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.
- 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
- 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. Proceedings of the International Conference on Parallel Processing. CRC Press. 2017. DOI: 10.1109/ICPP.2017.66
- 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
- AldÀ F, Rubinstein B. The bernstein mechanism: Function release under differential privacy. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017.
- Han Y, Alpcan T, Chan J, Leckie C, Rubinstein B. A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. IEEE - Institute of Electrical and Electronic Engineers. 2016, Vol. 11, Issue 3. DOI: 10.1109/TIFS.2015.2505680
- Fanaeepour M, Rubinstein B. Beyond Points and Paths: Counting Private Bodies. 16th IEEE International Conference on Data Mining (ICDM). IEEE. 2016. Editors: Bonchi F, Domingoferrer J, Baezayates R, Zhou ZH, Wu X. DOI: 10.1109/ICDM.2016.44
- Sanchez I, Aye Z, Rubinstein B, Kotagiri R. Fast Trajectory Clustering using Hashing Methods. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). IEEE. 2016, Vol. 2016-October. DOI: 10.1109/IJCNN.2016.7727674
- Aye Z, Kotagiri R, Rubinstein B. Large Scale Metric learning. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). IEEE. 2016, Vol. 2016-October. DOI: 10.1109/IJCNN.2016.7727368
- Alpcan T, Rubinstein B, Leckie C. Large-Scale Strategic Games and Adversarial Machine Learning. 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC). Institute of Electrical and Electronics Engineers. 2016. DOI: 10.1109/CDC.2016.7798940
- He J, Rubinstein B, Bailey J, Zhang R, Milligan S, Chan J. MOOCs meet measurement theory: A topic-modelling approach. 30th AAAI Conference on Artificial Intelligence (AAAI). 2016.
- Zhang Z, Rubinstein B, Dimitrakakis C. On the differential privacy of Bayesian inference. 30th AAAI Conference on Artificial Intelligence (AAAI). 2016.
- Milligan S, He J, Bailey J, Zhang R, Rubinstein B. Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums. 6th International Conference on Learning Analytics and Knowledge (LAK). Association for Computing Machinery Inc.. 2016, Vol. 25-29-April-2016. DOI: 10.1145/2883851.2883956
- Rubinstein J, Rubinstein B, Bartlett PL. Bounding embeddings of VC classes into maximum classes. Measures of Complexity: Festschrift for Alexey Chervonenkis. 2015. DOI: 10.1007/978-3-319-21852-6_21
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile