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.
- September 2019: Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity accepted to appear at ASRU 2019
- August 2019: Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds accepted to appear at EMNLP 2019
- 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
- Fall 2019: Unstructured Data Analytics
- Fall 2019: Data Wrangling with R
- Fall 2018: UMass First Year Seminar: Artificial Intelligence and Healthcare
- J.P. Lalor, H. Wu, H. Yu. Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. EMNLP 2019
- 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 [poster, code]
- 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 [poster]
- 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.