Research Group
Tom Kerby's Research Group
About Our Research Group
Our research group studies learning, reasoning, and decision-making in humans and AI from a statistical perspective. We develop methods for understanding what AI systems learn and what properties persist, for measuring what humans know and how they reason through interactions with AI, and for designing AI-assisted decisions that respect human agency under uncertainty.
We foster an inclusive and collaborative environment where students and researchers can develop their skills in statistical methodology, machine learning, computational techniques, and interdisciplinary problem-solving. Our members come from diverse backgrounds in statistics, computer science, mathematics, and domain-specific fields, working together on problems that span education, scientific discovery, cybersecurity, and family history.
Principal Investigator
Dr. Tom Kerby
Assistant Professor of Statistics
Dr. Kerby leads research at the intersection of statistics and AI, studying how modern AI systems create new problems in inference, measurement, generalization, and decision-making. He completed his PhD in Statistics & Data Science at Utah State University in 2025, where he developed methods for characterizing higher-order dependence in learned representations and extended training-free guidance to discrete diffusion models.
Email: tkerby@stat.byu.edu
Office: WVB 2196
Research Interests: Statistical understanding of AI, human-AI measurement and decision-making, trustworthy AI
Current Research Group Members
Matthew Warnick
BS Statistics - Biostatistics Emphasis, Freshman
Tenure: Jan 2026 – Present
Research Focus: AI Interpretability & Representation Structure
April Baik
BS Statistics - Data Science Emphasis, Sophomore
Tenure: Mar 2026 – Present
Research Focus: Human-AI Learning & Measurement
Spencer Pendley
BS Undecided, Freshman
Tenure: Apr 2026 – Present
Research Focus: Human-AI Learning & Measurement
Nikita Gallegos
BS Machine Learning, Junior
Tenure: Aug 2026 – Present
Research Focus: Human-AI Learning & Measurement
Research Group Alumni
Dallin Robinson
Aug 2025 - Nov 2025
Now at Purdue Business School
Patrick Willmott
Jan 2026 - Jul 2026
Now at Future PhD Student
Join Our Research Group
For Prospective Students
I work with students interested in understanding AI systems and in studying how humans learn, reason, and make decisions with AI. Projects range from mathematical and computational work on dependence, representation, and generalization in neural networks to statistical and experimental work on measurement, assessment, and decision-making with AI. These projects provide opportunities for students to apply and extend statistical thinking to some of the emerging challenges created by modern AI. Opportunities are available at the undergraduate, master's, and doctoral levels.
- • Graduate Research: Strong background in statistics, mathematics, or computer science, with interest in inference, measurement, or decision-making in AI systems
- • Undergraduate Research: Interest in understanding AI systems, measuring human learning with AI, or designing trustworthy AI—statistics coursework is helpful
Eligibility: Dr. Kerby mentors students who are currently enrolled at BYU. Students from other institutions are welcome to reach out, but arrangements must include a faculty member from your home institution who will co-mentor the collaboration. If you are an external student, please ask your faculty advisor to contact Dr. Kerby directly.
Contact Information
Email: tkerby@stat.byu.edu
Office: WVB 2196
Office Hours: Monday & Wednesday, 2:00-3:00 PM
Please include your CV or resume, research interests, and relevant background when reaching out about research opportunities.