Research Interests
To understand and reason about code, we (=humans) combine its highly structured,
formal nature with ambiguous information in the identifier names, comments,
and coding idioms. Can we devise computational methods that replicate this
form of reasoning? I research machine learning models and methods
that "understand" and generate code. My objective is to invent better machine
learning methods for semi-structured reasoning, and to inspire novel software
engineering tools that will assist developers in their work.
I am currently a principal researcher at Microsoft Research in Cambridge, UK
and part of the
Deep Program Understanding
project in the Machine Intelligence group.
Highlighted Publications
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Typilus: Neural Type Hints
M. Allamanis, E. T. Barr, S. Ducousso, Z. Gao. PLDI 2020
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Fast and Memory-Efficient Neural Code Completion
A. Svyatkovskiy, S. Lee, A. Hadjitofi, M. Riechert, J. Franco, M. Allamanis. 2020
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Learning to Represent Edits
P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, A. L. Gaunt. ICLR 2019
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The Adverse Effects of Code Duplication in Machine Learning Models of Code
M. Allamanis. SPLASH Onward! 2019
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A Survey of Machine Learning for Big Code and Naturalness
M. Allamanis, E. T. Barr, P. Devanbu, C. Sutton. ACM Computing Surveys 2018
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Learning to Represent Programs with Graphs
M. Allamanis, M. Brockscmidt, M. Khademi. ICLR 2018
Full list of publications