About

I am an Instructor at the Mendoza College of Business at the University of Notre Dame, and an ABD PhD candidate at the University of Massachusetts Amherst in the College of Information and Computer Science. At UMass I was a member of the Bio-NLP group, working with Dr. Hong Yu. 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.

Updates

  1. September 2019: Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity accepted to appear at ASRU 2019
  2. August 2019: Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds accepted to appear at EMNLP 2019
  3. 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
  4. 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
  5. February 2019: Our paper on detecting hypoglycemia incidents in secure messages was accepted by JMIR
  6. November 2018: Successfully defended my Ph.D. thesis proposal
  7. October 2018: Our latest paper using the ComprehENotes test was accepted for publication by the Journal of Medical Internet Research

Teaching

Publications

Posters and Abstracts

Projects

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