John Fields, Faculty, AI Researcher, Lab Co-Founder

John Fields.
Faculty & AI researcher.

Assistant Professor of Business Analytics, Concordia University Wisconsin Co-Founder, AI & Quantum Innovation Lab Ph.D. Candidate in Computer Science, Marquette University

Researching privacy-preserving ML, large language models, and data literacy. My work is guided by faith, ethics, and a commitment to fairness in everything I build.

Innovation tethered to conscience.

I view AI and quantum technologies not merely as tools for advancement, but as opportunities to reflect human dignity in the responsible stewardship of creativity. Every project I touch starts with the same question: does this glorify God and steward His gifts well?

- 01 / Faith

Vocation, not novelty.

My work is rooted in the conviction that technology should serve the neighbor. I see teaching and research as a calling. Using the gifts I've been given to prepare students for meaningful, principled lives.

- 02 / Ethics

Privacy by design.

I default to minimizing data, maximizing utility through differential privacy and federated learning, and publishing model cards. Every project carries an ethics note from day one. Not as compliance, but as care.

- 03 / Fairness

Built for the person.

Decision gates favor solutions that reflect compassion, stewardship, and truth over hype. I evaluate models for downstream impact on workers, students, and communities, and we publish our threat models openly.

AI & Quantum
Innovation Lab

Concordia's AI & Quantum Innovation Lab pairs faculty expertise with student talent to help industry and community partners prototype real solutions ethically and securely. We turn ideas into tested prototypes through discovery sprints, a prototyping studio, and research-fellow certificates.

My Projects

NAIRR

Grant Funded

2025

Lab Founded

Visit the Lab →

2024

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

J Fields, K Chovanec, P Madiraju. IEEE Access, 12, 6518–6531. Cited 211 times. Open access.

IEEE →

2024

Integrating Categorical and Continuous Data in a Cluster-Then-Classify Methodology for Predicting Undergraduate Student Success

J Fields, K Chovanec, P Madiraju. 2024 IEEE International Conference on Big Data (BigData), 8118–8126.

Scholar →

2023

Combining Demographic Tabular Data with BERT Outputs for Multilabel Text Classification in Higher Education Survey Data

K Chovanec, J Fields, P Madiraju. 2023 IEEE International Conference on Big Data (BigData), 1403–1409.

Scholar →

2025

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

KM Sajjadul Islam, J Fields, P Madiraju. arXiv preprint, arXiv:2511.04698.

arXiv →

2025

Explaining Pretrained Language Models in the Context of Higher Education

K Chovanec, J Fields, P Madiraju. IEEE Big Data 2025.

Scholar →

2025

A Privacy-Preserving Framework for Cross-Institutional Collaboration on Student Retention Prediction Using Remote Data Science

J Fields, KM Islam, R Thota, V Chen, P Madiraju.

Scholar →

John Fields.Faculty & AI researcher.

Innovation tethered to conscience.

Vocation, not novelty.

Privacy by design.

Built for the person.

AI & QuantumInnovation Lab

Research focus

Selected publications

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Integrating Categorical and Continuous Data in a Cluster-Then-Classify Methodology for Predicting Undergraduate Student Success

Combining Demographic Tabular Data with BERT Outputs for Multilabel Text Classification in Higher Education Survey Data

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

Explaining Pretrained Language Models in the Context of Higher Education

A Privacy-Preserving Framework for Cross-Institutional Collaboration on Student Retention Prediction Using Remote Data Science

Project archive

Ignite & Inspire Conference 2026

Data Mining Tutorial

PySyft Demo

The Disputed Federalist Papers

Predicting Student Retention

Survey: Text Classification with Transformers

Predicting the Salary of the Next Syracuse Football Coach

Neural Networks for Fashion Images

Statistical Analysis of Lawyer Ratings of State Judges

Time-Series Analysis of the Global Bond Market

Top U.S. ZIP Codes for REIT Investment (Zillow Data)

New CUW Business Analytics Program

Predicting Sci-Fi Book Awards from Goodreads

Marketing Analysis for Kirin Ichiban

Onondaga Lake Water Quality Analysis

Women's Professional Cycling Poster

BERT vs Traditional NLP for Movie Review Sentiment

Regression Models for Marketing at Retail Relay

Context-Free Grammars for Camelot

Identifying Handwritten Digits with ML

How Spammers Find Your Email & Phone Number

K-means Clustering for Marketing Segmentation

Fake or Fact? Classifying Online Reviews

NLP Analysis of Hank & Holly Williams Lyrics

Real Job Interview Challenge

Data Mining Concepts & Tasks

Los Angeles Traffic Analysis (Python)

Twitter Analysis with Python & MongoDB

Donor Analysis with Python

Sentiment Analysis of MLK's "I Have a Dream"

SVM for Air Quality Prediction

Antelope Fawn Predictions

State Median Income Mapping (ggplot2)

NY Air Quality (1973) with ggplot2

Maryland Accident Data Analysis (JSON + R)

R Sampling Tutorial

Predicting Artist & Song Popularity with R

SQL Database for IoT Health Data

Do Exercise & Early Bed Times Yield More Sleep?

Experience

Recognition

Service & engagement

Let's connect.

John Fields.
Faculty & AI researcher.

AI & Quantum
Innovation Lab