אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Deep Neural Networks are succeeding at a range of natural language tasks such as machine translation and text summarization. Recently, the interdisciplinary field of "big code" promises a new set of learnable statistical static analyses. While machine learning tasks on source code have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this talk, I discuss how graph neural networks that use code's syntactic and semantic structured information can detect variable misuses in code without any external information (e.g. unit tests).

Bio:
Miltos Allamanis (https://miltos.allamanis.com) is a researcher at Microsoft Research, Cambridge. He is interested in applications of machine learning and natural language processing to software engineering and programming languages to create smart software engineering tools for developers. Previously, he was a PhD student at the University of Edinburgh advised by Dr. Charles Sutton.