Innovation
I care about what happens after an idea leaves the research paper. Some ideas find their way into networks people rely on. Others become new programs, better ways to teach, or tools that solve a practical problem. These are a few of the efforts where I have been able to do that work.
Link Oregon
As Chief Scientist at Link Oregon, Oregon's statewide research and education network, I get to work with the people who run the infrastructure that universities and public institutions depend on. It is a chance to test research ideas against the needs of a production network.
Our work has touched new network architectures, machine learning for network security, infrastructure planning informed by data, and networks that connect quantum and classical systems. The most useful questions often come from the practical constraints operators face every day.
AIxchange
I founded AIxchange to connect people who understand a problem deeply with people who know how to apply AI to it.
A researcher or organization may have valuable data and a question worth asking, but no clear way to explore what AI could help with. Students and AI researchers, meanwhile, are looking for meaningful problems. AIxchange gives them a place to meet and find out whether they can work together.
Sometimes that leads to an experiment or a prototype. Sometimes it grows into a longer project. The starting point is always the problem, not the technology.
NEXT Studio
I helped start NEXT Studio (Nurturing Excellence in Technology and Software) because students learn some parts of software engineering best when they are responsible for something that has to work.
University of Oregon students work on projects with industry partners, research labs, startups, nonprofits, and groups on campus. They meet stakeholders, make design decisions, deal with constraints, and deliver software that others will use. As they gain experience, they can take on technical and team leadership.
NEXT Studio complements classroom learning. It gives students room to practice the judgment, collaboration, and persistence that real projects demand.
Teaching Innovation
AI is changing the tools students will use at work. That raises a teaching question I take seriously: how do we help students use AI well without losing the understanding they need to judge its work?
In my Introduction to Software Engineering course, students first build core skills without AI. They then use it as a programming partner and, later, use it more extensively on larger projects. UO's Teaching Support and Innovation program has featured this approach in its faculty teaching examples.
A Williams Instructional Grant for 2026 to 2027 supports the next stage of this work, Preserving Foundations, Expanding Possibilities: Software Engineering in the Age of Generative AI. I am developing ways to bring AI into the course while keeping students focused on understanding, reflection, and sound technical judgment.
I also lead a UO Community Accelerating the Impact of Teaching (CAIT) focused on AI. It will bring faculty together to think through what these tools mean for teaching and curriculum.
See UO's faculty teaching example → (opens in a new tab) Williams Instructional Fund project → (opens in a new tab)
Technical Advising
I also work as a consultant and technical advisor to companies on computer networks, distributed and cloud systems, AI in infrastructure, and emerging technologies.
That can mean reviewing an architecture, exploring a research direction, or helping a team weigh the tradeoffs behind a technical decision. I am most interested in work that takes a promising idea and asks whether it can hold up in a real system.
Company relationships are kept confidential unless otherwise agreed.