Professional Blog

One important lesson I have discvered is that when you’re learning something new — especially after accumulating a range of life experiences — it’s essential to connect new information to what you already know. This is the foundation of constructivist and connectivist learning theories, which emphasize the importance of prior knowledge and meaningful associations. Tools like knowledge graphs can support this process by visually mapping how concepts interrelate. In many ways, I see this as a modern evolution of the Feynman Technique, which I strongly advocate for: if you can explain an idea clearly and understand how it fits into a broader system, you’re far more likely to retain and apply it. Let's pretend I am a professional running coach. If I can take a computer science concept and apply it to a room of atheletes, then I have a better understanding and the runners learn something as well!

Beyond data and technology, I am deeply passionate about sustainability and the environment, neuroscience and trauma, and the wisdom of indigenous peoples in stewarding the land. I am especially interested in the intersections of these areas—where knowledge, science, and tradition come together to create solutions for a better world.

Since returning to my academic career, I have become deeply fascinated by the power of data to solve real-world problems. From analyzing trends to building predictive models, the possibilities within data science are endless. At the same time, my long-standing interest in technology and problem-solving naturally led me to explore computer science. As I built a foundation in these fields, I realized that sharing my journey could not only keep me motivated but also help me connect with others—whether they are just starting out or already established in these industries.