Associate Professor Ben Rubinstein
- Big Data (Scalable Learning, Data Integration)
- Computer Security & Privacy (Cybersecurity, Data Privacy)
- Statistical Machine Learning
Dr. Rubinstein joined the University of Melbourne in 2013 as a R@MAP appointee, as a Senior Lecturer in Computing and Information Systems. Previously he gained four years of US industry experience in the research divisions of Microsoft, Google, Intel and Yahoo!; followed by a short stint at IBM Research Australia. As a full-time Researcher at Microsoft Research, Silicon Valley for close to 3 years, Rubinstein shipped production systems for entity resolution in Bing and the Xbox360 (driving huge success accounting for revenues in the $100m's); he actively researches topics in machine learning, security, privacy, and databases. Rubinstein earned the PhD in Computer Science from UC Berkeley under Peter Bartlett in 2010, collaborating closely with the SecML group, at the boundary of machine learning and security.
- Roohi L, Rubinstein B, Teague V. Differentially-Private Two-Party Egocentric Betweenness Centrality. IEEE Conference on Computer Communications. IEEE Computer Society. 2019, Vol. 2019-April. DOI: 10.1109/INFOCOM.2019.8737405
- Li Y, Rubinstein B, Cohn T. Truth inference at scale: A Bayesian model for adjudicating highly redundant crowd annotations. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. 2019. Editors: Liu L, White R. DOI: 10.1145/3308558.3313459
- Han Y, Rubinstein B. Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. The AAAI-18 Workshop on Artificial Intelligence for Cyber Security. The AAAI Press. 2018.
- Fanaeepour M, Rubinstein B. Differentially private counting of users' spatial regions. KNOWLEDGE AND INFORMATION SYSTEMS. Springer London. 2018, Vol. 54, Issue 1. DOI: 10.1007/s10115-017-1113-6
- Aye Z, Rubinstein B, Kotagiri R. Fast manifold landmarking using locality-sensitive hashing. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer Verlag. 2018, Vol. 10939 LNAI. DOI: 10.1007/978-3-319-93040-4_36
- Rubinstein B, Fanaeepour M. Histogramming privately ever after: Differentially-private data-dependent error bound optimisation. 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE. 2018. DOI: 10.1109/ICDE.2018.00111
- Lyu L, Nandakumar K, Rubinstein B, Jin J, Bedo J, Palaniswami M. PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. IEEE - Institute of Electrical and Electronic Engineers. 2018, Vol. 14, Issue 8. DOI: 10.1109/TII.2018.2803782
- Han Y, Rubinstein B, Abraham T, Alpcan T, De Vel O, Monazam Erfani S, Hubczenko D, Leckie C, Montague P. Reinforcement learning for autonomous defence in software-defined networking. 9th International Conference, GameSec 2018. Springer Verlag. 2018, Vol. 11199 LNCS. DOI: 10.1007/978-3-030-01554-1_9
- Fish B, Reyzin L, Rubinstein B. Sublinear-time adaptive data analysis. International Symposium on Artificial Intelligence and Mathematics, ISAIM 2018. 2018.
- Dimitrakakis C, Nelson B, Zhang Z, Mitrokotsa A, Rubinstein B. Differential Privacy for Bayesian Inference through Posterior Sampling. JOURNAL OF MACHINE LEARNING RESEARCH. Journal of Machine Learning Research. 2017, Vol. 18.
- Liu X, Harwood A, Karunasekera S, Rubinstein B, Buyya R. E-Storm: Replication-based State Management in Distributed Stream Processing Systems. 2017 46TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING (ICPP). CRC Press. 2017. DOI: 10.1109/ICPP.2017.66
- Verkade H, Lodge J, Elliott K, Mulhern T, Espinosa A, Cropper S, Rubinstein B. Exploring misconceptions as a trigger for enhancing student learning. 40th HERDSA Annual International Conference. 2017, Vol. 40. Editors: Walker R, Bedford S.
- Marchant N, Rubinstein B. In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling. PROCEEDINGS OF THE VLDB ENDOWMENT. VLDB Endowment. 2017, Vol. 10, Issue 11.
- Lau L, Kankanige Y, Rubinstein B, Jones R, Christophi C, Muralidharan V, Bailey J. Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation. TRANSPLANTATION. Lippincott Williams & Wilkins. 2017, Vol. 101, Issue 4. DOI: 10.1097/TP.0000000000001600
- Verkade H, Mulhern T, Lodge J, Elliott K, Cropper S, Rubinstein B, Horton A, Elliott C, Espinosa A, Dooley L, Frankland S, Mulder R, Livett M. Misconceptions as a trigger for enhancing student learning in higher education. . The University of Melbourne. 2017.
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