Professor Graham Cormode

I studied at Cambridge and the University of Warwick. I was a principal researcher at Bell Labs (2004-06), a senior researcher at AT&T Labs-Research (2006-13) and Professor at the University of Warwick (2013-2025). I have also been a Senior Research Scientist at Meta, working on privacy-preserving machine learning.

Teaching

I enjoy teaching a range of subjects, including mathematics for computer science, algorithms, data management, and data analytics.
I am always interested to hear from strong potential PhD students and postdocs with backgrounds in algorithms/mathematics wanting to work on topics such as:

• streaming/sketching for rapid processing of massive dataset;
• anonymization and privacy for different applications, including privacy-preserving machine learning and synthetic data generation;
• zero-knowledge proofs, federated learning, homomorphic encryption, and other privacy-enhancing technologies.

My research interests are in data privacy, data stream analysis, massive data sets, and general algorithmic problems. I study all aspects of the 'data lifecycle', from data collection and cleaning, through mining, learning and analytics, and private data release. Many companies have used my work for data summarization and privacy-preserving analysis, including Apple, X/Twitter, Netflix, Yahoo!, Google, AT&T, Sprint, and Cloudera.

Recent Publications

• G. Cormode, S. Maddock, E. Ullah, and S. Gade. Synthetic tabular data: Methods, attacks and defenses. In ACM SIGKDD Conference, pages 5989-5998, 2025.
  https://dimacs.rutgers.edu/~graham/pubs/papers/synthsurvey.pdf
• H. Srinivas, G. Cormode, M. Honarkhah, S. Lurye, J. Hehir, L. He, G. Hong, A. Magdy, D. Huba, K. Wang, S. Guo, and S. Bhattacharya. PAPAYA federated analytics stack: Engineering privacy, scalability and practicality. In USENIX Symposium on Networked Systems Design and Implementation, NSDI, pages 883-898. USENIX Association, 2025.
  https://dimacs.rutgers.edu/~graham/pubs/papers/papaya-nsdi.pdf
• G. Cormode, M. Dall'Agnol, T. Gur, and C. Hickey. Streaming zero-knowledge proofs. In Computational Complexity Conference (CCC), 2024.
  https://dimacs.rutgers.edu/~graham/pubs/papers/streamingzk.pdf
• A. Bharadwaj and G. Cormode. Sample-and-threshold differential privacy: Histograms and applications. In AISTATS, 2022.
  https://dimacs.rutgers.edu/~graham/pubs/html/BharadwajCormode22.html
• G. Cormode and K. Yi. Small Summaries for Big Data. CUP, 2020.
  https://dimacs.rutgers.edu/~graham/ssbd.html

Awards and Distinctions

• ACM Fellow (2021)
• Adams prize for mathematics (2017)
• Royal Society Wolfson Research Merit Award (2014)
• SIGMOD test-of-time award (2024) for 'PrivBayes: Private Data Release via Bayesian Networks'
• Alberto Mendelzon award (2022) for 'Mergeable Summaries'
• Imre Simon award (2014), for 'Count-Min Sketch and its Applications'