2019 - ACM Paris Kanellakis Theory and Practice Award For seminal work on the foundations of streaming algorithms and their application to large scale data analytics.
2014 - IEEE Fellow For contributions to parallel computing and databases
2006 - ACM Fellow For contributions to parallel computing, databases, and sensor networks.
Phillip B. Gibbons spends much of his time researching Data mining, Parallel computing, Computer network, Distributed computing and Correctness. His Data mining study combines topics from a wide range of disciplines, such as Scheduling, Theoretical computer science and Set. His Parallel computing research includes themes of Dram, Hard disk drive performance characteristics and Distributed shared memory.
His work deals with themes such as Computer security, Alias and The Internet, which intersect with Computer network. He combines subjects such as Sequential consistency, Distributed memory, Shared memory, Cache coherence and Uniform memory access with his study of Distributed computing. His study in Correctness is interdisciplinary in nature, drawing from both Model of computation, Interface, Consistency model, Asynchronous communication and Robustness.
His primary areas of investigation include Parallel computing, Distributed computing, Cache, Parallel algorithm and Algorithm. His Parallel computing study incorporates themes from Dram, Scheduling and Asynchronous communication. In his research on the topic of Distributed computing, Protocol is strongly related with Key.
He regularly links together related areas like Speedup in his Cache studies. His research integrates issues of Discrete mathematics and Computation in his study of Parallel algorithm. His research in Shared memory focuses on subjects like Uniform memory access, which are connected to Interleaved memory and Memory map.
Parallel computing, Artificial intelligence, Cache, Distributed computing and Algorithm are his primary areas of study. His studies in Parallel computing integrate themes in fields like Dram and Compiler. His work in Artificial intelligence covers topics such as Machine learning which are related to areas like Key, Data modeling, Software system and Software deployment.
His research in Cache intersects with topics in Load balancing and Work stealing. His Distributed computing research integrates issues from Scratchpad memory, Porting, Programmer, Exploit and Programming paradigm. His study in the fields of Computation and Parallel algorithm under the domain of Algorithm overlaps with other disciplines such as Estimation.
Phillip B. Gibbons mostly deals with Parallel computing, Cache, Artificial intelligence, Key and Distributed computing. His research in the fields of Pipeline overlaps with other disciplines such as Throughput. His Pipeline research focuses on Range and how it relates to Computation.
His study in Cache is interdisciplinary in nature, drawing from both Memory-mapped file, Memory management and Shared memory. His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Normalization, Degree, Skew and Local area network. His research in Distributed computing intersects with topics in Elasticity, Reliability and Transient.
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.
Memory consistency and event ordering in scalable shared-memory multiprocessors
Kourosh Gharachorloo;Daniel Lenoski;James Laudon;Phillip Gibbons.
international symposium on computer architecture (1990)
LOCI: fast outlier detection using the local correlation integral
S. Papadimitriou;H. Kitagawa;P.B. Gibbons;C. Faloutsos.
international conference on data engineering (2003)
SybilGuard: defending against sybil attacks via social networks
Haifeng Yu;Michael Kaminsky;Phillip B. Gibbons;Abraham D. Flaxman.
acm special interest group on data communication (2006)
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
Haifeng Yu;Phillip B. Gibbons;Michael Kaminsky;Feng Xiao.
ieee symposium on security and privacy (2008)
Synopsis diffusion for robust aggregation in sensor networks
Suman Nath;Phillip B. Gibbons;Srinivasan Seshan;Zachary Anderson.
ACM Transactions on Sensor Networks (2008)
IrisNet: an architecture for a worldwide sensor Web
P.B. Gibbons;B. Karp;Y. Ke;S. Nath.
IEEE Pervasive Computing (2003)
New sampling-based summary statistics for improving approximate query answers
Phillip B. Gibbons;Yossi Matias.
international conference on management of data (1998)
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Qirong Ho;James Cipar;Henggang Cui;Seunghak Lee.
neural information processing systems (2013)
Join synopses for approximate query answering
Swarup Acharya;Phillip B. Gibbons;Viswanath Poosala;Sridhar Ramaswamy.
international conference on management of data (1999)
System and method for scheduling and controlling delivery of advertising in a communications network
Micah Alexei Adler;Phillip B. Gibbons;Yossi Matias.
Profile was last updated on December 6th, 2021.
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