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SUMMARY:Designing Smart Software Engineering Tools with Machines Learning 
 - Dr Miltos Allamanis\, Microsoft Research
DTSTART:20170202T131000Z
DTEND:20170202T140000Z
UID:TALK70804@talks.cam.ac.uk
CONTACT:Janet Gibson
DESCRIPTION:Developing software is a costly process: software engineers ne
 ed to tackle the inherent complexity of software to avoid bugs\, reduce de
 velopment and maintenance costs\, and deliver software products on time. O
 ur society's reliance on software makes it imperative to research new tool
 s that help engineers construct more reliable and maintainable software. O
 ne new and promising research direction aims to use machine learning to de
 velop smart tools that learn patterns from existing code and transfer this
  knowledge to new projects by providing smart recommendations to software 
 engineers. In this talk\, I will present a brief overview of this area and
  my research and discuss interesting challenges within this field.\n\nBio:
  Miltos Allamanis is a postdoc at Microsoft Research\, Cambridge. His rese
 arch interests concern applying and creating new machine learning and prog
 ramming language methods to create novel and smart software engineering to
 ols. During his PhD —in the University of Edinburgh\, advised by Charles
  Sutton— he worked on machine learning models of source code and their a
 pplications in programming languages and software engineering. He holds an
  MPhil in Advanced Computer Science from the University of Cambridge\, UK 
 and a DipEng in Electrical and Computer Engineering from the Aristotle Uni
 versity of Thessaloniki\, Greece.
LOCATION:The Richard King Room\, Darwin College
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