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Matthew Might

Researcher at University of Alabama at Birmingham

Publications -  144
Citations -  2905

Matthew Might is an academic researcher from University of Alabama at Birmingham. The author has contributed to research in topics: Abstract interpretation & Garbage collection. The author has an hindex of 27, co-authored 130 publications receiving 2275 citations. Previous affiliations of Matthew Might include Georgia Institute of Technology & University of Utah.

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Journal ArticleDOI

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

Julia Wang, +185 more
TL;DR: MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.
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The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease

Rachel B. Ramoni, +179 more
TL;DR: The Undiagnosed Diseases Network is extended nationally to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.
Proceedings ArticleDOI

Abstracting abstract machines

TL;DR: It is demonstrated that the derivational approach to abstract interpretation that yields novel and transparently sound static analyses when applied to well-established abstract machines scales up uniformly to allow static analysis of realistic language features, including tail calls, conditionals, side effects, exceptions, first-class continuations, and even garbage collection.
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Metformin Use Is Associated With Reduced Mortality in a Diverse Population With COVID-19 and Diabetes.

TL;DR: In this paper, a retrospective electronic health record data analysis of 25,326 subjects tested for COVID-19 between 2/25/20 and 6/22/20 at the University of Alabama at Birmingham Hospital, a tertiary health care center in the racially diverse Southern U.S.
Proceedings ArticleDOI

Improving flow analyses via ΓCFA: abstract garbage collection and counting

TL;DR: Two independent and complementary improvements for flow-based analysis of higher-order languages: abstract garbage collection and abstract counting are presented, collectively titled ΓCFA.