About Me

Dr. Mengyang Qiu is an Assistant Professor in the Department of Speech-Language Pathology at Saint Elizabeth University, beginning in Spring 2026. Before joining SEU, he was an Assistant Professor in the Department of Psychology at Trent University from 2023 to 2025. Dr. Qiu earned his PhD in Communicative Disorders and Sciences (Cognitive Science Track) from the University at Buffalo in 2023, under the supervision of Drs. Nichol Castro and Brendan T. Johns. He also holds an MS in Computational Linguistics, an MA in Linguistics, and a BA in Psychology and Linguistics (Honors in Language and Cognition) from the University at Buffalo.

Dr. Qiu’s research includes two main lines of work. The first examines language and cognition, with an emphasis on cognitive aging. The second focuses on natural language processing in educational settings, specifically grammatical error correction.

In his research on language and cognition, Dr. Qiu uses computational modeling approaches such as distributional semantics and network science, together with behavioral experiments, to explore how differences in linguistic experience influence language and memory processing across aging. Core questions include how much variation in lexical performance, such as word recognition, can be explained by individual differences in language experience, and which types of test stimuli most accurately capture an individual’s linguistic knowledge. The long-term goal of this work is to delineate the natural changes that occur due to accumulated linguistic experience from actual cognitive decline in aging, supporting the development of more sensitive assessments for neurodegenerative disorders and their precursors, such as mild cognitive impairment.

Dr. Qiu’s research on natural language processing for educational applications spans the full pipeline of grammatical error correction (GEC). This work includes developing linguistically informed frameworks for error annotation and evaluation in English, Chinese, and other typologically diverse languages, as well as exploring how large language models can be integrated into different stages of the GEC pipeline. Across this line of research, his goal is to develop scalable and interpretable methods that better support language learning and instruction.