Researcher Profile

Dr Maoying Qiao

Faculty of Law and Business, Peter Faber Business School

Areas of expertise: machine learning; computer vision; uncertainty quantification; object detection.

HDR Supervisor accreditation status: Provisional

ORCID ID: 0000-0002-0990-5506

Phone: +61426240513

Email: maoying.qiao@acu.edu.au

Location: ACU North Sydney Campus

Maoying Qiao received the BEng degree in information science and engineering from Central South University, Changsha, China, in 2009, and the MEng degree in computer science from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, in 2012, and the PhD degree in software engineer from University of Technology Sydney, Australia, in 2016. Her current research interests include diversity in machine learning and computer vision, uncertainty quantification, and exploring deep learning based modern object detection techniques to automate manual operations in industrials to make real impact. She is also dedicated to publishing her research outputs at the highest international standard, both in top journals and conferences in the fields of machine learning and computer vision, including refereed journals such as TCYB, PR, TKDE and refereed conferences such as AAAI, IJCAI, ICDE, CVPR.

Visit Website: https://webapps.acu.edu.au/staffdirectory/index.php?maoying-qiao

Select publications:

• Maoying Qiao, Dadong Wang, Geoff Tuck, L. Richard Little, Andre E. Punt, and Mike Gerner. Deep learning methods applield to electronic monitoring data: Automated catch event detection for longline fishing. ICES Journal of Marine Science, 78(1), pp.25-35, 2021.
• Dadong Wang, Yulia Arzhaeva, Liton Devnath, Maoying Qiao, Saeed Amirgholipour, Qiyu Liao, Rhiannon McBean, James Hillhouse, Suhuai Luo, David Meredith, Katrina Newbigin, Deborah Yates. Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs. Digital Image Computing: Techniques and Applications (DICTA), 2020
• Maoying Qiao, Jun Yu, Wei Bian, Qiang Li, and Dacheng Tao. Adapting Stochastic Block Model to Power-Law Degree Distributions. IEEE Transactions on Cybernetics, vol. 49, pp. 626-637, 2019. (ERA: A, IF: 8.803)
• Maoying Qiao, Liu Liu, Jun Yu, Chang Xu, and Dacheng Tao. Diversified Dictionaries for Multiple Instance Learning. Pattern Recognition, vol. 64, pp. 407-416, 2017. (ERA: A*)
• Maoying Qiao, Richard Yida Xu, Wei Bian, and Dacheng Tao. Fast Sampling for Time-Varying Determinantal Point Processes. ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 1, 2015. (Flagship Journal for Data Mining)
• Maoying Qiao, Wei Bian, Richard Yida Xu, and Dacheng Tao. Diversified Hidden Markov Models for Sequential Labeling. IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 27, no. 11, 2015. (ERA: A)
• Maoying Qiao, Jun Cheng, Wei Bian, and Dacheng Tao. Biview Learning for Human Posture Segmentation from 3D Points Clouds. PLoS ONE. vol. 9, no. 1, 2014. (ERA: A)
• Jun Cheng, Maoying Qiao, Wei Bian, and Dacheng Tao. 3D Human Posture Segmentation by Spectral Clustering with Surface Normal Constraint. Signal Processing, vol. 91, no. 9, pp. 2204-2212, 2011. (IF: 3.110)
• M. Rizwan Khokher, L. Richard Little, Geoffrey N. Tuck, Daniel V. Smith, Maoying Qiao, Carlie Devine, Helen O'Neill, John Pogonoski, Rhys Arangio and Dadong Wang. Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing. Canadian Journal of Fisheries and Aquatic Sciences. (accepted)

Projects:

• Diverse hard negative mining for imbalanced learning and its application for object detection and pneumonia detection (sole-CI, ACU Research Awards for Women Academic Staff, AUD$12.5K, awarded in 2020.
• Uncertainty-aware object detection via Bayesian nonparametric deep learning (co-CI), UTS Early Career Research Capability Development Initiative, AUD$34K, awarded in 2019.
• Cross-modal inference for image aesthetic quality assessment and enhancement (co-CI), Natural Science Foundation of China (Grant no. 6197011337), RMB$720K (AUD$144K), awarded in 2019.
• Diversity modeling based on Determinantal point processes (sole-CI), Natural Science Foundation of China (Grant no. 61702145), RMB$280K (AUD$53K), awarded in 2017.

Accolades and awards:

• UTS FEIT Best Publication Award, 2015
• Merit Student in Graduate School of Chinese Academy of Sciences, 2010
• Outstanding Graduate of CSU (top 10\% in Computer Science), 2009
• Outstanding Graduate of Hunan Province - (top 3\% in Computer Science at CSU), 2009

Appointments and affiliations:

• Lecturer, 2020.10 -- Now Peter Fabar Business School, Australian Catholic University.
• Postdoctoral Research Fellow, 2018.10 -- 2020. 10, Oceans and Atmosphere & Data61, CSIRO, Australia, Project: Fisheries management based on deep video analytic.
• Casual Research Engineer, 2018.03 -- 2018.10, Data61, CSIRO, Australia, Project: Yield estimation in viticulture based on deep learning techniques.
• Associate Professor, 2016.12 -- 2018.10, School of Computer Science, Hangzhou Dianzi University, China, Research: Determinantal point processes based diversity learning.

International Journal review panel:

• IEEE Transactions on Neural Network and Learning Systems (TNNLS)
• IEEE Transactions on Image Processing (TIP)
• Pattern Recognition (PR)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• IEEE Transactions on Cybernetics (TCYB)
• Computational Statistics and Data Analysis (CSDA)
• Neural Processing Letters (NPL)
• Neural Computing