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

Email: [firstname].[lastname]@unimelb.edu.au
Office: Room 7.21 Doug McDonell Building

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

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Juliet & Ella   Liam   Lachlan

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

Bio: Ben joined the University of Melbourne in 2013 as a Senior Lecturer in Computing and Information Systems (a RAMAP appointee). Previously he gained four years of industry experience in the research divisions of Microsoft, Google, Intel and Yahoo! (all in the US); 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); his research has helped identify and plug side-channel attacks against the popular Firefox browser, and deanonymise an unprecedented Australian Medicare data release, prompting introduction of the Re-identification Offence Bill 2016. He actively researches topics in machine learning, security & privacy, databases such as adversarial learning, differential privacy and record linkage respectively. His work has been recognised through an Australian Research Council DECRA award, and a Young Tall Poppy Science award. 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.

Service

Funding

Other News

Research Group

  • Postdocs
    • Shen Wang (2016 - ). Probabilistic programming, databases, record linkage.
    • 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: Zean Qin
    • Completed: Rui Hu, Justin Liang, Nouras Fatima, Zhe Lim (2014); Ben Schroeter, Soundarya Mallemarapu (2015); Samuel Jenkins, Xianjing Fan, Si Chen (2016)
  • Interns from Microsoft Research

Teaching

I co-designed several exciting programs offered by my department (within Engineering): the Master of Business Analytics (joint with the Melbourne Business School), the Bachelor of Science major in Data Science and the Master of Data Science (both joint with Maths & Stats within Science). These programs reflect the interdisciplinary nature of machine learning and its allied areas.

Publications

2017

  • Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein, Differential Privacy in a Bayesian Setting through Posterior Sampling, Journal of Machine Learning Research, 8(Feb), accepted Feb 2017 (report from initial version 2013 extends ALT'14)
  • Francesco Aldà and Benjamin I. P. Rubinstein, The Bernstein Mechanism: Function Release under Differential Privacy, in Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'2017), accepted Nov 2016 to appear Feb 2017. [full version]
  • 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, in press
  • Maryam Fanaeepour and Benjamin I. P. Rubinstein, End-to-End Differentially-Private Parameter Tuning in Spatial Histograms, arXiv:1702.05607 [cs.DB], Feb 2017.

2016

2015

2014

2012

2011 

2010

2009

2008

2007

Pre 2007