In 2011, Sadrzadeh was awarded an
Engineering and Physical Sciences Research Council Career Acceleration Fellowship. She was appointed to the faculty at
Queen Mary University of London, where she combined statistical and logical methods to study how language works. Whilst
machine learning can improve reasoning about textual data, systems making use of machine learning cannot be translated to all applications. Sadrzadeh develops tensor-based mathematical models to improve these processes by combining logic, statistics and machine learning to strengthen the information from textual data. These models are based on the
DisCoCat framework that she introduced with
Bob Coecke and Stephen Clark. She was awarded two industrial fellowships from the
Royal Academy of Engineering, which allowed her to build partnerships with the
BBC. In particular she concentrated on the development of tensorial analysis for textual understanding of subtitles and news. At the time it was estimated that the average adult spends about one and a half years of their lives trying to device what to watch on broadcasting platforms. Sadrzadeh is involved in the conference SemSpace (Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science). In 2020, Sadrzadeh gave a talk "Gaussianity and typicality in matrix distributional semantics" and in 2021 she was co-organiser. In 2022, Sadrzadeh was awarded a
Royal Academy of Engineering Senior Fellowship. == Selected publications ==