I am a PhD student 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.

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. November 2017: I'll be at the AMIA Annual Symposium presenting our podium abstract on a new test of Electronic Health Record note comprehension.
  2. October 2017: At HCOMP 2017 to present CIFT as a Works in Progress poster.
  3. September 2017: I gave a talk at Notre Dame to the ND Natural Language Processing group. Thanks to Prof. David Chiang for the invitation!
  4. June 2017: CIFT was accepted as a poster for the Work-in-Progress session at HCOMP 2017 in Quebec City!
  5. June 2017: Our podium abstract, Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory, was accepted for AMIA 2017 in Washington, DC!
  6. Summer 2017: Internship with the Alexa team at Amazon Boston!
  7. December 2016: I gave a talk on our EMNLP paper as part of the UMass CICS Machine Learning and Friends Lunch series on 12/8.
    • Slides from the talk can be found here.
    • If you're really interested, I recorded myself giving the talk as a practice run. It's on YouTube here. If you have any feedback I would love to hear it!
  8. September 2016: Tsendee's paper Citation Analysis with Neural Attention Models was accepted to the EMNLP LOUHI Workshop!
  9. July 2016: Our paper Building an Evaluation Scale using Item Response Theory was accepted to EMNLP 2016!


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.