Benjamin RubinsteinBenjamin Rubinstein
Senior Lecturer (equiv US Assistant Professor)
Dept. Computing & Information Systems
The University of Melbourne, Australia 

Fellow, Centre for Business Analytics, Melbourne Business School [link]
Associate Investigator, ARC Centre of Excellence for Mathematical & Statistical Frontiers [link]

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Office 7.21 Doug McDonell Building

Juliet & Ella   Liam   Lachlan

Interests: Statistical Machine Learning, Security & Privacy, Databases, Industry Engagement

Bio: Ben 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, Ben shipped production systems for entity resolution in Bing and the Xbox360 (driving huge success accounting for revenues in the $100m's); and his research has helped identify and plug side-channel attacks against the popular Firefox browser. He actively researches topics in machine learning, security, privacy, and databases. Ben 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.



Other News

  • 05/2016: Invited speaker at the National Fintech Cyber Security Summit at the Ivy, Sydney hosted by Data61, Stone & Chalk, the Chief Scientist of Australia.
  • 04/2016: Speaking at Telstra (data science)
  • 02/2016: Speaking at Samsung Research America and UC Berkeley.
  • 02/2016: Speaking in two exciting panels at AAAI'2016 on keeping AI beneficial and challenges for AI in cyber operations.
  • 12/2015: Plenary at the 12th Engineering Mathematics and Applications Conference (EMAC'2015) the biennial meeting of the EMG special interest group of ANZIAM
  • 07/2015: Keynote at the Australian Academy of Science Elizabeth and Frederick White Research Conference on Mining Data for Detection and Prediction of Failure in Geomaterials [link]
  • 12/2014: Excellence in Research Award 2014, Dept CIS, University of Melbourne
  • 11/2014: Facebook (Menlo Park) talk Data Integration through the Lens of Statistical Learning

Research Group

  • Postdocs
    • Yi Han (2016 - ). Adversarial machine learning.
  • PhD students
    • Lingjuan Lyu (w Marimuthu Palaniswami 2016 - ). Privacy in distributed sensing.
    • Neil Marchant (w Aurore Delaigle 2016 - ). Adaptive sampling.
    • Yuan Li (w Trevor Cohn 2015 - ). Bayesian optimisation, NLP.
    • Xunyun Liu (w Raj Buyya, Rodrigo Calheiros 2015 - ). Stream computing.
    • Safiollah Heidari (w Raj Buyya 2015 - ). ML for distributed computing.
    • Yamuna Kankanige (w James Bailey 2015 - ). Liver transplant outcomes, Austin Health
    • Zay Aye - CompSci (w Rao Kotagiri 2014 - ). Learning distance metrics.
    • Maryam Fanaeepour - CompSci (w Lars Kulik, Egemen Tanin 2014 - ). Location data privacy.
    • Jiazhen He - CompSci (w James Bailey, Rui Zhang 2014 - ). Education analytics and MOOCs.
    • Zuhe Zhang - Maths & Stats (w Sanming Zhou 2014 - ). Differential privacy in Bayesian statistics.
  • Masters students
    • Current: Samuel Jenkins, Xianjing Fan
    • Completed: Rui Hu, Justin Liang, Nouras Fatima, Zhe Lim (2014); Ben Schroeter, Soundarya Mallemarapu (2015)
  • Interns from Microsoft Research




  • Tansu Alpcan, Benjamin I. P. Rubinstein, and Christopher Leckie, Large-Scale Strategic Games and Adversarial Machine Learning, in Proceedings of the 55th IEEE Conference on Decision and Control (CDC'2016), accepted July 2016.
  • Zuhe Zhang, Benjamin I. P. Rubinstein, and Christos Dimitrakakis, On the Differential Privacy of Bayesian Inference, in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'2016), pp. 2365-2371, February 2016. [arXiv]
  • Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, and Jeffrey Chan, MOOCs Meet Measurement Theory: A Topic-Modelling Approachin Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'2016), pp. 1195-1201, February 2016. [arXiv]
  • Zay Maung Maung Aye, Kotagiri Ramamohanaro, and Benjamin I. P. Rubinstein, Large Scale Metric Learning, in 2016 International Joint Conference on Neural Networks (IJCNN), IEEE Press, accepted to appear July  2016.
  • Ivan Sanchez, Zay Maung Maung Aye, Benjamin I. P. Rubinstein, and Kotagiri Ramamohanaro, Fast Trajectory Clustering using Hashing Methods, in 2016 International Joint Conference on Neural Networks (IJCNN), IEEE Press, accepted to appear July 2016.
  • Sandra Milligan, Jiazhen He, James Bailey, Rui Zhang, and Benjamin I. P. Rubinstein, Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums, in Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK), pp. 546-547, April 2016.
  • Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. Doug Tygar, book Adversarial Machine Learning: Computer Security and Statistical Machine Learning, Cambridge University Press, accepted to appear
  • Francesco Aldà and Benjamin I. P. Rubinstein, The Bernstein Mechanism: Function Release under Differential Privacy, paper in submission.
  • Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein, Differential Privacy in a Bayesian Setting through Posterior Sampling, paper in submission.









Pre 2007