Dr Ben Rubinstein

  • Room: Level: 07 Room: 7.21
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Big Data (Scalable Learning, Data Integration)
  • Computer Security & Privacy (Cybersecurity, Data Privacy)
  • Statistical Machine Learning

Personal webpage



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.

Recent publications

  1. 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.
  2. 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.
  3. Fanaeepour M, Rubinstein B. Beyond Points and Paths: Counting Private Bodies. 2016 IEEE International Conference on Data Mining (ICDM). IEEE. 2016. Editors: Bonchi F, Domingo-Ferrer J, Baeza-Yates R, Zhou Z-H, Wu X.
  4. Sanchez I, Aye Z, Rubinstein B, Kotagiri R. Fast trajectory clustering using Hashing methods. 2016 International Joint Conference on Neural Networks (IJCNN). 2016, Vol. 2016-October.
  5. Aye Z, Kotagiri R, Rubinstein B. Large Scale Metric learning. 2016 International Joint Conference on Neural Networks (IJCNN). 2016, Vol. 2016-October.
  6. Alpcan T, Rubinstein B, Leckie C. Large-scale strategic games and adversarial machine learning. 2016 IEEE 55th Conference on Decision and Control (CDC). 2016.
  7. 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.
  8. Zhang Z, Rubinstein B, Dimitrakakis C. On the Differential Privacy of Bayesian Inference. 30th AAAI Conference on Artificial Intelligence (AAAI). Association for the Advancement of Artificial Intelligence. 2016.
  9. 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.
  10. Rubinstein J, Rubinstein B, Bartlett PL. Bounding embeddings of VC classes into maximum classes. Measures of Complexity: Festschrift for Alexey Chervonenkis. 2015.
  11. He J, Baileyt J, Rubinstein B, Zhang R. Identifying at-risk students in massive open online courses. Proceedings of the National Conference on Artificial Intelligence. 2015, Vol. 3.
  12. Zhang D, Rubinstein B, Gemmell J. Principled Graph Matching Algorithms for Integrating Multiple Data Sources. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. IEEE Computer Society. 2015, Vol. 27, Issue 10.
  13. Lim Z, Rubinstein B. Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015. Editors: Bonet B, Koenig S.
  14. Fanaeepour M, Kulik L, Tanin E, Rubinstein B. The CASE histogram: privacy-aware processing of trajectory data using aggregates. GEOINFORMATICA. Kluwer Academic Publishers. 2015, Vol. 19, Issue 4.
  15. Dimitrakakis C, Nelson B, Mitrokotsa A, Rubinstein B. Robust and Private Bayesian Inference. 25th International Conference on Algorithmic Learning Theory (ALT). Springer Verlag. 2014, Vol. 8776. Editors: Auer P, Clark A, Zeugmann T, Zilles S.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile