Graham Cormode

I am a professor in the Department of Computer Science at the University of Warwick, and my interests are in all aspects of the "data lifecycle", from data collection and cleaning, through mining and analytics, and private data release. From 2004--06, I worked at Bell Laboratories in the Internet Management Research Department. From 2006-2013, I was a researcher at AT&T Labs--Research.

Between 2002 and 2004, I was a postdoctoral fellow at DIMACS, the Center for Discrete Mathematics and Computer Science. I completed my PhD at the Department of Computer Science at the University of Warwick, UK in 2002. I spent a year of my PhD studying in Cleveland, Ohio at Case Western Reserve University with the Electrical Engineering and Computer Science Department , and Summer 2000 as an intern at AT&T Shannon research labs.

For more information, see a (reasonably up to date) CV.

Conference Publications

[1] G. Cormode, S. Maddock, E. Ullah, and S. Gade. Synthetic tabular data: Methods, attacks and defenses. In ACM SIGKDD Conference, 2025.
[2] G. Cormode and D. Ting. Federated data distribution shift estimation. In Proceedings of the VLDB Endowment, volume 18, pages 2399-2412, 2025.
[3] S. Maddock, S. Gade, G. Cormode, and W. Bullock. Leveraging vertical public-private split for improved synthetic data generation. 2025.
[4] 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.
[5] A. Biswas, G. Cormode, Y. Kanza, D. Srivastava, and Z. Zhou. Differentially private hierarchical heavy hitters. In ACM Principles of Database Systems (PODS), 2025.
[6] G. Cormode, S. Maddock, and C. Maple. FLAIM: AIM-based synthetic data generation in the federated setting. In ACM SIGKDD Conference, 2024.
[7] G. Cormode, I. L. Markov, and H. Srinivas. Private and efficient federated numerical aggregation. In International Conference on Extending Database Technology, pages 734-742, 2024.
[8] W. Chen, G. Cormode, A. Bharadwaj, P. Romov, and A. Özgür. Federated experiment design under distributed differential privacy. In AISTATS, volume 238 of Proceedings of Machine Learning Research, pages 2458-2466. PMLR, 2024.
[9] G. Cormode, M. Dall'Agnol, T. Gur, and C. Hickey. Streaming zero-knowledge proofs. In Computational Complexity Conference (CCC), 2024.
[10] G. Cormode and I. L. Markov. Federated calibration and evaluation of binary classifiers. In Proceedings of the VLDB Endowment, volume 16(11), pages 3253-3265, 2023.
[11] W.-N. Chen, A. Özgür, G. Cormode, and A. Bharadwaj. The communication cost of security and privacy in federated frequency estimation. In AISTATS, pages 4247-4274, 2023.
[12] J. Hehir, D. Ting, and G. Cormode. Sketch-flip-merge: Mergeable sketches for private distinct counting. In International Conference on Machine Learning, (ICML), 2023.
[13] A. Biswas and G. Cormode. Interactive proofs for differentially private counting. In ACM Conference on Computer and Communications Security, 2023.
[14] K. Cai, X. Xiao, and G. Cormode. Privlava: Synthesizing relational data with foreign keys under differential privacy. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2023.
[15] M. Shekelyan, G. Cormode, P. Triantafillou, Q. Ma, and A. M. Shanghooshabad. Streaming weighted sampling over join queries. In EDBT, pages 298-310, 2023.
[16] K. Prasad, S. Ghosh, G. Cormode, I. Mironov, A. Yousefpour, and P. Stock. Reconciling security and communication efficiency in federated learning. In International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022, 2022.
[17] S. Maddock, G. Cormode, S. Jha, C. Maple, and T. Wang. Federated boosted decision trees with differential privacy. In ACM Conference on Computer and Communications Security, 2022.
[18] L. Watson, C. Guo, G. Cormode, and A. Sablayrolles. On the importance of difficulty calibration in membership inference attacks. In International Conference on Learning Representations, ICLR, 2022.
[19] A. Bharadwaj and G. Cormode. Sample-and-threshold differential privacy: Histograms and applications. In AISTATS, 2022. (full version).
[20] Z. Huang, Y. Qiu, K. Yi, and G. Cormode. Frequency estimation under multiparty differential privacy: One-shot and streaming. In International Conference on Very Large Data Bases (VLDB), volume 15, pages 2058-2070. VLDB Endowment, 2022.
[21] A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov. Opacus: User-friendly differential privacy library in pytorch. In Privacy in Machine Learning (NeurIPS workshop), 2021.
[22] A. Bharadwaj and G. Cormode. Sample-and-threshold differential privacy: Histograms and applications. In Privacy in Machine Learning (NeurIPS workshop), 2021. (workshop version).
[23] G. Cormode and I. Markov. Bit-efficient numerical aggregation and stronger privacy for trust in federated analytics. In PPML Workshop, 2021.
[24] G. Cormode, C. Maple, and M. Scott. Applying the shuffle model of differential privacy to vector aggregation. In BICOD, 2021.
[25] T. Cunningham, G. Cormode, H. Ferhatosmanoglu, and D. Srivastava. Real-world trajectory sharing with local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2021.
[26] G. Cormode, S. Maddock, and C. Maple. Frequency estimation under local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2021.
[27] T. Cunningham, G. Cormode, and H. Ferhatosmanoglu. Privacy-preserving synthetic location data in the real world. In Proceedings of International Symposium on Spatial and Temporal Databases, 2021.
[28] G. Cormode, A. Mishra, J. Ross, and P. Veselý. Theory meets practice at the median:a worst case comparison of relative error quantile algorithms. In ACM SIGKDD Conference, 2021.
[29] M. Shekelyan and G. Cormode. Sequential random sampling revisited: Hidden shuffle method. In AISTATS, volume 130 of Proceedings of Machine Learning Research, pages 3628-3636. PMLR, 2021.
[30] G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and P. Veselý. Relative error streaming quantiles. In ACM Principles of Database Systems (PODS), 2021.
[31] G. Cormode, C. Dickens, and D. Woodruff. Subspace exploration: Bounds on projected frequency estimation. In ACM Principles of Database Systems (PODS), 2021.
[32] G. Cormode, M. Garofalakis, and M. Shekelyan. Data-independent space partitionings for summaries. In ACM Principles of Database Systems (PODS), 2021.
[33] G. Cormode and P. Veselý. A tight lower bound for comparison-based quantile summaries. In ACM Principles of Database Systems (PODS), pages 81-93. ACM, 2020.
[34] G. Cormode and C. Dickens. Iterative hessian sketch in input sparsity time. In Proceedings of Beyond First Order Methods in ML (NeurIPS workshop), 2019.
[35] G. Cormode and C. Hickey. Efficient interactive proofs for linear algebra. In Proceedings of International Symposium on Algorithms and Computation (ISAAC), 2019.
[36] G. Cormode and P. Veselý. Streaming algorithms for bin packing and vector scheduling. In Workshop on Approximation and Online Algorithms, 2019.
[37] R. Chitnis and G. Cormode. Towards a theory of parameterized streaming algorithms. In International Symposium on Parameterized and Exact Computation, 2019.
[38] G. Cormode, T. Kulkarni, and D. Srivastava. Answering range queries under local differential privacy. In International Conference on Very Large Data Bases (VLDB), 2019.
[39] G. Cormode, J. Dark, and C. Konrad. Independent sets in vertex-arrival streams. In International Colloquium on Automata, Languages and Programming (ICALP), 2019.
[40] G. Cormode, C. Dickens, and D. P. Woodruff. Leveraging well-conditioned bases: Streaming and distributed summaries in minkowski p-norms. In International Conference on Machine Learning, (ICML), 2018.
[41] G. Cormode, T. Kulkarni, and D. Srivastava. Marginal release under local differential privacy. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2018.
[42] G. Cormode and C. Hickey. You can check others' work more quickly than doing it yourself. In International Conference on Data Engineering (ICDE), 2018.
[43] G. Cormode, J. Dark, and C. Konrad. Approximating the caro-wei bound for independent sets in graph streams. In International Symposium on Combinatorial Optimization, 2018.
[44] Y. Zhang, S. Tirthapura, and G. Cormode. Learning graphical models from a distributed stream. In International Conference on Data Engineering (ICDE), 2018.
[45] G. Cormode and J. Dark. Fast sketch-based recovery of correlation outliers. In International Conference on Database Theory, 2018.
[46] G. Cormode and C. Hickey. Cheap checking for cloud computing: Statistical analysis via annotated data streams. In AISTATS, 2018.
[47] G. Cormode, T. Kulkarni, and D. Srivastava. Constrained private mechanisms for count data. In International Conference on Data Engineering (ICDE), 2018.
[48] G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan. Streaming algorithms for matching size estimation in sparse graphs. In European Symposium on Algorithms, 2017.
[49] Z. Jorgensen, T. Yu, and G. Cormode. Publishing attributed social graphs with formal privacy guarantees. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 107-122, 2016.
[50] R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, A. McGregor, M. Monemizadeh, and S. Vorotnikova. Kernelization via sampling with applications to dynamic graph streams. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2016.
[51] K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth. Correlation clustering in data streams. In International Conference on Machine Learning, (ICML), pages 2237-2246, 2015.
[52] X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava. DPT: differentially private trajectory synthesis using hierarchical reference systems. In Proceedings of the VLDB Endowment, volume 8, pages 1154-1165, 2015.
[53] R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, and M. Monemizadeh. New streaming algorithms for parameterized maximal matching & beyond. In Symposium on Parallelism in Algorithms, pages 56-58, 2015.
[54] J. Zhang, G. Cormode, M. Procopiuc, D. Srivastava, and X. Xiao. Private release of graph statistics using ladder functions. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2015.
[55] A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venkatasubramanian. Verifiable stream computation and Arthur-Merlin communication. In Computational Complexity Conference, 2015.
[56] Z. Jorgensen, T. Yu, and G. Cormode. Conservative or liberal? personalized differential privacy. In International Conference on Data Engineering (ICDE), 2015.
[57] R. Chitnis, G. Cormode, M. Hajiaghayi, and M. Monemizadeh. Parameterized streaming: Maximal matching and vertex cover. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015.
[58] G. Cormode, M. Procopiuc, D. Srivastava, X. Xiao, and J. Zhang. Privbayes: Private data release via bayesian networks. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2014.
[59] Q. Ma, S. Muthukrishnan, B. Thompson, and G. Cormode. Modeling collaboration in academia: A game theoretic approach. In WWW Workshop on Big Scholarly Data, 2014.
[60] G. Cormode, S. Muthukrishnan, and J. Yan. People like us: Mining scholarly data for comparable researchers. In WWW Workshop on Big Scholarly Data, 2014.
[61] A. Chakrabarti, G. Cormode, N. Goyal, and J. Thaler. Annotations for sparse data streams. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2014.
[62] G. Cormode, S. Muthukrishnan, and J. Yan. First author advantage: Citation labeling in research. In Proceedings of the Computational Scientometrics: Theory and Applications Workshop at CIKM, 2013.
[63] G. Cormode, X. Gong, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. UMicS: From anonymized data to usable microdata. In ACM Conference on Information and Knowledge Management (CIKM), 2013.
[64] S. Papadopoulos, G. Cormode, A. Deligiannakis, and M. Garofalakis. Lightweight authentication of linear algebraic queries on data streams. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2013.
[65] L. Wang, G. Luo, K. Yi, and G. Cormode. Quantiles over data streams: An experimental study. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2013.
[66] G. Cormode, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. Empirical privacy and empirical utility of anonymized data. In Privacy-Preserving Data Publication and Analysis (PrivDB), 2013.
[67] G. Cormode, K. Mirylenka, T. Palpanas, and D. Srivastava. Finding interesting correlations with conditional heavy hitters. In International Conference on Data Engineering (ICDE), 2013.
[68] G. Cormode, C. M. Procopiuc, D. Srivastava, and G. Yaroslavtsev. Accurate and efficient private release of datacubes and contingency tables. In International Conference on Data Engineering (ICDE), 2013.
[69] G. Cormode and D. Firmani. On unifying the space of l0-sampling algorithms. In SIAM Meeting on Algorithm Engineering and Experiments, 2013.
[70] G. Cormode, J. Thaler, and K. Yi. Verifying computations with streaming interactive proofs. In International Conference on Very Large Data Bases (VLDB), Sept. 2012.
[71] A. Goyal, H. Daumé III, and G. Cormode. Sketch algorithms for estimating point queries in NLP. In EMNLP-CoNLL, pages 1093-1103, 2012.
[72] P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi. Mergeable summaries. In ACM Principles of Database Systems (PODS), 2012.
[73] E. Cohen, G. Cormode, and N. Duffield. Don't let the negatives bring you down: Sampling from streams of signed updates. In ACM Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2012.
[74] G. Cormode and K. Yi. Tracking distributed aggregates over time-based sliding windows. In Scientific and Statistical Database Management (SSDBM), 2012.
[75] G. Cormode, S. Muthukrishnan, and J. Yan. Scienceography: the study of how science is written. In Proceedings of the International Conference on Fun with Algorithms (FUN), 2012.
[76] G. Cormode, M. Procopiuc, D. Srivastava, and T. Tran. Differentially private publication of sparse data. In International Conference on Database Theory (ICDT), 2012.
[77] G. Cormode, M. Procopiuc, E. Shen, D. Srivastava, and T. Yu. Differentially private spatial decompositions. In International Conference on Data Engineering (ICDE), 2012.
[78] M. Lu, S. Bangalore, G. Cormode, M. Hadjieleftheriou, and D. Srivastava. A dataset search engine for the research document corpus. In International Conference on Data Engineering (ICDE), 2012.
[79] G. Cormode, E. Shen, D. Srivastava, and T. Yu. Aggregate query answering on possibilistic data with cardinality constraints. In International Conference on Data Engineering (ICDE), 2012.
[80] G. Cormode, M. Mitzenmacher, and J. Thaler. Practical verified computation with streaming interactive proofs. In Innovations in Theoretical Computer Science (ITCS), 2012.
[81] P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi. Mergeable coresets. In Third Workshop on Massive Data Algorithmics (MASSIVE), 2011.
[82] G. Cormode. Personal privacy vs population privacy: Learning to attack anonymization. In ACM SIGKDD Conference, 2011.
[83] E. Cohen, G. Cormode, and N. Duffield. Structure-aware sampling: Flexible and accurate summarization. In International Conference on Very Large Data Bases (VLDB), 2011.
[84] G. Cormode and K. Yi. Tracking distributed aggregates over time-based sliding windows (brief announcement). In ACM Principles of Distributed Computing (PODC), 2011.
[85] E. Cohen, G. Cormode, and N. Duffield. Structure-aware sampling on data streams. In ACM Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2011.
[86] G. Cormode, H. Karloff, and T. Wirth. Set cover algorithms for very large datasets. In ACM Conference on Information and Knowledge Management (CIKM), 2010.
[87] G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang. Optimal sampling from distributed streams. In ACM Principles of Database Systems (PODS), 2010.
[88] S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Privacy in dynamic social networks. In World Wide Web Conference (WWW), 2010.
[89] G. Cormode, M. Mitzenmacher, and J. Thaler. Streaming graph computations with a helpful advisor. In European Symposium on Algorithms, 2010.
[90] A. Chakrabarti, G. Cormode, R. Kondapally, and A. McGregor. Information cost tradeoffs for augmented index and streaming language recognition. In IEEE Foundations of Computer Science (FOCS), 2010.
[91] G. Cormode, N. Li, T. Li, and D. Srivastava. Minimizing minimality and maximizing utility: Analyzing method-based attacks on anonymized data. In International Conference on Very Large Data Bases (VLDB), 2010.
[92] S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Prediction promotes privacy in dynamic social networks. In Workshop on Online Social Networks (WOSN), 2010.
[93] S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava. Class-based graph anonymization for social network data. In International Conference on Very Large Data Bases (VLDB), 2009.
[94] G. Cormode, A. Deligiannakis, M. Garofalakis, and A. McGregor. Probabilistic histograms for probabilistic data. In International Conference on Very Large Data Bases (VLDB), 2009.
[95] A. Chakrabarti, G. Cormode, and A. McGregor. Annotations in data streams. In International Colloquium on Automata, Languages and Programming (ICALP), 2009.
[96] G. Cormode, L. Golab, F. Korn, A. McGregor, D. Srivastava, and X. Zhang. Estimating the confidence of conditional functional dependencies. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2009.
[97] R. Berinde, G. Cormode, P. Indyk, and M. Strauss. Space-optimal heavy hitters with strong error bounds. In ACM Principles of Database Systems (PODS), 2009.
[98] G. Cormode, S. Tirthapura, and B. Xu. Time-decayed correlated aggregates over data streams. In SIAM Conference on Data Mining (SDM), 2009.
[99] G. Cormode and M. Garofalakis. Histograms and wavelets on probabilistic data. In International Conference on Data Engineering (ICDE), 2009. Best paper award.
[100] G. Cormode, F. Li, and K. Yi. Semantics of ranking queries for probabilistic data and expected ranks. In International Conference on Data Engineering (ICDE), 2009.
[101] G. Cormode, V. Shkapenyuk, D. Srivastava, and B. Xu. Forward decay: A practical time decay model for streaming systems. In International Conference on Data Engineering (ICDE), 2009.
[102] G. Cormode and M. Hadjieleftheriou. Finding frequent items in data streams. In International Conference on Very Large Data Bases (VLDB), 2008. Best paper award.
[103] G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings. In International Conference on Very Large Data Bases (VLDB), 2008.
[104] G. Cormode, F. Korn, and S. Tirthapura. Time-decaying aggregates in out-of-order streams. In ACM Principles of Database Systems (PODS), 2008.
[105] G. Cormode and A. McGregor. Approximation algorithms for clustering uncertain data. In ACM Principles of Database Systems (PODS), 2008.
[106] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Summarizing two-dimensional data with skyline-based statistical descriptors. In Scientific and Statistical Database Management (SSDBM), 2008.
[107] A. Chakrabarti, G. Cormode, and A. McGregor. Robust lower bounds for communication and stream computation. In ACM Symposium on Theory of Computing (STOC), 2008.
[108] G. Cormode, F. Korn, S. Muthukrishnan, and Y. Wu. On signatures for communication graphs. In International Conference on Data Engineering (ICDE), 2008.
[109] G. Cormode, F. Korn, and S. Tirthapura. Exponentially decayed aggregates on data streams. In International Conference on Data Engineering (ICDE), 2008.
[110] G. Cormode, S. Muthukrishnan, and K. Yi. Algorithms for distributed, functional monitoring. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2008.
[111] S. Bhagat, G. Cormode, and I. Rozenbaum. Applying link-based classification to label blogs. In Joint WEBKDD and SNA-KDD Workshop, 2007.
[112] S. Ganguly and G. Cormode. On estimating frequency moments of data streams. In Proceedings of RANDOM, 2007.
[113] G. Cormode, S. Tirthapura, and B. Xu. Time-decaying sketches for sensor data aggregation. In ACM Principles of Distributed Computing (PODC), 2007.
[114] G. Cormode and M. Garofalakis. Sketching probabilistic data streams. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2007.
[115] S. Bhagat, G. Cormode, S. Muthukrishnan, I. Rozenbaum, and H. Xue. No blog is an island analyzing connections across information networks. In International Conference on Weblogs and Social Media, 2007.
[116] G. Cormode, S. Muthukrishnan, and W. Zhuang. Conquering the divide: Continuous clustering of distributed data streams. In International Conference on Data Engineering (ICDE), 2007.
[117] A. Chakrabarti, G. Cormode, and A. McGregor. A near-optimal algorithm for computing the entropy of a stream. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2007.
[118] G. Cormode and S. Muthukrishnan. Combinatorial algorithms for compressed sensing. In SIROCCO, 2006.
[119] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Space- and time-efficient deterministic algorithms for biased quantiles over data streams. In ACM Principles of Database Systems (PODS), 2006.
[120] G. Cormode, R. Keralapura, and J. Ramimirtham. Communication-efficient distributed monitoring of thresholded counts. In ACM SIGMOD International Conference on Management of Data (SIGMOD), 2006.
[121] G. Cormode, M. Garofalakis, and D. Sacharidis. Fast approximate wavelet tracking on streams. In Extending Database Technology, pages 4-22, 2006.
[122] G. Cormode, S. Muthukrishnan, and W. Zhuang. What's different: Distributed, continuous monitoring of duplicate-resilient aggregates on data streams. In International Conference on Data Engineering (ICDE), pages 20-31, 2006.
[123] G. Cormode, S. Muthukrishnan, and I. Rozenbaum. Summarizing and mining inverse distributions on data streams via dynamic inverse sampling. In International Conference on Very Large Data Bases (VLDB), pages 25-36, 2005.
[124] G. Cormode and M. Garofalakis. Sketching streams through the net: Distributed approximate query tracking. In International Conference on Very Large Data Bases (VLDB), pages 13-24, 2005.
[125] G. Cormode and S. Muthukrishnan. Space efficient mining of multigraph streams. In ACM Principles of Database Systems (PODS), pages 271-282, 2005.
[126] G. Cormode, M. Garofalakis, S. Muthukrishnan, and R. Rastogi. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 25-36, 2005.
[127] G. Cormode and S. Muthukrishnan. Summarizing and mining skewed data streams. In SIAM Conference on Data Mining (SDM), 2005.
[128] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Effective computation of biased quantiles over data streams. In International Conference on Data Engineering (ICDE), pages 20-31, 2005.
[129] G. Cormode and S. Muthukrishnan. Substring compression problems. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 321-330, 2005.
[130] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Diamond in the rough: Finding hierarchical heavy hitters in multi-dimensional data. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 155-166, 2004.
[131] G. Cormode, F. Korn, S. Muthukrishnan, T. Johnson, O. Spatscheck, and D. Srivastava. Holistic UDAFs at streaming speeds. In ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 35-46, 2004.
[132] G. Cormode. The hardness of the lemmings game, or Oh no, more NP-completeness proofs. In Proceedings of Third International Conference on Fun with Algorithms, pages 65-76, 2004.
[133] G. Cormode, A. Czumaj, and S. Muthukrishnan. How to increase the acceptance ratios of top conferences. In Proceedings of Third International Conference on Fun with Algorithms, pages 262-273, 2004.
[134] G. Cormode and S. Muthukrishnan. What's new: Finding significant differences in network data streams. In Proceedings of IEEE Infocom, pages 1534-1545, 2004.
[135] G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. In Proceedings of Latin American Theoretical Informatics (LATIN), pages 29-38, 2004.
[136] G. Cormode. Stable distributions for stream computations: it's as easy as 0,1,2. In Workshop on Management and Processing of Massive Data Streams at FCRC, 2003.
[137] G. Cormode and S. Muthukrishnan. What's hot and what's not: Tracking most frequent items dynamically. In ACM Principles of Database Systems (PODS), pages 296-306, 2003.
[138] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Finding hierarchical heavy hitters in data streams. In International Conference on Very Large Data Bases (VLDB), pages 464-475, 2003.
[139] G. Cormode and S. Muthukrishnan. Estimating dominance norms of multiple data streams. In European Symposium on Algorithms, volume 2838 of LNCS, 2003.
[140] G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. In International Conference on Very Large Data Bases (VLDB), pages 335-345, 2002.
[141] G. Cormode, P. Indyk, N. Koudas, and S. Muthukrishnan. Fast mining of tabular data via approximate distance computations. In International Conference on Data Engineering (ICDE), pages 605-616, 2002.
[142] G. Cormode and S. Muthukrishnan. The string edit distance matching problem with moves. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 667-676, 2002.
[143] G. Cormode, S. Muthukrishnan, and S. C. Sahinalp. Permutation editing and matching via embeddings. In International Colloquium on Automata, Languages and Programming (ICALP), volume 2076, pages 481-492, 2001.
[144] G. Cormode, M. Paterson, S. C. Sahinalp, and U. Vishkin. Communication complexity of document exchange. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 197-206, 2000.
[145] G. Ozsoyoglu, N. H. Balkir, G. Cormode, and Z. M. Ozsoyoglu. Electronic books in digital libraries. In Proceedings of IEEE Advances in Digital Libraries (ADL), pages 5-14, 2000.

Journal Publications

[1] A. Bharadwaj and G. Cormode. Federated computation: a survey of concepts and challenges. Distributed and Parallel Databases, 42(3):299-335, 2024.
[2] K. Prasad, S. Ghosh, G. Cormode, I. Mironov, A. Yousefpour, and P. Stock. Reconciling security and communication efficiency in federated learning. IEEE Data Engineering Bulletin, 46(1), 2023.
[3] G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and Veselý. Relative error streaming quantiles. Journal of the ACM (JACM), 70(5):1-48, 2023.
[4] G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and Veselý. Relative error streaming quantiles. SIGMOD Record, 51(1):66-79, Mar. 2022.
[5] G. Cormode. Current trends in data summaries. SIGMOD Record, 50(4):6-15, Jan. 2022.
[6] G. Cormode, C. Maple, and M. Scott. Aggregation and transformation of vector-valued messages in the shuffle model of differential privacy. IEEE Trans. Inf. Forensics Secur., 17:612-627, 2022.
[7] G. Cormode, T. Kulkarni, and D. Srivastava. Constrained private mechanisms for count data. IEEE Transactions on Knowledge and Data Engineering, 33(2):415-430, Feb. 2021.
[8] P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. A. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D'Oliveira, H. Eichner, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konečný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, H. Qi, D. Ramage, R. Raskar, M. Raykova, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao. Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2):1-210, 2021.
[9] K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth. Correlation clustering in data streams. Algorithmica, 83(7):1980-2017, 2021.
[10] G. Cormode and P. Veselý. Streaming algorithms for bin packing and vector scheduling. Theory of Computing Systems, 65(6):916-942, 2021.
[11] A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venktatasubramanian. Verifiable stream computation and Arthur-Merlin communication. SIAM Journal on Computing (SICOMP), 2019.
[12] G. Cormode and H. Jowhari. lp samplers and their applications: A survey. ACM Computing Surveys, 2018.
[13] G. Cormode, A. Dasgupta, A. Goyal, and C. H. Lee. An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs. PLOS ONE, 13(1):e0191175, 2018.
[14] J. Zhang, G. Cormode, M. Procopiuc, D. Srivastava, and X. Xiao. Privbayes: Private data release via bayesian networks. ACM Transactions on Database Systems, 2017.
[15] G. Cormode. Data sketching. Communications of the ACM (CACM), 60(9):48-55, 2017.
[16] G. Cormode and H. Jowhari. A second look at counting triangles in graph streams (revised). Theoretical Computer Science, 683:22-30, 2017.
[17] E. Cohen, G. Cormode, N. Duffield, and C. Lund. On the tradeoff between stability and fit. ACM Transactions on Algorithms, 13(1), 2016.
[18] A. Chakrabarti, G. Cormode, and A. McGregor. Robust lower bounds for communication and stream computation. Theory of Computing, 12(10):1-35, 2016.
[19] G. Luo, L. Wang, K. Yi, and G. Cormode. Quantiles over data streams: experimental comparisons, new analyses, and further improvements. The VLDB Journal, 25(4):449-472, 2016.
[20] K. Mirylenka, G. Cormode, T. Palpanas, and D. Srivastava. Conditional heavy hitters: detecting interesting correlations in data streams. The VLDB Journal, 24(3):395-414, 2015.
[21] S. Papadopoulos, G. Cormode, A. Deligiannakis, and M. N. Garofalakis. Lightweight query authentication on streams. ACM Transactions on Database Systems, 39(4):30:1-30:45, 2015.
[22] A. Chakrabarti, G. Cormode, A. McGregor, and J. Thaler. Annotations in data streams. ACM Transactions on Algorithms, 11(1), 2014.
[23] G. Cormode and D. Firmani. A unifying framework for l0-sampling algorithms. Distributed and Parallel Databases, 32(3):315-335, 2014. Special issue on Data Summarization on Big Data.
[24] G. Cormode. What does an associate editor actually do? SIGMOD Record, 42(2):52-58, June 2013.
[25] G. Cormode. The continuous distributed monitoring model. SIGMOD Record, 42(1), Mar. 2013.
[26] P. K. Agarwal, G. Cormode, Z. Huang, J. M. Phillips, Z. Wei, and K. Yi. Mergeable summaries. ACM Transactions on Database Systems, 38(4):26, 2013.
[27] A. Chakrabarti, G. Cormode, R. Kondapally, and A. McGregor. Information cost tradeoffs for augmented index and streaming language recognition. SIAM Journal on Computing (SICOMP), 42(1):61-83, 2013.
[28] G. Cormode, Q. Ma, S. Muthukrishnan, and B. Thompson. Socializing the h-index. Journal of Informetrics, 7(3):718 - 721, 2013.
[29] G. Cormode, M. Mitzenmacher, and J. Thaler. Streaming graph computations with a helpful advisor. Algorithmica, 65(2):409-442, 2013.
[30] G. Cormode, S. Muthukrishnan, and J. Yan. Studying the source code of scientific research. SIGKDD Explorations, 14(2):59-62, Dec. 2012.
[31] G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang. Continuous sampling from distributed streams. Journal of the ACM (JACM), 59(2), Apr. 2012.
[32] G. Cormode and S. Muthukrishnan. Approximating data with the count-min data structure. IEEE Software, 2012.
[33] G. Cormode, S. Muthukrishnan, and K. Yi. Algorithms for distributed functional monitoring. ACM Transactions on Algorithms, 7(2):1-21, 2011.
[34] G. Cormode, J. Jestes, F. Li, and K. Yi. Semantics of ranking queries for probabilistic data. IEEE Transactions on Knowledge and Data Engineering, 23(12):1903-1917, 2011.
[35] G. Cormode, B. Krishnamurthy, and W. Willinger. A manifesto for modeling and measurement in social media. First Monday, 15(9), Sept. 2010.
[36] G. Cormode and M. Garofalakis. Histograms and wavelets on probabilistic data. IEEE Transactions on Knowledge and Data Engineering, 22(8):1142-1157, Aug. 2010.
[37] A. Chakrabarti, G. Cormode, and A. McGregor. A near-optimal algorithm for computing the entropy of a stream. ACM Transactions on Algorithms, 6(3), 2010.
[38] R. Berinde, G. Cormode, P. Indyk, and M. Strauss. Space-optimal heavy hitters with strong error bounds. ACM Transactions on Database Systems, 35(4), 2010.
[39] G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings. The VLDB Journal, 19(1):115-139, 2010.
[40] G. Cormode and M. Hadjieleftheriou. Methods for finding frequent items in data streams. The VLDB Journal, 19(1):3-20, 2010.
[41] G. Cormode, S. Tirthapura, and B. Xu. Time-decaying sketches for robust aggregation of sensor data. SIAM Journal on Computing (SICOMP), 39(4):1309-1339, 2009.
[42] G. Cormode, S. Tirthapura, and B. Xu. Time-decayed correlated aggregates over data streams. Statistical Analysis and Data Mining, 2(5-6):294-310, 2009.
[43] K. Yi, F. Li, G. Cormode, M. Hadjieleftheriou, G. Kollios, and D. Srivastava. Small synopses for group-by query verification on outsourced data streams. ACM Transactions on Database Systems, 34(3), 2009.
[44] G. Cormode and M. Hadjieleftheriou. Finding the frequent items in streams of data. Communications of the ACM (CACM), 52(10):97-105, 2009.
[45] G. Cormode. How not to review a paper: The tools and techniques of the adversarial reviewer. SIGMOD Record, 37(4):100-104, Dec. 2008.
[46] G. Cormode and B. Krishnamurthy. Key differences between web 1.0 and web 2.0. First Monday, 13(6), June 2008.
[47] G. Cormode and M. Garofalakis. Approximate continuous querying over distributed streams. ACM Transactions on Database Systems, 33(2), June 2008.
[48] G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Finding hierarchical heavy hitters in streaming data. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(4), Jan. 2008.
[49] G. Cormode and S. Muthukrishnan. The string edit distance matching problem with moves. ACM Transactions on Algorithms, 3(1), 2007.
[50] G. Cormode and S. Muthukrishnan. What's new: Finding significant differences in network data streams. Transactions on Networking, 13(6):1219-1232, December 2005.
[51] G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. Journal of Algorithms, 55(1):58-75, April 2005.
[52] G. Cormode and S. Muthukrishnan. What's hot and what's not: Tracking most frequent items dynamically. ACM Transactions on Database Systems, 30(1):249-278, March 2005.
[53] G. Cormode. Representations of the research student in popular culture. Annals of Improbable Research, 10(1):26-27, 2004.
[54] G. Ozsoyoglu, N. H. Balkir, G. Cormode, and Z. M. Ozsoyoglu. Electronic books in digital libraries. IEEE Transactions on Knowledge and Data Engineering, 16(3):317-331, 2004.
[55] G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. IEEE Transactions on Knowledge and Data Engineering, 15(3):529-541, 2003.

Books and Theses
[1] G. Cormode and K. Yi. Small Summaries for Big Data. CUP, 2020.
[2] G. Cormode, M. Garofalakis, P. Haas, and C. Jermaine. Synopses for Massive Data: Samples, Histograms, Wavelets and Sketches. now publishers, 2012.
[3] G. Cormode and M. Thottan, editors. Algorithms for Next Generation Networks. Springer, 2010.
[4] J. Abello and G. Cormode, editors. Discrete Methods in Epidemiology, volume 70 of DIMACS. AMS, 2006.
[5] G. Cormode. Sequence Distance Embeddings. PhD thesis, University of Warwick, 2003.

Book Chapters, Technical Reports and Unrefereed Papers

[1] G. Cormode. Technical perspective: Toward building a differentially private DBMS. In Communications of the ACM (CACM), volume 68, page 92. 2025.
[2] G. Cormode. Technical perspective on 'OmniSketch: Streaming data analytics with arbitrary predicates'. In SIGMOD Record, volume 54, page 27. 2025.
[3] G. Cormode. Technical perspective on 'better differentially private approximate histograms and heavy hitters using the misra-gries sketch. In SIGMOD Record, volume 53(1), page 6. ACM, Mar. 2024.
[4] G. Cormode. Technical perspective on `R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys'. In SIGMOD Record, volume 52(1), page 114. ACM, Mar. 2023.
[5] G. Cormode. Gems of pods: Applications of sketching and pathways to impact. In ACM Principles of Database Systems (PODS). ACM, 2023.
[6] G. Cormode. Technical perspective: A framework for adversarially robust streaming algorithms. In SIGMOD Record, volume 50(1). ACM, Mar. 2021. Introduction to research highlights paper by Ben-Eliezer, Jayaram, Woodruff, and Yogev.
[7] G. Cormode. The true cost of popularity. In Communications of the ACM (CACM). ACM, July 2019. Introduction to research highlights paper by Larsen, Nelson, Nguyen and Thorup.
[8] G. Cormode. Technical perspective: εktelo: A framework for defining differentially-private computations. In SIGMOD Record, volume 48(1). ACM, Mar. 2019. Introduction to research highlights paper by Zhang, McKenna, Kotsogiannis, Bissias, Hay, Machanavajjhala and Gerome Miklau.
[9] G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan. The sparse awakens: Streaming algorithms for matching size estimation in sparse graphs. Technical report, ArXiV, 2016.
[10] G. Cormode and M. Garofalakis. Join sizes, frequency moments, and applications. In M. Garofalakis, J. Gehrke, and R. Rastogi, editors, Data Stream Management: Processing High-Speed Data Streams. Springer, 2016.
[11] G. Cormode and P. Indyk. Stable distributions in streaming computations. In M. Garofalakis, J. Gehrke, and R. Rastogi, editors, Data Stream Management: Processing High-Speed Data Streams. Springer, 2016.
[12] G. Cormode. Encyclopedia entry on 'count-min sketch'. In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer, 2015.
[13] G. Cormode. Encyclopedia entry on 'ams sketch'. In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer, 2015.
[14] G. Cormode. Encyclopedia entry on 'misra-gries summary'. In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer, 2015.
[15] G. Cormode. Continuous distributed monitoring: A short survey. In Paper accompanying invited talk at Algorithms and Models for Distributed Event Processing (AlMoDEP). ACM, Sept. 2011.
[16] G. Cormode. Sketch techniques for massive data. In G. Cormode, M. Garofalakis, P. Haas, and C. Jermaine, editors, Synposes for Massive Data: Samples, Histograms, Wavelets and Sketches, Foundations and Trends in Databases. NOW publishers, 2011.
[17] S. Bhagat, G. Cormode, and S. Muthukrishnan. Node classification in social networks. In C. C. Aggarwal, editor, Social Network Data Analytics. Springer, 2011.
[18] G. Cormode, J. Thaler, and K. Yi. Verifying computations with streaming interactive proofs. Technical Report TR10-159, Electronic Colloquium on Computational Complexity (ECCC), 2010.
[19] G. Cormode. Individual privacy vs population privacy: Learning to attack anonymization. Technical Report arXiv:1011.2511, arXiv, 2010.
[20] G. Cormode. Encyclopedia entry on 'Count-Min Sketch'. In L. Liu and M. T. Ozsu, editors, Encyclopedia of Database Systems, pages 511-516. Springer, 2009.
[21] G. Cormode and M. Garofalakis. Histograms and wavelets on probabilistic data. Technical Report arXiv:0806.1071, arXiv, 2008.
[22] G. Cormode, F. Korn, and S. Tirthapura. Time decaying aggregates in out-of-order streams. Technical Report 2007-10, Center for Discrete Mathematics and Computer Science (DIMACS), 2007.
[23] G. Cormode. Computational fundamentals of analyzing and mining data streams. In Workshop on Data Stream Analysis. 2007.
[24] G. Cormode and S. Muthukrishnan. Combinatorial algorithms for compressed sensing. In Proceedings of Conference on Information Sciences and Systems (CISS). IEEE, 2006. Invited submission.
[25] G. Cormode. Some key concepts in data mining - clustering. In Discrete Methods in Epidemiology, volume 70 of DIMACS, pages 2-9. AMS, 2006.
[26] G. Cormode and M. Garofalakis. Efficient strategies for continuous distributed tracking tasks. In IEEE Data Engineering Bulletin, pages 33-39. IEEE, March 2005.
[27] G. Cormode and S. Muthukrishnan. Combinatorial algorithms for compressed sensing. Technical Report 2005-40, Center for Discrete Mathematics and Computer Science (DIMACS), 2005.
[28] G. Cormode and S. Muthukrishnan. Towards an algorithmic theory of compressed sensing. Technical Report 2005-25, Center for Discrete Mathematics and Computer Science (DIMACS), 2005.
[29] G. Cormode, S. Muthukrishnan, and I. Rozenbaum. Summarizing and mining inverse distributions on data streams via dynamic inverse sampling. Technical Report 2005-11, Center for Discrete Mathematics and Computer Science (DIMACS), 2005.
[30] G. Cormode. The hardness of the lemmings game, or, “Oh no, more NP-Completeness proofs”. Technical Report 2004-11, Center for Discrete Mathematics and Computer Science (DIMACS), 2004.
[31] J. Abello and G. Cormode. Report on DIMACS working group meeting: Data mining and epidemiology, March 18-19, 2004. Technical Report 2004-37, Center for Discrete Mathematics and Computer Science (DIMACS), 2004.
[32] G. Cormode, A. Czumaj, and S. Muthukrishnan. How to increase the acceptance ratios of top conferences. Technical Report 2004-12, Center for Discrete Mathematics and Computer Science (DIMACS), 2004.
[33] G. Cormode and S. Muthukrishnan. Radial histograms for spatial streams. Technical Report 2003-11, Center for Discrete Mathematics and Computer Science (DIMACS), 2003.
[34] G. Cormode and S. Muthukrishnan. Estimating dominance norms of multiple data streams. Technical Report 2002-35, Center for Discrete Mathematics and Computer Science (DIMACS), 2002.
[35] G. Cormode and S. Muthukrishnan. The string edit distance matching problem with moves. Technical Report 2001-26, Center for Discrete Mathematics and Computer Science (DIMACS), 2001.
[36] G. Cormode. Topic dependencies for electronic books. (unpublished manuscript), 1999.
[37] G. Cormode. Springs and sound layouts. (unpublished manuscript), 1998.

Invited Talks and Tutorials

[1] Federated computation beyond learning, 2024. Talk at FedKDD workshop.
[2] Private data analysis over large populations, 2024. Talk at Birmingham and Cambridge.
[3] An introduction to federated computation, June 2022. Tutorial presented at SIGMOD and the Web Conference.
[4] Mergeable summaries, 2022. PODS 2022 Test-of-time award presentation.
[5] Frequency estimation in local and multiparty differential privacy, May 2021. Invited talk at Distributed and Private Machine Learning Workshop.
[6] New lower and upper bounds for quantile summary algorithms, Nov. 2020. IGAFIT colloquium and Bar-Ilan University Colloquium.
[7] Towards federated analytics with local differential privacy, Oct. 2020. Talk at Facebook and Google.
[8] Scaling up by scaling down, Feb. 2020. Presentation at the Alan Turing Institute workshop on Data Science and AI at Scale.
[9] Distributed private data collection at scale, Jan. 2020. Talks at Amazon Research and Samsung Research, Cambridge.
[10] Local differential privacy: Solution or distraction?, June 2019. Talk at Google Workshop on Federated Learning and Analytics.
[11] Data science and privacy preservation, June 2019. Tutorial at Trust in Data Science Summer School in Ghent.
[12] Distributed private data collection at scale, 2019. Talk at Edinburgh University, University of Washington.
[13] Data summarization for machine learning, Jan. 2019. Talk at Computer Science Research Week 2019, National University of Singapore.
[14] Data summarization and distributed computation, 2018. Keynote talk at PODC 2018.
[15] G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang. Privacy at scale: Local differential privacy in practice, 2018. Tutorial at SIGMOD and KDD.
[16] Distributed private data collection at scale, Jan. 2018. Talk at HiPEDs CDT (Imperial).
[17] Locally private release of marginal statistics, Nov. 2017. Talk at Google (Zurich) Algorithms and Optimization Day.
[18] The confounding problem of private data release, Sept. 2017. Talk at EPSRC Workshop on Future Research Directions in Demand Management, Oxford University (CS).
[19] Engineering streaming algorithms, June 2017. Invited talk at Symposium on Experimental Algorithms.
[20] Engineering privacy for small groups, Nov. 2016. Talk at Isaac Newton Institute.
[21] Matching and covering in streaming graphs, Sept. 2016. Invited keynote talk at DISC 2016.
[22] The confounding problem of private data release, Sept. 2016. Invited talk at Heilbronn Conference; WMG; Liverpool University.
[23] Sub-quadratic recovery of correlated pairs, June 2016. Talk at Google Research, Facebook, Simons Institute, Manchester U., LSE.
[24] Compact summaries over large datasets, May 2015. Invited tutorial in PODS 2015 and BICOD 2015.
[25] Trusting the cloud with practical interactive proofs, Apr. 2015. Talk at Google NYC, Bristol, Oxford Algorithms Day, Durham.
[26] The confounding problem of private data release, Mar. 2015. Invited talk at EDBT/ICDT 2015.
[27] Sampling for big data, Aug. 2014. Tutorial at SIGKDD 2014 conference.
[28] Sketches, streaming and big data, July 2014. Summer school on Hashing at University of Copenhagen.
[29] Differentially private mechanisms for data release, March 2014. Talk at Hamilton Institute; Edinburgh University; Yahoo! Research, New York.
[30] Sketch data structures and concentration bounds / mergeable summaries, Sept. 2013. Invited tutorial at Yandex conference.
[31] Streaming, sketching and sufficient statistics, Sept. 2013. Invited tutorial at Big Data Boot Camp, Simons Institute for Theoretical Computer Science, Berkeley.
[32] Summary data structures for massive data, July 2013. Invited talk in Session on Data Streams and Compression, Computability in Europe 2013.
[33] Computing + statistics = data science, June 2013. An introduction to data science for teenagers, IGGY DUX awards, Experience Warwick.
[34] Streaming verification of outsourced computation, May 2013. Talk at Big Data Analytics Workshop, Microsoft Research Cambridge, and University of Warwick.
[35] Building blocks of privacy: Differentially private mechanisms, Apr. 2013. Invited tutorial talk at Privacy Preserving Data Publication and Analysis (PrivDB) workshop.
[36] Privacy and big data: Challenges and promise, Mar. 2013. Invited panel at NYU Abu Dhabi conference on Big Data Systems, Applications, and Privacy.
[37] Current industry trends in computer science research, Mar. 2013. Invited Talk/Panel at NSF Research Experience for Undergraduates PI Meeting.
[38] Data-driven concerns in private data release, Sept. 2012. Talk at Stevens Institute of Technology; AT&T Labs; UMass Amherst; Rutgers University-Newark; Bell Labs; NYU-Abu Dhabi.
[39] Sketches: Past, present and future, 2012. Invited Panel on Sketching and Streaming at SAMSI Workshop, 2012.
[40] Small summaries for Big Data, 2012. Talk at Duke ARO workshop on Big Data at Large; MSR Cambridge; Princeton.
[41] Continuous distributed monitoring: A short survey, Sept. 2011. Invited keynote at Algorithms and Models for Distributed Event Processing (AlMoDEP).
[42] Some sketchy results, May 2011. Talk at DIMACS Workshop on Algorithms in the Field (8F).
[43] Mergeable summaries, Apr. 2011. Talk at Harvard University; DIMACS; Johns Hopkins; University of Pennsylvania; AT&T Labs; Warwick University.
[44] Data anonymization, Mar. 2011. Guest lecture in 'Dealing with Massive Data' at Columbia University.
[45] Distributed summaries, 2011. Talk at DIMACS workshop on Parallelism: a 2020 vision.
[46] G. Cormode and D. Srivastava. Anonymized data: Generation, models, usage, Mar. 2010. Tutorial at ICDE 2010.
[47] Sipping from the firehose: Streaming interactive proofs for verifying computations, February 2010. Invited talk at Bristol Algorithms Days 2010; University of Maryland.
[48] Progress in data anonymization: from k-anonymity to the minimality attack, February 2010. Talk in Bristol.
[49] Anonymization and uncertainty in social network data, Oct. 2009. Invited talk at DBIR Day 2009 at NYU Poly.
[50] G. Cormode and D. Srivastava. Anonymized data: Generation, models, usage, July 2009. Tutorial at SIGMOD 2009.
[51] Processing graph streams: Upper and lower bounds, June 2009. Talk at Workshop on Algorithms and Models for Complex Networks, Bristol UK.
[52] Finding frequent items in data streams, March 2009. Talk at DIMACS Working group on Streaming, Coding and Compressive Sensing; AT&T Labs; UMass Amherst; Dartmouth College.
[53] On 'selection and sorting with limited storage', Sept. 2008. Talk at Mike66 Workshop celebrating Mike Paterson.
[54] Algorithms for distributed functional monitoring, Aug. 2008. Talk at Dagstuhl Seminar on Sublinear Algorithms.
[55] Data stream algorithms, July 2008. Tutorial at Bristol Summer School on Probabilistic Techniques in Computer Science.
[56] G. Cormode and M. Garofalakis. Streaming in a connected world: Querying and tracking distributed data streams, March 2008. Tutorial at VLDB 2006, SIGMOD 2007, EDBT 2008.
[57] Analyzing web 2.0, blogs and social networks, Dec. 2007. Talk at AT&T Labs.
[58] Computational fundamentals of analyzing and mining data streams, March 2007. Tutorial at Workshop on Data Stream Analysis, Caserta, Italy.
[59] Computing the entropy of a stream, December 2006. AT&T Labs; Bell Labs; DyDAn Center.
[60] A compact survey of compressed sensing, December 2006. Workshop on Algorithms for Data Streams, IIT Kanpur, India.
[61] Biased quantiles, June 2006. Bertinoro.
[62] Cluster and data stream analysis, March 2006. Tutorial at DIMACS Workshop on Data Mining and Epidemiology.
[63] Tracking inverse distributions of massive data streams, July 2005. Network Sampling Workshop in Paris, Bell Labs Research Seminar.
[64] Towards an algorithmic theory of compressed sensing, July 2005. Schloss Dagstuhl.
[65] Summarizing and mining skewed data streams, May 2005. NJIT.
[66] Algorithms for processing massive data at network line speed, March 2004. Talk at U. Iowa; U. Minnesota; Dartmouth; Google; AT&T; CWRU; Poly.
[67] How hard are computer games?, February 2004. Talk at DIMACS.
[68] What's hot, what's not, what's new and what's next, October 2003. Bell Labs; DIMACS Mixer at AT&T Labs.
[69] Zeroing in on the l0 metric, August 2003. DIMACS Workshop on Discrete Metric Spaces and their Algorithmic Applications at Princeton.
[70] Tracking frequent items dynamically, 2003. Institute of Advanced Studies; DIMACS; Stonybrook; U. Pennsylvania.
[71] Algorithmic embeddings for comparing large text streams, June 2002. CCR/DIMACS Workshop/Tutorial on Mining Massive Data Sets and Streams: Mathematical Methods and Algorithms for Homeland Defense.
[72] Embeddings of metrics on strings and permuations, March 2002. Workshop on Discrete Metric Spaces and their Algorithmic Applications in Haifa, Israel; BCTCS.
[73] Short string signatures, September 2000. DIMACS Workshop on Sublinear Algorithms in Princeton, NJ.

See also the listings from the dblp and their co-authors list. Google scholar page. Microsoft Academic Search page.

Programme Committees and Workshops

Editorships, Current Program Committees and Workshops: Consider submitting your papers and giving me something to read!

Previous Editorships Program Committees and Workshops:
  1. ICML 2025 (area chair)
  2. SynthData ICLR 2025
  3. EDBT Industrial 2025
  4. PPAI 2025
  5. AISTATS 2025
  6. SIGMOD 2025
  7. ICML 2024
  8. ICDCS 2024
  9. PPAI 2024
  10. FL at ICML 2023
  11. ICDT 2024 (PC Chair)
  12. VLDB 2023
  13. ICML 2023
  14. SIGMOD 2023
  15. SIGMOD 2022
  16. FL-ICML
  17. PPML
  18. IJCAI 2021
  19. PPAI 2021
  20. ICDE 2021
  21. SIGMOD 2021
  22. VLDB 2021
  23. Privacy Preserving Machine Learning -- PriML & PPML joint edition 2020
  24. Privacy in statistical databases 2020
  25. Fun with Algorithms 2020/21
  26. AAAI Workshop on Privacy-preserving Artificial Intelligence
  27. PPML 2019
  28. WPES 2019
  29. TPDP 2019
  30. SIGMOD 2020
  31. PODS 2020
  32. ICDT 2020
  33. Associate Editor for ACM Transactions on Database Systems (2012--2018)
  34. Associate editor for Journal of Discrete Algorithms (2015--2019)
  35. ICDE 2019 (Area Vice Chair)
  36. ICDE 2019 (Industrial track)
  37. Privacy Preserving Machine Learning (NIPS Workshop) 2018
  38. SIGMOD 2018
  39. PODS 2018
  40. SIGMOD 2017
  41. PODS 2016
  42. KDD 2016
  43. ICDT 2016
  44. ICALP 2016
  45. BeyondMR 2016
  46. PETS 2015/16
  47. TPDP 15
  48. ICDE 2015
  49. EDBT 2015
  50. CIKM 2014: Chair, Mining Big Data and Streams Track
  51. Associate Editor for IEEE Transactions on Knowledge and Data Engineering (2010-14)
  52. VLDB 2014 (Senior Program Committee)
  53. Workshop on Privacy in the Electronic Society 2014
  54. ICDT 2014
  55. COSN 2013: ACM Conference on Social Networks
  56. BD3: Workshop on Big, Dynamic, Distributed Data (PC co-chair)
  57. Workshop on Privacy in the Electronic Society 2013
  58. ACM STOC 2013
  59. ACM-SIAM ALENEX 2013
  60. IEEE ICDE 2013
  61. ACM SIGMOD 2013
  62. Workshop on Online Social Systems (WOSS 2012)
  63. Workshop on Databases and Social Networks (DBSocial12)
  64. Proceedings of the VLDB Endowment (2009-2012)
  65. ACM PODS 2012
  66. IEEE ICDE 2012
  67. ESA 2011
  68. DBSocial 2011
  69. MASSIVE 2011
  70. KDD 2011
  71. North-east DBIR day October 22nd 2010
  72. ICDT 2011
  73. ICDE 2011
  74. Middleware 2010
  75. CIKM 2010
  76. WOSN 2010
  77. VLDB 2010 -- Industrial Applications and Experience Track
  78. Program co-chair of Second Workshop on Management and mining of Uncertain Data (MOUND 2010)
  79. Group Leader, SIGMOD 2010
  80. PC Member, PODS 2010
  81. WOSN 2009
  82. ICDE 2009
  83. International Workshop on Analysis of Dynamic Networks
  84. CIKM 2008 Track on Streaming, Sensor Networks and Networking Databases
  85. Bristol Summer School on Probabilistic Techniques in Computer Science
  86. VLDB 2008
  87. NetDB 2008
  88. PODS 2008
  89. SSPS 2008
  90. DIMACS Tutorial on Algorithms for Next Generation Networks (Aug 6-8 2007)
  91. MineNet 2007
  92. NSF panel, 2007
  93. VLDB 2007
  94. European Workshop on Data Stream Analysis (WDSA), in Naples, March 14-16 2007
  95. DISN 2007
  96. ICDM 2006
  97. DAWAK 2006
  98. DMSN'06
  99. MineNet 2006
  100. DIMACS Tutorial on Data Mining, March 2006
  101. ICSNW 2006
  102. ICDM 2005
  103. VLDB 2005
  104. ICDE 2005
  105. KDD 2004
  106. ICDM 2004
  107. DIMACS Working Group on Data Mining and Epidemiology, March 2004

Teaching

Autumn 2023, 2024: CS260 Algorithms, Warwick University
Spring 2015: CS346 Advanced Databases, Warwick University
Spring 2014: CS131 Mathematics for Computer Scientists 2 (part 1), Warwick University
Autumn 2013, 2014, 2015: CS910 Foundations of Data Analytics, Warwick University.
Summer 2012: Distributed Streaming Algorithms Course at MADALGO Summer School -- follow link for slides

Spring 2011: Map Reduce Algorithms Seminar Class at Rutgers on Map Reduce and related topics.

Summer 2008: Streaming algorithms at Bristol Summer school on Probabilistic Techniques in Computer Science A short course on streaming algorithms - see above for slides, write-up, and video.

Spring 2006: Blog Data Analysis A seminar class on blog (weblog) data.

Summer 2003, Summer 2004, Summer 2006: Mentor in the DIMACS REU Program (2003) (2004)

Spring 2003: Processing Massive Data Sets. 4 x 2 hour lectures on recent research on data streams, including maintaining frequent itemsets, computing distinct items and clustering on the stream. Slides available from the class page.


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