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John Lalor

Natural Language Processing (NLP) Biomedical Informatics Model Evaluation Quantifying Uncertainty Bio-NLP
University of Notre Dame College of Engineering

About

I am an Assistant Professor at the Mendoza College of Business at the University of Notre Dame. I completed my Ph.D. 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, quantifying uncertainty, and 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.

Research Performance Summary

51
Total Papers
1031
Total Citations
18
H-Index
2015-2025
Active Research Span

First Recorded Paper

Reconsidering the impact of CS1 on novice attitudes

Year: 2015

Citations: 26

Venue: Proceedings of the 46th ACM Technical Symposium on Computer Science

Latest Recorded Paper

FROM POLICY TO PRACTICE: RESEARCH DIRECTIONS FOR TRUSTWORTHY AND RESPONSIBLE AI “BY DESIGN”

Year: 2025

Citations: 0

Venue: N/A

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2025
13
2024
10
2023
4
2022
3
2021
4
2020
4
2019
4
2018
2
2017
1
2016
2
2015
4

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 24
Journal 10
Book 0

Top Coauthors

Lalor Abbasi Yang

Research Impact by Period

2025-2026

Period Stats
Papers 13
Citations 50
Avg. Citations / Paper 3.8
H-Index 5

2020-2024

Period Stats
Papers 25
Citations 481
Avg. Citations / Paper 19.2
H-Index 11

2015-2019

Period Stats
Papers 13
Citations 500
Avg. Citations / Paper 38.5
H-Index 12

Contact and Professional Links

Contact Information

john.lalor@nd.edu
5746315104

Detected Research Keywords

Item Response Theory Electronic Health Record Health Record Note Record Note Comprehension Randomized Trial Electronic Trial Electronic Health Note Comprehension Interventions Health Record Notes Natural Language Processing Linked Courses Learning