I am Co-Director of the Human Language Analysis Beings (HLAB) at the College of Connected Computing (www.humanlanguage.org), where we develop human-centered NLP and machine-learning methods to advance AI language understanding and enable more accurate, data-driven assessment of mental health.
I work at the intersection of psychology and computer science to develop and validate methods for assessing psychological constructs and understanding psychological processes. In my research, I combine psychological theory, psychometrics, and artificial intelligence, including natural language processing and machine learning. A central aim of my work is to develop language-based methods that are useful and trustworthy in real-world settings. This involves careful validation (e.g., the Sequential Evaluation and Model Pre-registration approach) and transparent reporting (e.g., the LEADING reporting guideline; www.leading-guideline.org).
I develop and maintain the R Language Analysis Suite—including text (www.r-text.org), topics (www.r-topics.org), and talk (www.r-talk.org)—along with the Language-Based Assessment Models (L-BAM) Library to help researchers analyse natural language data and test hypotheses in the social and behavioural sciences.