- Language Generation: Using language models (including but not limited to GPT) to generate text is fun, and getting better: how can we control the outputs to ensure they match our requirements, while mitigating bias and hallucination?
- Computational creativity: How do machines process creative language? Can we detect it? Can we generate it? Metaphors, metonymy, sarcasm, humor, and more all pose significant challenges even to state-of-the-art systems.
- Crisis Informatics: How do we best make of use of the massive amount of data created during crisis events (including natural disasters like hurricanes, terrorist events/wars, and pandemics like COVID-19)? Social media can help, but applying NLP can be difficult, and we deal with issues of data collection and annotation, machine learning, and ethical issues.
- NLP for Social Good: Relatedly, how can we leverage the vast amount of power AI has to improve peoples lives in concrete ways? How do we deal with ethical issues, problems of inclusivity, and systematic bias in data, systems, and researchers?
- Language: My background is originally in linguistics - I'm always interested in studying linguistic problems, particularly in semantics and pragmatics. Other areas of interest include construction grammar, frame semantics, and creative language.