Research Interests

Can we use machine learning to create better tools for software engineers? My research concerns machine learning models and methods that "understand" and generate source code. By learning from existing code, I aim to create useful machine learning-based software engineering tools, interfaces and insights. My research focuses on developer tools with a strong machine learning component, while using problems in this area to motivate machine learning research.

I am currently a senior researcher at Microsoft Research in Cambridge, UK and part of the Deep Program Understanding project.

Highlighted Publications

• Learning to Represent Edits
  P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, A. L. Gaunt. ICLR 2019
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
  H. Husain, H. Wu, T. Gazit, M. Allamanis, M. Brockschmidt. 2019
• The Adverse Effects of Code Duplication in Machine Learning Models of Code
  M. Allamanis. SPLASH Onward! 2019
• A Survey of Machine Learning for Big Code and Naturalness
  M. Allamanis, E. T. Barr, P. Devanbu, C. Sutton. ACM Computing Surveys 2018
• Learning to Represent Programs with Graphs
  M. Allamanis, M. Brockscmidt, M. Khademi. ICLR 2018

Full list of publications