Research
One question runs through all of my work: what do we learn by building things? I pursue it in three connected areas, asking what we learn by building in science, in the study of the mind, and in the practice of engineering itself.
Across all three areas I work with two kinds of tool at once: the careful conceptual analysis and logical argumentation of philosophy and the computational methods of artificial intelligence, including computer simulations, robots, and large-language-model text analysis.
Research Areas
Scientific Progress
My dissertation, Engineering Progress in Science (University of Cincinnati, 2025), is about how science makes progress. The usual story says science advances by accumulating truer theories, new laws, and better explanations. I argue that this misses half of what scientists actually accomplish. The things they build, including instruments, models, calibration routines, and whole experimental systems, are achievements in knowledge and understanding in their own right, every bit as much as a new theory. Progress, on my account, is the growth of what science can do as much as what it can say. I call this the operative account of progress.
This changes how we should think about engineering. Engineering is not science applied after the fact; it is a way of finding things out and of making things better. In a 2025 paper with Tim Elmo Feiten (“Leveraging Participatory Sense-Making and Public Engagement with Science for AI Democratization,” Studies in History and Philosophy of Science), I build on our experience as NSF Fellows at the “Intelligent Cyberinfrastructure with Computational Learning in the Environment” AI institute to argue that genuine back-and-forth between scientists and communities is not just morally obligatory but a way of generating new understanding. A book project under contract with Cambridge University Press, A Guide for Academic Researchers Conducting Science Outreach, turns this into practical advice for researchers doing public outreach.
The Mind, the Body, and Machines
My second area joins the philosophical study of the mind and artificial intelligence engineering. I treat thinking as something that depends on the body and its surroundings, not only on processes inside the head, and I test that idea by building. I make small artificial agents, robots that evolve their own behavior, and sensors modeled on the whiskers animals use to feel their way in the dark (the subject of my engineering master’s thesis). Each is a controlled setting for asking how much of intelligent behavior the body and the environment handle on their own. This work draws on a double training, a PhD in philosophy and a master’s in robotics and intelligent autonomous systems.
I utilize agentic AI tools to study AI itself. I have used large language models to sort and code large collections of social-science documents, and I am developing philosophical work on simulation and on video games, where AI-driven systems are increasingly becoming places of crucial cognitive and ethical import.
The Ethics of Engineering
My third area grows directly out of my training as an engineer. I am developing an ethics of engineering built around professional identity. The core idea is that what an engineer ought to do is best understood through the kind of person the profession asks them to become, rather than through rules or a tally of costs and benefits alone. This carries into my teaching: in my ethics, medical-ethics, and moral-issues courses I encourage students to reflect on the role their identities play in shaping what ethical practices look like for them.