Rate Professor Ben Rubinstein

Professional Summary: Professor Benjamin Rubinstein

Professor Benjamin Rubinstein is a distinguished academic at the University of Melbourne, Australia, with a focus on data science, machine learning, and cybersecurity. His work bridges theoretical advancements and practical applications, contributing significantly to the fields of artificial intelligence and data privacy.

Academic Background and Degrees

Professor Rubinstein holds advanced degrees in computer science and related fields, with his foundational education shaping his expertise in data-driven research:

• Ph.D. in Computer Science from the University of California, Berkeley, with a focus on machine learning and security.
• Bachelor of Science (Honours) in Computer Science from the University of Melbourne.

Research Specializations and Academic Interests

Professor Rubinstein’s research spans several critical areas within computer science, with particular emphasis on:

• Machine Learning: Developing robust and scalable algorithms for data analysis.
• Data Privacy: Innovating techniques for differential privacy and secure data sharing.
• Cybersecurity: Addressing vulnerabilities in machine learning models and data systems.
• Artificial Intelligence: Exploring ethical and technical challenges in AI deployment.

Career History and Appointments

Professor Rubinstein has held several prestigious positions across academia and industry, reflecting his expertise and leadership:

• Professor, School of Computing and Information Systems, University of Melbourne (current position).
• Former researcher at Microsoft Research, contributing to advancements in data privacy.
• Postdoctoral researcher at IBM Research, focusing on machine learning applications.

Major Awards, Fellowships, and Honors

Professor Rubinstein has been recognized for his contributions to computer science and data privacy with notable accolades, including:

• Recipient of the Chris Wallace Award for Outstanding Research Contribution by the Computing Research and Education Association of Australasia (CORE).
• Grants and fellowships from the Australian Research Council (ARC) for projects in data science and privacy.

Key Publications

Professor Rubinstein has authored numerous influential papers and articles in top-tier journals and conferences. A selection of his key works includes:

• 'The Bernstein Mechanism: Differentially Private Query Answering with Optimal Utility' (2017), published in the ACM Transactions on Database Systems.
• 'Pain-Free Random Differential Privacy with Sensitivity Sampling' (2017), presented at the International Conference on Machine Learning (ICML).
• 'Data Poisoning Attacks on Factorization-Based Collaborative Filtering' (2016), published in the Proceedings of the Neural Information Processing Systems (NeurIPS).

Influence and Impact on Academic Field

Professor Rubinstein’s research has had a profound impact on the fields of machine learning and data privacy. His work on differential privacy has informed policies and practices for secure data handling in both academia and industry. Additionally, his contributions to understanding vulnerabilities in machine learning models have shaped discussions on AI ethics and security, influencing both technical development and regulatory frameworks.

Public Lectures, Committees, and Editorial Contributions

Professor Rubinstein is actively involved in the broader academic community through public engagement and leadership roles:

• Regular speaker at international conferences such as NeurIPS, ICML, and the Privacy Enhancing Technologies Symposium (PETS).
• Member of program committees for leading conferences in machine learning and data privacy.
• Editorial board member and reviewer for prominent journals in computer science and data science.