AIxchange

scope × match × build

2026–27 SCDS Pilot

Campus challenges
AI expertise

Connecting UO's AI expertise, infrastructure, and students with real problems.

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About

AI capability is distributed. AIxchange helps connect it.

UO already has AI expertise, student talent, research infrastructure, datasets, and real problems across the university. Those pieces are not always easy to find.

AIxchange makes capabilities more visible and creates recurring activity around identifying problems, forming teams, and turning promising matches into projects.

SCDS serves as connective tissue, helping AI expertise, student talent, infrastructure, and problems across UO become easier to discover and combine.

How It Works

From problem to project.

  1. 1

    Bring a Problem

    Faculty, researchers, campus units, and partners bring a real challenge.

  2. 2

    Scope

    We help determine whether AI is appropriate and turn the challenge into a project students can realistically tackle.

  3. 3

    Match + Build

    Connect students, faculty expertise, data, and infrastructure. Small teams investigate, prototype, evaluate, or develop a solution.

  4. 4

    Grow

    Promising projects can continue as research collaborations, capstones, independent studies, grants, publications, or externally supported work.

Projects

Projects at AIxchange

Real problems. Small teams. Hands-on AI.

Now Recruiting Students

Real-Time ML for Smart Sensors

Ong Lab Computer Science

This collaboration is underway, and we are looking for students to join it.

I'm Interested

The project

The Ong Lab develops smart sensing systems and is already using machine learning to generate calibration routines for sensor data. Through AIxchange, we are exploring the next step: whether lightweight machine learning can operate in real time on incoming sensor measurements.

The research direction

The project will investigate how ML can be integrated into the sensor or reader pipeline for continuous calibration and inference, while accounting for the constraints of real sensing systems.

What we may explore

  • Real-time inference from sensor streams
  • ML-based calibration
  • Robustness to sensor variability and noise
  • Lightweight and TinyML models
  • Deployment on constrained hardware
  • Accuracy, latency, memory, computation, and energy tradeoffs

For students

Good fit for students interested in: Machine learning · TinyML · embedded systems · sensor data · edge AI

Join a project already underway and work with the team on a real sensing challenge.

More projects coming soon.

Have a research or campus problem that could become an AIxchange project?

Bring a Problem

For Students

Learn AI by working on something real.

AIxchange gives students the opportunity to work with faculty and domain experts on real problems involving real data, systems, and constraints.

Projects may involve machine learning, AI systems, agents, computer vision, scientific computing, TinyML, data analysis, or other areas depending on the problem.

Students may participate through research experiences, independent study, capstones, short-term projects, or funded positions depending on the project.

We are currently looking for students for the Real-Time ML for Smart Sensors project.

View the Project Tell Us About Your Interests

For Faculty, Units, and Partners

Have a problem that AI might help solve?

You do not need to arrive with a model, an AI strategy, or a fully defined technical problem. Bring the underlying challenge.

You do not need to decide whether AI is the right solution. We can help with that.

AIxchange can help assess the opportunity, scope a tractable project, identify the right expertise and students, and connect the team with the data and infrastructure it needs.

Bring a Problem

Bring us a problem.

Got a real problem and no obvious path forward?

Perfect.

Bring the question, the constraints, the messy data, or the system that is not working the way you want.

We'll help figure out what comes next.

Bring a Problem Explore Projects