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

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

[firstname].i.p.[lastname]@gmail.com
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Office 7.21 Doug McDonell Building

Juliet & Ella   Liam   Lachlan

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

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 for close to 3 years, Ben 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. 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.

Feb 2016: I have open positions for 2 postdocs, one on adversarial machine learning/security, the other machine learning/systems 

Service

Funding

Other News

  • 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

Students

  • PhD students
    • 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 - ). Distance metric learning.
    • 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

Teaching

Publications

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), accepted.
  • 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), accepted.
  • 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
  • Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein, Differential Privacy in a Bayesian Setting through Posterior Sampling, journal paper in submission.

2015

2014

2012

2011 

2010

2009

2008

2007

Pre 2007