Doctor of Philosophy (Queensland University of Technology)
Research theme: Information Technology
Research discipline: Computer Science
Dr. Jinglan Zhang is a senior lecturer in Queensland University of Technology. She received her PhD in Information Technology in 2003 from Queensland University of Technology. Dr. Jinglan Zhang's broad research area falls in Artificial Intelligence and Information Systems. In particular, her research interests include Visual and Acoustic Information (Graphics, Images, and Sound) Processing and Retrieval, Big data analysis and visualization, Computer Human Interaction, eScience, software engineerig, and mobile and web applications. She has published more than 100 refereed papers and jointly received over $1M in research funding. She has successfully supervised (as principal and associate supervisor) 10 PhD students and 2 Masters by Research students to completion. Dr. Zhang has worked as an Engineer in Computer Aided Design and Computer Aided Engineering (CAD/CAE) for 8 years and a researcher in Information Technology for more than 15 years. Dr. Zhang is an accredited supervisor for PhD students. She is also a member of QUT's college of mentoring supervisors. Scroll down to see her introduction in Chinese (张静兰 简介)
From 1982 to 1990, she studied in the Department of Manufacture Engineering, Beijing University of Aeronautics and Astronautics (BUAA) (now renamed as Beihang University after its Chinese pronunciation), Beijing, China. She received a Bachelor of Engineering in 1987 and Master of Engineering in 1990, majoring in the area of computer aided design. From 1998 to 1999, she studied a Master of Computing by coursework in the Department of Computing at Macquarie University, Australia. From 2000 to 2003, she studied as a PhD student under the supervision of Prof. Binh Pham and A/Prof. Yi-Ping Phoebe Chen in the Science and Engineering Faculty, Queensland University of Technology. She received her PhD on 9th April, 2003.
From 1990 to 1998, she worked as an engineer in Beijing Central Engineering and Research Incorporation of Iron and Steel Industry, Beijing, China (CERIS). Dr Jinglan Zhang has worked as an associate lecturer (July, 2002 -July 2004), a lecturer (August 2004 – July 2008), and senior lecturer (August 2008 – present) in Queensland University of Technology (QUT).
Dr Jinglan Zhang’s broad research area falls in artificial intelligence, in particular:
- visual and acoustic information (graphics, images and sound) processing and retrieval
- big data analysis using machine learning and visualisation
- computer human interaction, including information access for people with disability and older adults.
- software engineering
- mobile and web applications
- computer graphics (in networked, mobile and collaborative environment)
- user modelling/profiling
Her research applications are in environmental monitoring with acoustic sensors, health, and the web.
Dr Zhang has published more than 100 refereed papers and jointly received over $1M in research funding. She has successfully supervised (as principal and associate supervisor) 10 PhD students and 2 Masters by Research students to completion. Dr Zhang is an accredited supervisor for PhD students. She is also a member of QUT’s college of mentoring supervisors. Dr Zhang has worked as an Engineer in Computer Aided Design and Computer Aided Engineering (CAD/CAE) for 8 years and a researcher in Information Technology for more than 17 years.
Dr Jinglan Zhang’s introduction in Chinese
张静兰 学历简介 张静兰 1982 年入读北航机械工程系 。 在校期间学习成绩优异并积极参与和领导各项班级活动， 年年被评为校三好学生 并在1984年被评为北京市三好学生。本科毕业后，在温文彪教授指导下就读硕士研究生，主攻计算机辅助设计（CAD）。1990年顺利硕士毕业。 1998 年，在取得了8年的工程设计与计算经验以后，静兰决定到海外继续学习深造。在进修了几门计算机科学课程以后， 她收到了澳大利亚昆士兰科技大学奖学金资助，在Binh Pham教授和Yi-Ping Phoebe Chen博士指导下， 主要研究人工智能在CAD当中的应用。她刻苦钻研三年后发表多篇科技论文并顺利取得博士学位。 业务经历简介 1990年硕士毕业后静兰来到冶金部北京钢铁设计研究总院工作，担任计算机辅助设计与工程（CAD/CAE）工程师8年， 曾经参与多项国内外大型钢铁厂（上海宝钢以及泰国和印度的多家钢铁厂）的工程设计工作。静兰所在小组是该院工程计算明星， 经常接待各级部委领导视察和外国专家及国内同行来访。静兰也曾经被该院派往美国接受工程软件培训。在该院工作期间，静兰还与同事合作开发小型专业软件并发表了两篇关于钢铁厂建模与计算的学术论文，不但得到单位的好评与奖励，还为以后到海外深造奠定了基础。 2003年静兰在澳大利亚昆士兰科技大学（QUT）以优异成绩完成博士学习之后直接留校任教至今。现任高级讲师，主要从事教学，科研和学术交流领域的工作等等。张静兰是澳大利亚符合资质的博士生导师。她的研究领域主要包括人工智能与计算机软件在环境监测与网络智能方面的应用。 具体技术包括信息可视化，多媒体计算如文字，图形，图像，生物声音处理与检索，移动计算，模糊逻辑，进化计算，数据挖掘，语义网等等。到目前为止，静兰已经与同事一起成功从澳洲政府和工业界包括微软公司收到了近一百万澳元的科研经费， 发表了100余篇科技论文，成功指导完成了两名博士生和一名研究型硕士生。静兰目前是3名博士生的正导师和另外3名博士生的副导师。静兰最引以为豪的是她教授过的本科毕业生和研究生遍布全世界。 静兰是IC3K 国际会议程序委员会成员， 负责审核会议论文。她是IEEE 和 AAAI会员。她也是多个国际会议组织委员会成员。 她与同事一起成功组织了多个大型国际会议包括IEEE e-Science2010， 为本领域的学术交流做出贡献。 静兰为伟大的祖国，中国，感到骄傲。她也为能够作为一个华人在澳大利亚的知名高等学府里执教感到自豪。她充分发挥有中国成长背景和熟谙中英双语的优势，积极推进两国学术界交流与合作。 她积极地穿针引线，促成QUT与中国西北农林科技大学和太原理工大学签订了双边国际合作协议。她也促成了QUT学者与中国科学院和中国林学院同行共同申请中澳合作研究基金。静兰已经邀请并接待了多名中国大学教授到QUT访问，交流与合作。她已经指导了多名中国国家留学基金委资助的博士研究生。
Service and professional activities Within QUT: Co-leader, Environment and Natural Systems, QUT Centre for Data Science (2020 - present) Student Advisor (Computer Science) (2018 - present) Course coordinator, Bachelor of Information Technology (Honours) (2010-2015) Workplace health and safety officer/representative/warden (2007 - present) Outside QUT: International journal editor
- Mobile Information Systems (2015 – 2020)
International journal reviewer
- Computer-aided design (Elsevier) (2005 – 2006)
- Journal of Research and Practice in Information Technology (2002)
Conference program committee
- Knowledge Engineering and Ontology Development (KEOD) (2012-2017)
- International Conferences on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2013 – 2017)
- International Multi-Media Modelling Conference (MMM) (2004 - 2006)
International conference organizing committee
- Served as the secretary of the 2nd International Symposium on Autonomous Minirobots for Research and Edutainment (AMIRE 2003) and helped the local chair to successfully organize this conference.
- Served as a member of the Local Organizing Committee of MMM2004.
- Served as a member of the steering committee of South East Queensland Chapter of ACM SIGGRAPH.
- Served as a member of the Local Organizing Committee of eScience2010
- Human Data Interaction (Big Data Visual Analytics): We will research on integration of the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. This research will use the datasets that Queensland government and the QUT ecoAcoustic research group have collected over multiple years. Other big datasets, such as Amazon’s product review dataset, could also be used. Research question: What are good ways to support human interaction with big data?
- Data for environment and nature: Sensor networks bring ecologists and pattern recognition researchers together to make some applications possible. These applications include assessing risks from potential bird collisions, un-obtrusive observations (where the presence of humans changes some animal behaviours), and the studies of spatial and temporal variation in biological processes. While huge amount of data has been collected, processing and mining those data is challenging. One approach to this is to apply existing high performance computing, data management and analysis solutions to specific scientific challenges and solve the new problems that will arise. A systematic investigation on the ways for effective data analysis and pattern recognition will be conducted. This project will investigate algorithms for animal species recognition using machine learning and image processing techniques. We also investigate semi-automated analysis and citizen science approaches. The QUT ecoAcoustic research group is one of the leading groups in the world in this area. We are looking for more research students in this area. We need multiple students, and each student can work on a specific aspect of the project e.g. computer-human interaction, information retrieval, machine learning, signal processing and pattern recognition etc. Research question: How to utilize information technology to aid ecological research?
- Designing technologies for preventing homelessness: This project is about preventing homelessness for women before it begins. This aim is to intervene prior to the crisis occurring, that is, we aim to intervene when housing stress is occurring or likely to occur (nb. housing stress is defined here as meaning having difficulty maintaining safe and appropriate housing). According to the health continuum of prevention, three stages of harm prevention exist: primary (prevent the harm from occurring), secondary (attempt to stop existing harm early), and tertiary (attempt to reduce the impact of established harm). While there are some excellent resources available, these resources tend to focus on women in the secondary or tertiary points of intervention (at the ‘tipping point’ or already in crisis, respectively). Other resources such as helplines or access to emergency housing are also targeted to women already experiencing a crisis. We aim to help women at the primary stage. This project focuses on designing and developing a digital solution that will enable early responders (that is, a professional source of support who is not directly associated with homeless services – like a real estate agent or a lawyer) to have conversations with pre-crisis (primary prevention-stage) women to help them access the support needed. The digital solution could be anything: – it could be a web interface, an app, a chatbot, etc. Research Question: How might we empower women experiencing a change of circumstance and prevent homelessness?
- Information accessibility for people with disability: The web has become the primary mechanism for information delivery. However, for people with intellectual disabilities there can be significant barriers in accessing this type of information. This project aims to address the web usability issue by developing new technological solutions that allow people with intellectual disabilities to independently seek information. The research involves collaborative investigation between QUT researchers, a disability service provider and people with intellectual disabilities to understand their aspirations and current practices in accessing the web. We will bring together technical and client-centred knowledge to develop innovative information access techniques, tested in a web app, mobile app or conventional PC app or IoT solution. We need multiple students, and each student can work on a specific aspect of the project e.g. computer-human interaction, information retrieval, user-centred and participatory design, or inclusive web design. Research question: How can we make information accessible to people with diverse needs? older adults is another user group.
- Data for Good (analytics for humanity) is a general area I am interested in. Another project along this direction is physical activity analysis for the wellbeing of youth people.
- Information Retrieval (IR): Information storage and retrieval is the art and science of retrieving useful information from a collection of items that serves the user’s purpose. The main trick is to retrieve what is useful while leaving behind what is not. The more important trick is to push the most relevant and important information onto the top 10 in the results list (the first page in web search). We will focus on search engine technologies, including textual documents and audio/image documents. We will also research on the evaluation of the search technologies e.g. precision/recall metrics, human and machine based evaluation etc. Search efficiency (how fast) and effectiveness (how well) are the main goals.
- Gan, H., Zhang, J., Towsey, M., Truskinger, A., Stark, D., Van Rensburg, B., Li, Y. & Roe, P. (2021). A novel frog chorusing recognition method with acoustic indices and machine learning. Future Generation Computer Systems, 125, 485–495. https://eprints.qut.edu.au/212075
- Oleiwi, S., Al-Shamma, O., Zhang, J., Alzubaidi, L. & Fadhel, M. (2020). DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools and Applications, 79(21-22), 15655–15677. https://eprints.qut.edu.au/131069
- Dema, T., Brereton, M., Cappadonna, J., Roe, P., Truskinger, A. & Zhang, J. (2017). Collaborative exploration and sensemaking of big environmental sound data. Computer Supported Cooperative Work, 26(4 - 6), 693–731. https://eprints.qut.edu.au/113943
- Xie, J., Towsey, M., Truskinger, A., Eichinski, P., Zhang, J. & Roe, P. (2015). Acoustic classification of Australian anurans using syllable features. Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015), 1–6. https://eprints.qut.edu.au/89673
- Towsey, M., Zhang, L., Cottman-Fields, M., Wimmer, J., Zhang, J. & Roe, P. (2014). Visualization of long-duration acoustic recordings of the environment. Procedia Computer Science, 29, 703–712. https://eprints.qut.edu.au/74420
- Zhang, J., Huang, K., Cottman-Fields, M., Truskinger, A., Roe, P., Duan, S., Dong, X., Towsey, M. & Wimmer, J. (2013). Managing and analysing big audio data for environmental monitoring. Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering, 997–1004. https://eprints.qut.edu.au/69096
- Yang, H., Zhang, J. & Roe, P. (2011). Using reputation management in participatory sensing for data classification. Procedia Computer Science, 5, 190–197. https://eprints.qut.edu.au/42443
- Li, Z., Liu, Y., Walker, R., Hayward, R. & Zhang, J. (2010). Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved hough transform. Machine Vision and Applications, 21(5), 677–686. https://eprints.qut.edu.au/29121
- Mason, R., Roe, P., Towsey, M., Zhang, J., Gibson, J. & Gage, S. (2008). Towards an acoustic environmental observatory. Proceedings of the IEEE Fourth International Conference on eScience, 2008, 135–142. https://eprints.qut.edu.au/29068
- Cai, J., Ee, M., Pham, B., Roe, P. & Zhang, J. (2007). Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition. Proceedings of 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 293–298. https://eprints.qut.edu.au/11227
- New Information Access Technologies for People with Intellectual Disability
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- CAT 1 - Australian Competitive Grant
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- Framing authentic assessment of service learning within an information technology curriculum
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- CAT 1 - Australian Competitive Grant
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- Assessment And Feedback Practices; Information Technology; Personal Development; Service Learning
- Sensor-Based Prediction of Physical Activity and its Impacts Using Machine Learning (2018)
- Acoustic Classification of Australian Frogs for Ecosystem Survey (2017)
- Classification and Ranking of Environmental Recordings to Facilitate Efficient Bird Surveys (2017)
- Aspect-Based Opinion Mining from Customer Reviews (2016)
- Content-based Birdcall Retrieval from Environmental Audio (2016)
- Automated Species Recognition in Environmental Recordings (2014)
- Aerial Image Analysis Using Spiking Neural Networks with Application to Power Line Corridor Monitoring (2011)
- Ontology Based Image Annotation (2010)