About

Anna Korhonen is a Professor of Natural Language Processing at the University of Cambridge where she co-directs the Language Technology Laboratory (LTL). She is also the director of the Centre for Human-Inspired Artificial Intelligence (CHIA) and the co-founder and co-director of the Institute for Technology and Humanity. She is a Senior Research Fellow of Churchill College, a Fellow of ACL and a Fellow of ELLIS. She is also a chair elect of EACL, the European Chapter of ACL, and previously served as a Turing Fellow of the Alan Turing Institute.

Anna’s research focuses on Natural Language Processing (NLP) and other areas of AI, specifically on developing, adapting, and applying computational techniques to meet the needs of intelligent applications. She has a particular interest in responsible, human-centric technologies that are informed by an understanding of human cognitive, social, and creative intelligence, with a focus on applications that promote social and global good. Many of the projects are interdisciplinary and involve collaboration with researchers from various fields.

Prior to starting her current position, she was a Royal Society University Research Fellow in this University, based in Computer Science and Linguistics. Before that, I was a JSPS postdoctoral fellow in Japan where she worked at the National Institute of Informatics in Tokyo. Previously, she was a researcher at University of Pennsylvania. Anna’s PhD is in Computer Science from the University of Cambridge and she holds masters degrees in both Computer Science and Linguistics.

Research Areas

Current research focuses on NLP and its many real-life applications (ranging from text mining to machine translation and dialogue systems) and on the development of responsible and human-centric NLP / AI aimed at social and global good. 

Some current areas of interest include:

• equitable, inclusive and culturally adapted NLP 

• human-centric NLP

• interpretability, explainability,  fairness and trustworthiness of NLP

• multilingual and low resource NLP

• dialogue systems and conversational AI 

• domain, task and language adaptation / transfer

• information extraction, text mining, knowledge discovery

• machine learning / deep learning 

• AI for data science (e.g., biomedical and cognitive sciences)

• NLP for health, climate and enviroment

• real-world and responsible applications of AI