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.
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.
- August 2018: One paper accepted to EMNLP 2018!
- August 2018: Two papers accepted to the UAI Uncertainty in Deep Learning workshop!
- Summer 2018: Internship with the Amazon Alexa team in Cambridge, MA.
- February 2018: Our paper ComprehENotes: Development and Validation of an Instrument to Assess Patient EHR Note Reading Comprehension was accepted for publication by the Journal of Medical Internet Research!
- January 2018: I presented at the first Northern Lights Deep Learning Workshop in Tromso, Norway.
- December 2017: I passed the CICS Portfolio and am now a PhD candidate!
- November 2017: I attended the AMIA Annual Symposium presenting our podium abstract on a new test of Electronic Health Record note comprehension.
- October 2017: I was at HCOMP 2017 to present CIFT as a Works in Progress poster.
- September 2017: I gave a talk at Notre Dame to the ND Natural Language Processing group. Thanks to Prof. David Chiang for the invitation!
- J.P. Lalor, H. Wu, H. Yu. Soft Label Memorization-Generalization for Natural Language Inference. UAI Workshop on Uncertainty in Deep Learning., 2018.
- J.P. Lalor, H. Wu, T. Munkhdalai, H. Yu. Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study. UAI Workshop on Uncertainty in Deep Learning., 2018.
- J.P. Lalor, H. Wu, L. Chen, K. Mazor, H. Yu, ComprehENotes, an Instrument for Assessing Patient Electronic Health Record Note Reading Comprehension: Development and Validation, J Med Internet Res 2018;20(4):e139. [project page]
- T. Munkhdalai, J.P. Lalor, H. Yu. Citation Analysis with Neural Attention Models, In LOUHI 2016 EMNLP Workshop. [pdf]
- J.P. Lalor, H. Wu, H. Yu. Building an Evaluation Scale using Item Response Theory, In EMNLP 2016. [arXiv pre-print, project page]
- C. Miller, A. Settle, and J.P. Lalor. Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls, SIGITE 2015. [ACM link]
- A. Settle, J.P. Lalor, and T. Steinbach. Evaluating a Linked-courses Learning Community for Development Majors. In SIGITE 2015. [ACM link]
- A. Settle, J.P. Lalor, and T. Steinbach. A Computer Science Linked-courses Learning Community, In ITiCSE 2015. [ACM link]
- A. Settle, J.P. Lalor, and T. Steinbach. Reconsidering the Impact of CS1 on Novice Attitudes. In SIGCSE 2015. [ACM link]
Posters and Abstracts
- J.P. Lalor, H. Wu, H. Yu, Modeling Difficulty to Understand Deep Learning Performance. Northern Lights Deep Learning Workshop (NLDL), 2018 [slides]
- J.P. Lalor, H. Wu, L. Chen, K. Mazor, H. Yu, Generating a Test of Electronic Health Narrative Comprehension with Item Response Theory. Podium Abstract, In AMIA 2017. [project page, slides]
- J.P. Lalor, H. Wu, H. Yu. CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability, HCOMP 2017 Works in Progress [poster]
- J.P. Lalor, H. Wu, H. Yu. CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability [arxiv]
- J.P. Lalor, H. Wu, T. Munkhdalai, H. Yu. An Analysis of Machine Learning Ability [arxiv]
Below are a few projects that I've worked on, either as part of a class project or on my own time.
- GutenRecs: "More like this" book recommendations for Project Gutenberg. Final Project for ECT 584: Web Data Mining at DePaul
- An Analysis of Major League Baseball as a Social Network: Final project for CSC 495: Social Network Analysis at DePaul.
- Goodreads Right Click: A Chrome Extension for Searching on Goodreads. I wanted this to exist, so I built it.