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Community Spotlight: Kyle Stanley, PhD

Community Spotlight March 25, 2026

Kyle Stanley, PhD

Kyle Stanley, PhD

Postdoctoral Researcher

Biostatistics Division
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Kyle Stanley, PhD, is a postdoctoral researcher in the Department of Biostatistics, Epidemiology, and Informatics whose work focuses on developing novel methods to analyze wearable device data. He applies functional data analysis, distributional modeling, and machine learning techniques to capture the rich information in wearables and improve prediction of adverse health events. A central goal of his research is to leverage these data to enable earlier interventions and better patient outcomes.

In this Q&A, Kyle shares how his background in neuroimaging and functional data analysis shaped his research path, how his work addresses real-world health challenges, what he values most about DBEI’s collaborative environment, and the interests and activities that inspire him outside of academia.

Can you tell us about your current research and what inspired you to pursue this area?

Thus far, my postdoctoral work (advised by Dr. Jeffrey Morris) has focused on developing new approaches for analyzing wearable device data. Many wearable analyses leverage only scalar summaries of these rich datasets. The main thrust of our work is to incorporate more expressive distributional summaries of these data into novel modeling frameworks. In doing so, we have drawn upon ideas from functional data analysis (FDA), distributional data analysis, and other statistical/ML fields. I became interested in this research after working within the FDA paradigm during my PhD at Penn State — with a focus on neuroimaging data — and learning more about applications of FDA to the increasingly relevant field of wearable devices.

How does your research intersect with real-world challenges?

One high-level aim of my work is to improve prediction of adverse patient-level health events (e.g., heart attacks) by leveraging wearables data. A system capable of reliably predicting such events would enable physicians to intervene before these events take place, thereby improving patient outcomes and reducing costs associated with expensive treatments.

What do you find most rewarding about working in the Department of Biostatistics, Epidemiology, and Informatics?

I’ve really enjoyed working with and alongside DBEI researchers. Everyone I’ve met is very engaged with their discipline and excited to talk about it. It makes for many stimulating conversations and plenty of learning!

Can you share a significant recent project, publication, or professional recognition that you are particularly proud of contributing to or achieving? 

My first paper, titled “Functional Factor Modeling of Brain Connectivity“, which I co-authored with Nicole Lazar and Matthew Reimherr, received a Student Paper Award at the 2024 New England Statistical Symposium and was later published in the Annals of Applied Statistics. The paper merges ideas from FDA, factor analysis, and matrix completion to develop a new method for analyzing statistical dependencies within the brain.

What advice would you give to students or early-career professionals in your field?

I still consider myself early-career and in search of guidance! But my advice to those more junior than me would be to (i) ask plenty of questions (especially the ones that seem dumb), (ii) reserve time to study topics outside your subspecialty (you’ll likely find that you can borrow ideas for your own work), and (iii) find people who you enjoy working with.

What is an interest or pastime that you enjoy outside of academia?

I love a good story in nearly any form — book, movie, play, etc. In the past, I played baseball and, more recently, have enjoyed performing and working in community theater productions.