Rate My Professor Amber Stubbs

Stubbs's research focuses on natural language processing, particularly annotation methodologies and corpus creation for machine learning applications in the bioclinical domain. She co-authored the book Natural Language Annotation for Machine Learning (O'Reilly Media, 2012) with James Pustejovsky and developed lightweight annotation tools MAE and MAI. Her work has advanced clinical natural language processing through participation in shared tasks, including overviews of the 2014 i2b2/UTHealth challenges on de-identification and heart disease risk factors (Journal of Biomedical Informatics, 2015), cohort selection for clinical trials (n2c2 2018 Track 1, Journal of the American Medical Informatics Association, 2019), and adverse drug events extraction (n2c2 2018, JAMIA, 2020). These publications have garnered significant citations. Stubbs has secured funding through CRA-W Collaborative Research Experience for Undergraduates grants (2015, 2016, 2018, 2019), the Simmons School of Library and Information Science Dole Award (2016-2017), and serves as Principal Investigator on a collaborative NSF Linguistics Grant proposal. She has held leadership roles on university committees, such as Chair of Computer Science faculty search committees (2019-2020, 2023-2024), the COCIS Ad-hoc Technology Committee (2018-2019, 2020-present), and guest editor for special issues in the Journal of Biomedical Informatics and Journal of the American Medical Informatics Association. Additionally, she has presented public lectures, including 'PC, MD? Can computers learn to diagnose medical problems?' in the Simmons Lunchtime Lecture Series.