About Me
My name’s Bing Wang. I am currently working as a research associate in MDO group School of Engineering and Information Technology, University of New South Wales, Canberra. My research interests are evolutionary optimization, data mining and agent system. My current focus is on designing algorithms for solving expensive bilevel optimization problems. I am interested in incorporating machine learning techniques especially knowledge transfer into optimization process. My past research experiences include agent system, social network analysis, medical image process and natural language processing. I am fortunate to have worked with many interesting and inspiring scholars in these projects, and the experiences have positioned me to be able to see research questions from a wide range of directions.
Experience
UNSW Canberra
Associate Lecturer
October 2019 - Present
https://research.unsw.edu.au/people/dr-bing-wang
Everything is optimization
Expensive problems refer to optimization problems wherein evaluation of each solution can incur significant cost, either in the form of computation time or financial investment. For such problems, it is important to control the number of evaluations while searching for optimal solutions. In this work, we look into techniques like surrogate modelling and knowledge transfer to assist search in controlling cost.
Chinese Academy of Sciences
Postdoc, Associate professor
May 2015 - October 2019
Understand human
Social media has become a big part of people’s day to day life. How to analyse the dynamic of social media open a series of interesting research questions. We ustilize tools like graph analysis, natual language process to study natural community of social network, influence of KOLs, and Sentiment of users responding to events.
UNSW Canberra
Ph.D project
August 2011 - March 2014
Make sense of world from the eyes of agent
Without any prior knowledge, how could an artifical agent work out causal relations among its sensing data? In this project, we rely on association rule mining and neural netowrk to design a inference mechanism for artifical agent, so that it can learn to build a causal knowledge network from its observation.
UNSW Sydney
Ph.D project
September 2009 - July 2011
Brain is interesting
Brain is mysterious. MRI techniques open a window for us to peek it. In this project, we use hidden Markov model to build an brain age estimator for assisting doctors diagose mild cognitive impairment. Later development of this technqiue was purchased and adopted in a commercial hospital systems.
Education
UNSW Canberra
Doctor of Philosophy (Ph.D), Computer Science
2009 - 2014
Thesis title
- Autonomous hypothesis generation for knowledge discovery in continuous domains
Supervisor
- Prof. Hussein A. Abbass
- Prof. Kathryn E. Kasmarik
Ocean University of China
Master of Engineering, Control theory and control engineering
2006 - 2009
Thesis title
- Video compression algorithm for underwater acoustic channel transmission
Supervisor
- Prof. Qingzhong Li
Ocean University of China
Bachelor of Engineering, Automation
2002 - 2006
UG project
- Software development for mobile robot motion control based on intelligent PID
Supervisor
- Prof. Qingzhong Li
Fundings
I have secured a list of fundings from both industry and academic funding agencies
- Self forming research group, School of Engineering and Information Technology, UNSW Canberra 2021 (Collaborative)
- Institute of acoustics, Chinese academy of Sciences, Young Elite Research Project. 2018 (PI)
- Longleding technology, Brain imaging analysis software. 2017 (PI)
- Foundation of Science and technology on Information Assurance Laboratory, KJ-17-102. 2017 (PI)
- Chinese Academy of Sciences intenational Postdoctoral exchange fellowship program. 2016 (PI)
- China Postdoctoral science foundation grant 2015LH0041. 2015 (PI)
Services
- Supervision, I have involved in HDR supervision of 6 students who are graduated. I am currently supervising UG project: data driven expensive optimization methods
- IEEE Computational intelligence society, ACT chapter. Executive commitee 2021 - present
- UNSW Canberra, yound women in engineering program, robot team. 2019-2022
Selected publication
- B. Wang, H. Singh, T. Ray. Investigating Neighborhood Solution Transfer Schemes for Bilevel Optimization, WCCI 2022
- B. Wang, Hemant K. Singh, T. Ray. Adjusting normalization bounds to improve hypervolume based search for expensive multi-objective optimization. Complex Intell. Syst. (2021)
- B. Wang, Singh HK, Ray T. Investigating Normalization Bounds for Hypervolume-Based Infill Criterion for Expensive Multiobjective Optimization. In Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021
- B. Wang, Singh, H.K. and Ray, T., Bridging Kriging believer and expected improvement using bump hunting for expensive black-box optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021.
- B. Wang, Singh, H.K. and Ray, T., Comparing expected improvement and Kriging believer for expensive bilevel optimization, in Proceedings of IEEE Congress on Evolutionary Computation, CEC 2021.
- Manshu Tu, Bing Wang. Adding Prior Knowledge in Hierarchical Attention Neural Network for Cross Domain Sentiment Classification. IEEE Access. vol.7, 2019.
- Junqing He, Bing Wang, Mingming Fu, Tianqi Yang, Xuemin Zhao. Hierarchical Attention and Knowledge Matching Networks With Information Enhancement for End-to-End Task-Oriented Dialog Systems. IEEE Access. vol. 7. 2019.
- Manshu Tu, Bing Wang, Xunmin Zhao. Chinese Dialogue Intention Classification Based on Multi-Model Ensemble. IEEE Access. vol. 7. 2018.
- Taisong Li, Bing Wang, Yasong Jiang, Yan Zhang, Yonghong Yan. Restricted Boltzmann Machine-Based Approaches for Link Prediction in Dynamic Networks. IEEE Access. vol. 6. 2018.
- B. Wang, K. Merrick, H. Abbass. Cooperative Co-Evolutionary Neural Networks for Mining Functional Association Rules. IEEE transactions on neural networks and learning system. 2016
- B. Wang, K. Merrick, H. Abbass. Autonomous Hypothesis Generation as an Environment Learning Mechanism for Agent Design. Artificial Life and Computational Intelligence Lecture Notes in Computer Sciences. vol. 8955, pp. 210-225, 2015.
- B. Wang, T. Pham. MRI-based age prediction using hidden Markov models. Journal of neuroscience methods. vol. 199, no.1, 2011.
- T. Pham, F. Salvetti, B. Wang, and others. The hidden-Markov brain: comparison and inference of white matter hyperintensities on magnetic resonance imaging (MRI). Journal of Neural Engineering. vol. 8, no. 1, 2011.