2008 - ACM Fellow For contributions to programming language theory and systems.
Alex Aiken mostly deals with Programming language, Parallel computing, Theoretical computer science, Data mining and Static analysis. His Programming language research incorporates elements of Algorithm, Type and Benchmark. His Parallel computing research includes elements of Compiler, Memory leak, Memory management and Programming paradigm.
He has researched Theoretical computer science in several fields, including Type theory, Reliability, Linux kernel and Of the form. Alex Aiken has included themes like Cryptovirology, Isolation and Debugging in his Data mining study. His research in Static analysis intersects with topics in State, Active database and Code.
Alex Aiken spends much of his time researching Programming language, Theoretical computer science, Parallel computing, Program analysis and Compiler. His work in Programming language is not limited to one particular discipline; it also encompasses Code. The study incorporates disciplines such as Scalability, Software pipelining and Memory management in addition to Parallel computing.
His Memory management study frequently draws parallels with other fields, such as Memory map. His studies in Program analysis integrate themes in fields like Algorithm, Type inference and Constraint. Static analysis is closely attributed to Android in his study.
Scalability, Parallel computing, Programming language, Parallelism and Artificial intelligence are his primary areas of study. His Scalability study incorporates themes from Supercomputer, Task analysis and Data mining. Alex Aiken regularly links together related areas like Program analysis in his Parallel computing studies.
His study in the fields of Compiler, Correctness and Unit testing under the domain of Programming language overlaps with other disciplines such as Atlas. His Parallelism study also includes
His primary areas of study are Scalability, Parallelism, Parallel computing, Artificial intelligence and Deep learning. His Scalability research includes themes of SOAP, Sample and Deep neural networks. The various areas that Alex Aiken examines in his Parallelism study include Layer and Convolutional neural network.
His Convolutional neural network research is multidisciplinary, relying on both Dimension, Process, Parallelizable manifold, Granularity and Speedup. His work on Artificial neural network is typically connected to Superoptimization as part of general Artificial intelligence study, connecting several disciplines of science. His Deep learning research includes elements of Automated theorem proving, Theoretical computer science and Graph.
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Winnowing: local algorithms for document fingerprinting
Saul Schleimer;Daniel S. Wilkerson;Alex Aiken.
international conference on management of data (2003)
A First Step Towards Automated Detection of Buffer Overrun Vulnerabilities.
David A. Wagner;Jeffrey S. Foster;Eric A. Brewer;Alexander Aiken.
network and distributed system security symposium (2000)
Scalable statistical bug isolation
Ben Liblit;Mayur Naik;Alice X. Zheng;Alex Aiken.
programming language design and implementation (2005)
Bug isolation via remote program sampling
Ben Liblit;Alex Aiken;Alice X. Zheng;Michael I. Jordan.
programming language design and implementation (2003)
Titanium: a high-performance Java dialect
Katherine A. Yelick;Katherine A. Yelick;Luigi Semenzato;Luigi Semenzato;Geoff Pike;Geoff Pike;Carleton Miyamoto;Carleton Miyamoto.
Concurrency and Computation: Practice and Experience (1998)
Sequoia: programming the memory hierarchy
Kayvon Fatahalian;Daniel Reiter Horn;Timothy J. Knight;Larkhoon Leem.
conference on high performance computing (supercomputing) (2006)
Effective static race detection for Java
Mayur Naik;Alex Aiken;John Whaley.
Static detection of security vulnerabilities in scripting languages
Yichen Xie;Alex Aiken.
usenix security symposium (2006)
Legion: expressing locality and independence with logical regions
Michael Bauer;Sean Treichler;Elliott Slaughter;Alex Aiken.
ieee international conference on high performance computing data and analytics (2012)
Flow-sensitive type qualifiers
Jeffrey S. Foster;Tachio Terauchi;Alex Aiken.
programming language design and implementation (2002)
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