bipr.net Benjamin Rubinstein

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Benjamin Rubinstein Home data Juliet & Ella Lachlan Liam Proceedings of the First AUSCC Proceedings of the Second AUSCC secml Home Benjamin Rubinstein Senior Lecturer (equiv US Assistant Professor) Dept. Computing & Information Systems The University of Melbourne, Australia Affiliations: Fellow, Centre for Business Analytics, Melbourne Business School [link] Associate Investigator, ARC Centre of Excellence for Mathematical & Statistical Frontiers [link] [firstname].i.p.[lastname]@gmail.com LinkedIn Twitter Scholar arXiv UniMelb Find an Expert 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. Service Member, National Committee for Information and Communication Sciences, Australian Academy of Science (2 year term starting Nov 2015) Program Committees: KDD'2015,'2016, ICDE'2016, CSF'2014 (co-chair AI&Security Track), NIPS'2014, SIGMOD'2013, IJCAI'2013, ACML'2013, ICML '2011,'2012 Organiser: ACM AI & Security workshops AISEC '2011,'2012,'2014 (at CCS) Organiser: Learning, Security & Privacy workshop (at ICML'2014) Chair, Demonstration and Workshop Local Arrangements SIGMOD'2015 Workshop PCs: AI & Security AISec'2009,'2010,'2013,'2015,'2016 (at CCS), S+SSPR'2016 (at ICPR), PSDML'2010 (at ECML/PKDD) Funding 10/2015: Australian Research Council Discovery Early Career Researcher Award Secure and Private Machine Learning (for 2016-18 as sole-CI fellow) was funded for $370k Cat1 (additional $85k University of Melbourne DECRA establishment grant) 09/2015: Melbourne Networked Society Institute Active Defence (with Atif Ahmad, Tansu Alpcan, Andre Gygax, Chris Leckie), $48k [media: pursuit] 07/2015: Elon Musk-backed FLI Project Grant for Security Evaluation of Machine Learning Systems (sole-CI), $128k Cat3 [media: vice news.com.au pursuit] 01/2015: Microsoft Research Azure Machine Learning Award to support work on big data preparation 11/2014: Australian Research Council Discovery Project Democratising Big Machine Learning (for 2015-17 as sole-CI) was funded for $216k Cat1 10/2014: University of Melbourne Early Career Researcher Grant to support work on adversarial machine learning $39k 03/2014: Amazon AWS Machine Learning Grant to support work on adversarial machine learning 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 Akshay Balsubramani - UC San Diego (2012) Emanuele Coviello - UC San Diego (2012) → Founder/CEO at Keevio Sahand Negahban - UC Berkeley (2011) → Assistant Prof at Yale Statistics Duo Zhang - UIUC (2011) → Software Engineer at Twitter Bo Zhao - UIUC (2011) → Researcher at Microsoft Research Teaching BUSA90501 Statistical Learning 2: Winter, 2015 - ongoing (in Melbourne Business School, Master of Business Analytics) COMP90051 Statistical Machine Learning: Semester 2, 2013 - 2015 COMP10002 Foundations of Algorithms: Semester 1, 2014 - 2015 Publications 2016 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 Approach, in 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. 2015 Yi Han, Tansu Alpcan, Jeffrey Chan, Christopher Leckie, Benjamin I. P. Rubinstein, A Game Theoretical Approach to Defend against Co-resident Attacks in Cloud Computing using Semi-supervised Learning, IEEE Transactions on Information Forensics and Security, accepted October 2015. J. Hyam Rubinstein, Benjamin I. P. Rubinstein, and Peter L. Bartlett, Bounding Embeddings of VC Classes into Maximum Classes, chapter in V. Vovk, H. Papadopoulos, and A. Gammerman (eds.), Measures of Complexity: Festschrift of Alexey Chervonenkis, pp. 303-325, Springer, Berlin, 2015. Maryam Fanaeepour, Lars Kulik, Egemen Tanin, and Benjamin I. P. Rubinstein, The CASE Histogram: Privacy-Aware Processing of Trajectory Data Using Aggregates, in GeoInformatica, 19(4), pp. 747-798, accepted April 2015. Duo Zhang, Benjamin I. P. Rubinstein, and Jim Gemmell, Principled Graph Matching Algorithms for Integrating Multiple Data Sources, in IEEE Transactions on Knowledge and Data Engineering, 27(10), pp. 2784-2796, accepted March 2015 [earlier report] Zhe Lim and Benjamin I. P. Rubinstein, Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching, in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'2015), pp. 1791-1798, January 2015 [datasets] Jiazhen He, James Bailey, Benjamin I. P. Rubinstein, and Rui Zhang, Identifying At-Risk Students in Massive Open Online Courses, in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'2015), pp. 1749-1755, January 2015 2014 Christos Dimitrakakis, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein (eds.), Proceedings of the 7th Workshop on Artificial Intelligence and Security (AISec 2014), 124 pages, ACM Press, November 2014 Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, and Benjamin I. P. Rubinstein, Robust and Private Bayesian Inference, 25th Conference on Algorithmic Learning Theory (ALT), Lecture Notes in Computer Science, 8776, pp. 291-305, Springer, October 2014. Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli, Security Evaluation of Support Vector Machines in Adversarial Environments, chapter in book Y. Ma and G. Guo (eds.), Support Vector Machine Applications, pp. 105-153, Springer, February 2014 [techreport version] 2012 Sahand Negahban, Benjamin I. P. Rubinstein, and Jim Gemmell, Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning, in the Proc. 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), pp. 2224-2228, October 2012 [full version] Alvaro A. Cárdenas, Blaine Nelson, and Benjamin I. P. Rubinstein (eds.), Proceedings of the 5th Workshop on Artificial Intelligence and Security (AISec 2012), 110 pages, ACM Press, October 2012 Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft, Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning, in Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy of the Journal of Privacy and Confidentiality, 4(1), pp. 65-100, August 2012 [2009 preprint] Benjamin Rubinstein and Aleksandr Simma, On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers, in IEEE Transactions on Information Theory, 58(7), pp. 4160-4163, July 2012 Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, and Peter L. Bartlett, A Learning-Based Approach to Reactive Security, in IEEE Transactions on Dependable and Secure Computing, 9(4), pp. 482-493, July-Aug 2012 Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, and J. D. Tygar, Query Strategies for Evading Convex-Inducing Classifiers, in Journal of Machine Learning Research, 13(May), pp. 1293-1332, MIT Press, 2012 Benjamin I. P. Rubinstein and J. Hyam Rubinstein, A Geometric Approach to Sample Compression, in Journal of Machine Learning Research, 13(April), pp. 1221-1261, MIT Press, 2012 Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, and Jiawei Han, A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration, in Proc. 2012 International Conference on Very Large Data Bases (VLDB'12/PVLDB), 5(February), pp. 550-561, VLDB Endowment Inc., February 2012 2011 Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. D. Tygar, Adversarial Machine Learning, in Proceedings of the 4th ACM Workshop on Artificial Intelligence and Security, pp. 43-54, 21 October 2011 Alvaro A. Cárdenas, Rachel Greenstadt, and Benjamin I. P. Rubinstein (eds.), Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 116 pages, ACM Press, October 2011 Adam Barth, Saung Li, Benjamin I. P. Rubinstein, and Dawn Song, How Open Should Open Source Be?, Technical Report UCB/EECS-2011-98, Dept. EECS, UC Berkeley, 31 August 2011 Jim Gemmell, Benjamin I. P. Rubinstein, and Ashok K. Chandra, Improving Entity Resolution with Global Constraints, Technical Report MSR-TR-2011-100, Microsoft Research, 30 August 2011 Arvind Narayanan, Elaine Shi, and Benjamin Rubinstein, Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge, in Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1825-1834, IEEE, 22 February 2011 2010 Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, and J. D. Tygar, Classifier Evasion: Models and Open Problems, in ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, 2010 Benjamin Rubinstein, Secure Learning and Learning for Security: Research in the Intersection, PhD Dissertation, Dept. EECS, UC Berkeley, 13 May 2010 Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, and Peter L. Bartlett, A Learning-Based Approach to Reactive Security, in Proceedings of the Fourteenth International Conference on Financial Cryptography and Data Security (FC 2010), 2010 Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven Lee, Satish Rao, Anthony Tran, and J. D. Tygar, Near Optimal Evasion of Convex-Inducing Classifiers, in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 549-556, 2010 2009 Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J. D. Tygar, ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors, in Proceedings of the Ninth Internet Measurement Conference (IMC 2009), pp. 1-14, 2009 Arpita Ghosh, Benjamin I. P. Rubinstein, Sergei Vassilvitskii, and Martin Zinkevich, Adaptive Bidding for Display Advertising, in Proceedings of the 18th International World Wide Web Conference (WWW 2009), pp. 251-260, 2009 Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting: One-Inclusion Mistake Bounds and Sample Compression, in Journal of Computer and System Sciences, 75(1), pp. 37–59, 2009 [2009 erratum] Previously: Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting: One-Inclusion Mistake Bounds and Sample Compression, Technical Report UCB/EECS-2007-86, Dept. EECS, UC Berkeley, June 2007 Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia, Misleading Learners: Co-opting Your Spam Filter, book chapter in Machine Learning in Cyber Trust: Security, Privacy, and Reliability, pp. 17–51, Springer, 2009 Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J. D. Tygar, Stealthy Poisoning Attacks on PCA-based Anomaly Detectors, in ACM SIGMETRICS Performance Evaluation Review, 37(2), pp. 73–74, 2009 Benjamin I. P. Rubinstein, Shifting in the n-Cube: Online Mistake Bounds and the Sample Compression Conjecture, Masters Research thesis, Faculty of Engineering, University of Melbourne, 2009 2008 Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, and J. D. Tygar, Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract), in Proceedings of the 11th International Symposium on Recent Advances in Intrusion Detection (RAID 2008), pp. 394-395, 2008 winner of RAID08 best poster award Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, and J. D. Tygar, Open Problems in the Security of Learning, in Proceedings of the 1st ACM Workshop on AISec (AISec 2008), pp. 19-26, 2008 Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, and Doug Tygar, Compromising PCA-based Anomaly Detectors for Network-Wide Traffic, Technical Report UCB/EECS-2008-73, Dept. EECS, UC Berkeley, May 2008 Benjamin I. P. Rubinstein and J. Hyam Rubinstein, Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression, in Proceedings of the 21st Annual Conference on Learning Theory (COLT'08), pp. 299-310, 2008 Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia, Exploiting Machine Learning to Subvert Your Spam Filter, in First USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET'08), 2008 2007 Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein, Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds, in Advances in Neural Information Processing Systems 19 (NIPS 2006), pp. 1193-1200, 2007 Pre 2007 Gad Abraham and Benjamin I. P. Rubinstein (eds.), Proceedings of the Second Australian Students' Computing Conference (AUSCC), ISBN 0-975-71730-8, 175 pages, 2004 Benjamin I. P. Rubinstein, Nelson Chan, and K. K. Kshetrapalapuram (eds.), Proceedings of the First Australian Undergraduate Students' Computer Conference (AUSCC), ISBN 0-646-42751-2, 126 pages, 2003 Benjamin I. P. Rubinstein, Jon McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, and Terence P. Speed, Machine Learning in Low-level Microarray Analysis, in ACM SIGKDD Explorations (Special Issue on Microarray Data Mining), 5(2), pp. 130–139, December 2003 Benjamin I. P. Rubinstein, Evolving Quantum Circuits using Genetic Programming, in Proceedings of the 2001 IEEE Congress on Evolutionary Computation (CEC2001), IEEE Press, 2001 winner of IEEE Computer Society's Lance Stafford Larson Scholarship for best ugrad student paper worldwide. Previously: Benjamin I. P. Rubinstein, Evolving Quantum Circuits using Genetic Programming, in Genetic Algorithms and Genetic Programming at Stanford 2000, pp. 325–334, Stanford Bookstore, Stanford University, CA, 2000 Report Abuse|Powered By Google Sites

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