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.
- 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
- 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
- February 2019: Our paper on detecting hypoglycemia incidents in secure messages was accepted by JMIR
- November 2018: Successfully defended my Ph.D. thesis proposal
- October 2018: Our latest paper using the ComprehENotes test was accepted for publication by the Journal of Medical Internet Research
- October 2018: Talk at Notre Dame Mendoza College of Business
- 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
- J. Chen, J.P. Lalor, W. Liu, E. Druhl, E. Granillo, V. Vimalananda, H. Yu. Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-sensitive Learning and Oversampling to Reduce Data Imbalance. J Med Internet Res 2019;21(3):e11990. doi:10.2196/11990. [paper]
- J.P. Lalor, B. Woolf, H. Yu, Improving EHR Note Comprehension with NoteAid: A Randomized Trial of EHR Note Comprehension Interventions with Crowdsourced Workers, J Med Internet Res 2019;21(1):e10793. [paper, project page]
- J.P. Lalor, H. Wu, T. Munkhdalai, H. Yu. Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study. In EMNLP 2018. [arXiv pre-print, project page, youtube link for presentation]
- J.P. Lalor, H. Wu, H. Yu. Soft Label Memorization-Generalization for Natural Language Inference. UAI Workshop on Uncertainty in Deep Learning., 2018. [arxiv pre-print]
- 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. [paper, 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. Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. NAACL Workshop on Shortcomings in Vision and Language (SiVL) 2019
- J.P. Lalor, H. Wu, H. Yu. Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting. NAACL Workshop on Cognitive Modeling and Computational Linguistics (CMCL) 2019
- J. Chen, J.P. Lalor, H. Yu. Detecting Hypoglycemia Incidents from Patients' Secure Messages. American Medical Informatics Association (AMIA) Annual Symposium Poster, 2018
- 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. American Medical Informatics Association (AMIA) Annual Symposium Podium Abstract, 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]
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.