Welcome

I am an Associate Professor at the School of Information at the University of Arizona with courtesy appointments in Linguistics, Cognitive Science, Computer Science, and Applied Mathematics.

I previously worked as an assistant professor in Computer and Information Science at the University of Alabama at Birmingham, and as a postdoctoral researcher at Stanford University’s Natural Language Processing group, Johns Hopkins University’s Human Language Technology Center of Excellence, KULeuven’s Language Intelligence and Information Retrieval group in Belgium, and the University of Colorado’s Center for Language and Education Research.

My research interests include natural language processing and machine learning theory and applications, including modeling the language of time and timelines, normalizing text to medical and geospatial ontologies, and information extraction models for clinical applications. There is a large community at the University of Arizona pursuing similar natural language processing research. Visit us at: http://nlp.arizona.edu/

My ongoing funded projects include:

• Temporal Relation Discovery for Clinical Text (NIH NLM R01LM010090; funded since 2010), in which we are designing machine learning algorithms to extract timelines from clinical text and integrate those with structured data from the electronic medical record.
• Extended Methods and Software Development for Health NLP (NIH NIGMS R01GM114355; funded since 2016), in which we are designing machine learning algorithms that leverage large collections of text to improve information extraction tasks such as mapping text to ontologies and discovering links between health events.
• Using Natural Language Processing to Determine Predictors of Healthy Diet and Physical Activity Behavior Change in Ovarian Cancer Survivors (NIH NCI R21CA256680; funded since 2021), in which we are designing machine-learning algorithms to analyze conversations in behavioral interventions, with the goal of improving patient outcomes by improving how patients are coached.
• Learning science concepts through metaphor comprehension, production, and conversation: Behavioral, neural and artificial intelligence measures (NSF BCS 2140897; funded since 2022), in which we are using language models to help students generate metaphors and improve their science knowledge.

My past funded projects include:

• A Data Science Platform and Mechanisms for Its Sustainability (NSF SMA RIDIR 1831551; funded since 2018), in which we are designing information extraction algorithms to make it easier to search environmental impact statements.
• Global Reading and Assembly for Semantic, Probabilistic World Models (DARPA W911NF-18-1-0014; funded 2017-2022), in which we were designing machine learning algorithms to infer from text the times and locations over which a causal relation is valid, with the goal of modeling complex interactions in domains like food security.
• Voice Assistant for Data Entry and Recording (VADER; USAF SBIR FA8649-21-P-0834; funded 2021-2022), in which we were designing speech-to-text algorithms to assist aircraft maintainers in logging their activities.
• Automated Domain Adaptation for Clinical Natural Language Processing (NIH NLM R01LM012918; funded 2018-2021), in which we were designing algorithms that can improve machine learning models trained in one medical institution with data from another institution without any need for sharing of patient data.