I will be joining the Mendoza College of Business at the University of Notre Dame as an assistant professor in Fall 2019!

I am a PhD candidate at the University of Massachusetts Amherst in the College of Information and Computer Science. I work with Dr. Hong Yu in the Bio-NLP group. My research interests are in Machine Learning and Natural Language Processing. I am particularly interested in model evaluation and quantifying uncertainty, as well as applications in biomedical informatics.

Prior to UMass, I worked as a software developer at Eze Software in Chicago and as an IT Audit Associate for KPMG. I received my Master's Degree in Computer Science at DePaul University, where I worked on projects in Computer Science Education and Recommender Systems. I received my bachelor's degree in IT Management from Universty of Notre Dame, with a minor in Irish Language & Literature.


  1. March 2019: Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds accepted as an extended abstract at the NAACL Shortcomings in Vision and Language workshop
  2. March 2019: Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting accepted as an extended abstract at the NAACL Cognitive Modeling and Computational Linguistics workshop
  3. February 2019: Our paper on detecting hypoglycemia incidents in secure messages was accepted by JMIR
  4. November 2018: Successfully defended my Ph.D. thesis proposal
  5. October 2018: Our latest paper using the ComprehENotes test was accepted for publication by the Journal of Medical Internet Research
  6. October 2018: Talk at Notre Dame Mendoza College of Business
  7. August 2018: One paper accepted to EMNLP 2018
  8. August 2018: Two papers accepted to the UAI Uncertainty in Deep Learning workshop
  9. Summer 2018: Internship with the Amazon Alexa team in Cambridge, MA


Fall 2018 FYS: Artificial Intelligence and Healthcare


Posters and Abstracts


Below are a few projects that I've worked on, either as part of a class project or on my own time.