A/Prof Ben Rubinstein

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

00m'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.

Find out more about Ben Rubinstein's experience

Ben Rubinstein's highlights

FEATURED Journal article

Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 with technological interventions.

Ben Rubinstein's selected work

LEX34201 - DISTRIBUTED AUTONOMOUS SPECTRUM MANAGEMENT (DUST)

Research Contracts

When tools for a health emergency become tools of oppression

NEWS ARTICLE

Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 through technolo..

Journal article

ACADEMIC CENTRES OF CYBER SECURITY EXCELLENCE (ACCSE) PROGRAM

Research Contracts

Two data points enough to spot you in open transport records

NEWS ARTICLE

Early identification of an impending rockslide location via a spatially-aided Gaussian mix..

Journal article

Projects

Displaying the 10 most recent projects by Ben Rubinstein.

Towards designing complex networks resilient to stealthy attack and cascading failure

2018 - 2021

Active

Detection of Infected Internet-of-Thing (IoT) Devices to Prevent Distributed Denial of Service (DDoS) Attacks

2018 - 2018

Completed

Scaling up Bayesian record linkage

2018 - 2018

Completed

Situation-aware Smart Cognitive Radio Networks

2017 - 2020

Completed

AMSI Intern Program - Evaluate the methodological feasibility of Bayesian approaches to entity resolution and de-duplication

2017 - 2018

Completed

20

Projects

Project Types

13

Research Contracts

5

Research Grant

2

Internal Research Grant

Additional Information

Microsoft Research Azure Machine Learning Award, 2015 Amazon AWS Machine Learning Grant, 2013

Scholarly Works

Displaying the 10 most recent scholarly works by Ben Rubinstein.

Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 with technological interventions.

Kobi Leins, Christopher Culnane, Benjamin Ip Rubinstein

Journal article | 2020 | Medical Journal of Australia

Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 through technological interventions. (in reply)

Christopher Culnane, Kobi Leins, Benjamin Ip Rubinstein

Journal article | 2020 | Medical Journal of Australia

Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model

Shuo Zhou, Howard Bondell, Antoinette Tordesillas, Benjamin IP Rubinstein, James Bailey

Journal article | 2020 | Annals of Applied Statistics

Movement of soil and rocks in an unstable slope under gravitational forces is an example of a complex system that is highly dynami..

Sampling Without Compromising Accuracy in Adaptive Data Analysis

Benjamin Fish, Lev Reyzin, Benjamin Rubinstein

Conference Proceedings | 2020 | Proceedings of the 31st International Conference on Algorithmic Learning Theory

In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing th..

Legion: Best-first concolic testing (competition contribution)

D Liu, G Ernst, T Murray, BIP Rubinstein

Conference Proceedings | 2020 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Legion is a grey-box coverage-based concolic tool that aims to balance the complementary nature of fuzzing and symbolic execution ..

Function Interpolation for Learned Index Structures

NF Setiawan, BIP Rubinstein, R Borovica-Gajic

Conference Proceedings | 2020 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Range indexes such as B-trees are widely recognised as effective data structures for enabling fast retrieval of records by the que..

Assessing Centrality Without Knowing Connections

Leyla Roohi, Benjamin IP Rubinstein, Vanessa Teague

Conference Proceedings | 2020 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric between..

Attacking Data Transforming Learners at Training Time

Scott Alfeld, Ara Vartanian, Lucas Newman-Johnson, Benjamin IP Rubinstein

Conference Proceedings | 2019 | Thirty-Third AAAI Conference on Artificial Intelligence

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, littl..

Truth inference at scale: A Bayesian model for adjudicating highly redundant crowd annotations

Y Li, BIP Rubinstein, T Cohn

Conference Proceedings | 2019 | WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)

Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses th..

Differentially-Private Two-Party Egocentric Betweenness Centrality

L Roohi, BIP Rubinstein, V Teague

Conference Proceedings | 2019 | IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

77

Scholarly Works

Keyword Analysis

Computer Software and Services

Artificial Intelligence and Image Processing

Distributed Computing

Computation Theory and Mathematics

News

Displaying the 9 most recent news articles by Ben Rubinstein.