Zhichao Wang, V. Solo.Particle Filtering on the Stiefel Manifold with Optimal Transport. 2020 IEEE 58th Conference on Decision and Control (CDC 20), pp. 4111- 4116.
V. Solo, Zhichao Wang. Numerical Method for Stochastic Differential Equation in Stiefel Manifold via Cayley Transform. 2019 IEEE 58th Conference on Decision and Control (CDC19), pp.3303-3038.
PhD Supervision:
Haiyang Xia, Australian National University, 2021-current
Dianer Yu. University of Technology Sydney, 2021-current
Professional Services:
Professional Services:
CVPR'22:IEEE/CVF Conference on Computer Vision and Pattern Recognition
ICCV’21: International Conference on Computer Vision
CVPR’21: IEEE/CVF Conference on Computer Vision and Pattern Recognition
IJCAI’21: International Joint Conferences on Artificial Intelligence
ICLR’21-22: International Conference on Learning Representations
ICML’20-21: International Conference on Machine Learning
NeurIPS’20: Conference on Neural Information Processing Systems
AAAI’20-22: Conference on Artificial Intelligence
PAKDD’19-21: Pacific-Asia Conference on Knowledge Discovery and Data Mining
KSEM’20: Conference on Knowledge Science, Engineering and Management
IEEE Transactions on Neural Networks and Learning Systems
Journal of Knowledge based system
Talks:
Talks:
Invited Talk at the School of Information Technology at Deakin University: “Discriminative Representation for Topological Data Analysis”, Feb, 2021
Invited Talk at Zhejiang Lab (online): “Wasserstein Geometry for non-Euclidean Space”, Feb, 2021.
Invited Talk at Yau Mathematical Sciences Center at Tsinghua University: “Computational algebraic geometry”, June, 2018.
Conference Presentation: CDC2019/2020, CVPR2019/2021, ICASSP2020/2021, AAAI2017
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