I actively research topics in machine learning, security & privacy, databases such as adversarial learning, differential privacy and record linkage. Prior to joining the University of Melbourne in 2013, I enjoyed four years in the research divisions of Microsoft, Google, Intel and Yahoo! (all in the United States), followed by a short stint at IBM Research Australia. As a full-time Researcher at Microsoft Research, Silicon Valley, I shipped production systems for entity resolution in Bing and the Xbox360; my research has helped identify and plug side-channel attacks against the popular Firefox browser, and deanonymise Victorian myki transport data and an unprecedented Australian Medicare data release, prompting introduction of the Re-identification Offence Bill 2016. Since joining Melbourne in 2013, I have been awarded $4.68m in competitive funding ($2.26m as lead). My work has been recognised through an Australian Research Council DECRA award, and a Young Tall Poppy Science award.
Zhang, D., Rubinstein B.I.P., & Gemmell J.
(2015). Principled Graph Matching Algorithms for Integrating Multiple Data Sources. IEEE Transactions on Knowledge and Data Engineering. 27(10), 2784-2796. doi: 10.1109/TKDE.2015.2426714