
Wenjie Zhang is a Professor, ARC Future Fellow, Head of the Data and Knowledge Research Group (https://unswdb.github.io/) and Deputy Head of School (Research) in the School of Computer Science and Engineering, the University of New South Wales, Australia. Her research interests include spatial-temporal data analysis, uncertain data analysis and graph data processing. Since 2008, she has published more than 160 papers in top venues such as TKDE, TODS, VLDBJ, SIGMOD, VLDB, and ICDE. She received the Australian Research Council Future Fellowship (FT3) in 2021 and Discovery Early Career Researcher Award in 2011. In 2019, she received the Chris Wallace Award for her significant contributions to large-scale graph data processing. Her research has been well supported by 12 Australian Research Council grants and several industry funded projects. Wenjie is an Associate Editor for IEEE Transactions on Knowledge and Data Engineering and The VLDB Journal. The full list of her publications (and presentation slides) : http://www.cse.unsw.edu.au/~zhangw/
Mutiple PhD scholarships (living allowance around $35k-$40k/year plus tuition fee waive) available. Send me an email (wenjie.zhang@unsw.edu.au) with your CV if you would like to pursue PhD degree in the area of large-scale dynamic data analytics and processing.
Current undergraduate students or master by course students in UNSW are encourged to contact me if you are interested in research degrees (Master by research or PhD) or research projects (honors thesis project or master research project).
- 2022 Google Inclusion Research Award
- 2022 Distinguished Associate Editor Award from VLDB 2022.
- 2022 Equity, Diversity and Inclusion Award, Faculty of Engineering UNSW.
- 2022 Best Student Paper Award in the 33rd Australasian Database Conference (ADC 2022).
- 2021 Australian Research Council Future Fellowship (FT3).
- 2021 Academic Excellence Award (for both Educational Excellence and Innovation and Research Excellence), Faculty of Engineering UNSW.
- 2021 Best Demonstration Award nomination in CIKM 2021.
- 2021 ACM SIGMOD Research Highlight Award: The SIGMOD Research Highlight Award showcases research projects that exemplify core database research. In particular, “the project must address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact.”
- 2020 ACM SIGMOD One of the Best Papers: The extended version of the paper was invited to publish in the special issue of "Best of SIGMOD" in ACM Transactions on Database Systems (TODS). A concise version was invited to publish in the ACM SIGMOD Record.
- 2020 Best Paper Award in the 25th International Conference on Database Systems for Advanced Applications (DASFAA 2020).
- 2020 Best Student Paper Award in The 13th International Conference on Knowledge Science, Engineering and Management (KSEM 2020).
- 2019 CORE Chris Wallace Award for Outstanding Research Contribution in the field of Computer Science (for my research contribution in Large-scale Graph Data Processing): The annual prize is awarded to at most one academic for post-PhD research undertaken within a university or research institution in Australia or New Zealand. The research should include a notable breakthrough or a contribution of particular significance.
- 2019 One of the Best Papers in the 24th International Conference on Database Systems for Advanced Applications (DASFAA 2019).
- 2013 One of the Best Papers in the 29th IEEE International Conference on Data Engineering (ICDE 2013). The extended version of the paper was invited to publish in the speical issue of "Best of ICDE" in IEEE Transactions on Knowledge and Data Engineering.
- 2013 Best Paper Award in the 14th International Conference on Web Information Systems Engineering WISE 2013.
- 2013 CiSRA Research Publication Award, CSE UNSW.
- 2012 One of the Best Papers in the 28th IEEE International Conference on Data Engineering (ICDE 2012). The extended version of the paper was invited to publish in the speical issue of "Best of ICDE" in IEEE Transactions on Knowledge and Data Engineering.
- 2012 One of the Best Papers in the 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012).
- 2012 Best Student Paper Award in the 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012).
- 2012 Australian Research Council Early Career Researcher Award (DECRA).
- 2012 CiSRA Research Publication Award, CSE UNSW.
- 2010 One of the Best Papers in the 26th IEEE International Conference on Data Engineering (ICDE 2010). The extended version of the paper was invited to publish in the speical issue of "Best of ICDE" in IEEE Transactions on Knowledge and Data Engineering.
- 2010 Best Paper Award in the 21st Australasian Database Conference (ADC 2010).
- 2010 CiSRA Research Publication Award, CSE UNSW.
- 2009 Best Paper Award in the Joint International Conference APWeb/WAIM 2009.
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My Research Supervision
Multiple PhD positions available with full support of tuition fee and living stipend. Please send me a copy of your CV if you are interested in doing PhD with me. Currently undergraduate and master by course students of UNSW are welcome to discuss with me if you are interested in research degrees of either PhD or Master by Research / Master of Philosophy.
Some topics include:
Graph Data Processing: graph is a powerful model to captures objects as well as the relationships / connections among them, and is a commonly used tool in many different areas including world wide web, social networks, biology networks, protein interaction networks, traffic networks, etc. My research in this area focuses on supporting structure based queries over large scale graphs and cover various graph types including multi-dimensional graphs, attributed graphs, bipartite graphs, and heterogeneous information network.
Spatial Data Processing: the widely available positioning devices foster a rich amount of multi-dimensional spatial data. My research in this area focuses on spatial keyword search and spatial-social data processing.
AI / Machine Learning Techniques for Large-scale Data Analytics and Processing: to apply machine learning techniques to improve efficiency and effectiveness in managing large-scale data.