What is he best known for?
The fields of study he is best known for:
- Programming language
- Statistics
- Algorithm
Frank McSherry spends much of his time researching Differential privacy, Data mining, Algorithm, Information privacy and Mathematical optimization.
His Differential privacy study integrates concerns from other disciplines, such as Extensibility, Privacy preserving and Scope.
His studies in Data mining integrate themes in fields like Consistency, Raw data and Set.
His biological study deals with issues like Row, which deal with fields such as Theoretical computer science, Inference attack and Range query.
His Information privacy research incorporates themes from Function, Common value auction, Combinatorial auction and Fixed price.
His research investigates the connection between Mathematical optimization and topics such as Mechanism design that intersect with issues in Unit demand and Strategic dominance.
His most cited work include:
- Calibrating noise to sensitivity in private data analysis (3875 citations)
- Mechanism Design via Differential Privacy (1286 citations)
- Our data, ourselves: privacy via distributed noise generation (1005 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Differential privacy, Computation, Theoretical computer science and Distributed computing are his primary areas of study.
His work carried out in the field of Data mining brings together such families of science as Function, Set and Information retrieval.
His Differential privacy research is multidisciplinary, relying on both Facility location problem, Approximation algorithm and Steiner tree problem.
His Computation research is multidisciplinary, incorporating elements of Discrete mathematics, Parallel computing, Low-rank approximation, Eigenvalues and eigenvectors and Scaling.
The various areas that Frank McSherry examines in his Theoretical computer science study include Distributed algorithm, Graph database, Graph and Markov chain.
His studies deal with areas such as Scalability, Graph and Dataflow as well as Distributed computing.
He most often published in these fields:
- Data mining (21.57%)
- Differential privacy (21.57%)
- Computation (21.57%)
What were the highlights of his more recent work (between 2012-2020)?
- Distributed computing (13.73%)
- Dataflow (9.80%)
- Computation (21.57%)
In recent papers he was focusing on the following fields of study:
Frank McSherry mostly deals with Distributed computing, Dataflow, Computation, Graph and Stateful firewall.
His Distributed computing research incorporates elements of Scalability, Thread, Dataflow architecture, Parallelizable manifold and Big data.
His studies in Dataflow integrate themes in fields like Network topology, Tuple and Data stream mining.
His Computation research is included under the broader classification of Algorithm.
The study incorporates disciplines such as Flow control, Scheduling, Stream processing and Modular design in addition to Graph.
His work on Datalog is typically connected to Generalization as part of general Theoretical computer science study, connecting several disciplines of science.
Between 2012 and 2020, his most popular works were:
- Naiad: a timely dataflow system (472 citations)
- Scalability! but at what cost? (194 citations)
- Differential dataflow (119 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Statistics
- Algorithm
His scientific interests lie mostly in Distributed computing, Data mining, Sensitivity, Differential privacy and Differential.
His Distributed computing study incorporates themes from Scalability, Graph, Thread, Parallelizable manifold and Big data.
Frank McSherry integrates many fields, such as Data mining and Noise, in his works.
His Sensitivity research encompasses a variety of disciplines, including Row, Measure, Set, Noise and Function.
Frank McSherry has researched Differential in several fields, including Strongly connected component, Data stream mining and Theoretical computer science.
His Strongly connected component research includes themes of Computation and Dataflow.
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