In my research, we are keen on analyzing moving images to extract geometric objects and understand motion, which is crucial for applications like self-driving cars. Geometric algebra serves as a unifying language, simplifying complex computations in fields like computer vision, making it easier to work with concepts like rotations and translations without confusion.

One of my early projects involved translating classical projective geometry concepts into geometric algebra, which simplified the mathematical complexity of computer vision tasks.

Using geometric algebra provides a clear origin point for calculations, eliminating the confusion of multiple coordinate systems and making it easier to handle rotations and translations in computer vision.

Geometric algebra provides a mathematical framework that allows us to work with lines in computer vision, which is more complex than working with points.

Geometric algebra allows for a unified approach to various fields, enabling work in rigid body dynamics, linear algebra, and quantum physics without relying on matrices or tensors.

We designed our technology to be performer-friendly and scalable, enabling content creation without the need for large motion capture stages.

The goal during my five-year research period was to apply geometric algebra to practical uses and basic research, exploring its potential in various applications.

My husband, Anthony, became fascinated with geometric algebra, which led me to explore its applications in engineering, particularly in rotations.

In collaborations with hospitals, we focus on developing algorithms that maximize the quality of high-resolution images from limited data, particularly in medical imaging.