2020 - ACM Fellow For contributions to data summarization and privacy enabling data management and analysis
2013 - ACM Distinguished Member
His primary areas of investigation include Data mining, Data stream mining, Theoretical computer science, Data stream and Algorithm. In the field of Data mining, his study on Aggregate overlaps with subjects such as Focus. His work carried out in the field of Data stream mining brings together such families of science as Zipf's law, Distributed algorithm, Cluster analysis, Range and Data set.
His Theoretical computer science study combines topics in areas such as Uncertain data, Probabilistic logic and Graph. The various areas that Graham Cormode examines in his Data stream study include Skewness and Data warehouse. His Algorithm research is multidisciplinary, relying on both Entropy and Wireless sensor network.
His primary areas of study are Data mining, Theoretical computer science, Data stream mining, Data stream and Algorithm. His work in the fields of Uncertain data overlaps with other areas such as Tuple. His work on Communication complexity is typically connected to Streaming algorithm as part of general Theoretical computer science study, connecting several disciplines of science.
His biological study spans a wide range of topics, including Distributed computing, Wireless sensor network, Quantile, Approximation algorithm and Automatic summarization. While working in this field, Graham Cormode studies both Data stream and Point. His Algorithm study incorporates themes from Range and Hash function, Count–min sketch.
Differential privacy, Combinatorics, Streaming algorithm, Algorithm and Discrete mathematics are his primary areas of study. His Differential privacy research also works with subjects such as
He has included themes like Range, Hash function and Graph in his Algorithm study. His work focuses on many connections between Graph and other disciplines, such as Data set, that overlap with his field of interest in Data stream mining. His Data stream mining research is within the category of Data mining.
Graham Cormode mainly investigates Differential privacy, Combinatorics, Streaming algorithm, Data collection and Federated learning. Differential privacy is a subfield of Data mining that he investigates. When carried out as part of a general Combinatorics research project, his work on Pointer jumping, Maximal independent set, Independent set and Vertex is frequently linked to work in Quadratic equation, therefore connecting diverse disciplines of study.
His Pointer jumping research includes themes of Vertex model and Tilde. His Data collection research incorporates themes from Key, Scale and Internet privacy. His Federated learning research is multidisciplinary, incorporating elements of Training set, World Wide Web, Mobile device, Service provider and Orchestration.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
An improved data stream summary: the count-min sketch and its applications
Graham Cormode;S. Muthukrishnan.
Journal of Algorithms (2005)
Key differences between Web 1.0 and Web 2.0
Graham Cormode;Balachander Krishnamurthy.
First Monday (2008)
What's hot and what's not: tracking most frequent items dynamically
Graham Cormode;S. Muthukrishnan.
symposium on principles of database systems (2003)
Advances and Open Problems in Federated Learning
Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet.
Foundations and Trends® in Machine Learning (2021)
Advances and Open Problems in Federated Learning
Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet.
arXiv: Learning (2019)
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Graham Cormode;Minos Garofalakis;Peter J. Haas;Chris Jermaine.
(2012)
Node Classification in Social Networks
Smriti Bhagat;Graham Cormode;S. Muthukrishnan.
Social Network Data Analytics (2011)
Differentially Private Spatial Decompositions
Graham Cormode;Cecilia Procopiuc;Divesh Srivastava;Entong Shen.
international conference on data engineering (2012)
Finding frequent items in data streams
Graham Cormode;Marios Hadjieleftheriou.
very large data bases (2008)
Anonymizing bipartite graph data using safe groupings
Graham Cormode;Divesh Srivastava;Ting Yu;Qing Zhang.
very large data bases (2008)
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