Leanne Beet

Leanne Beet is the Astroparticle Physics Facility Manager at TRIUMF, where she manages the construction of a Class 6 clean room for testing and characterizing silicon photomultipliers—devices that detect single photons and support complex physics experiments, including particle identification.

Her role also involves working with departmental timesheet data to understand how employee time aligns with project commitments. For Leanne, AI data analytics in research operations is not abstract. It helps her analyze project-based data quickly, de-identify sensitive information before using AI tools, and strengthen privacy, compliance, and confidence in the results.

I joined the Applied AI in Data Analytics Program because I wanted to strengthen my AI literacy and build a more adaptable skill set for the evolving workplace. My supervisor recommended the program because he recognized how important AI skills are becoming, but I was already looking for a course that could help me move beyond self-directed learning.

In my role, I work with operational and project-based data. I wanted to learn how to use AI in a way that could directly support my work, especially when I need to analyze timesheet data quickly, validate the results, and ensure that any data used with AI has been carefully de-identified.

Before I completed the program, my main challenge was finding a better way to work with complex data that we had traditionally analyzed in Excel. Excel remains a powerful tool, and I still value it, but the work can become cumbersome and time-consuming when I need to review project data frequently.

With AI, I can now use both approaches together. I can keep the strengths of traditional tools while also using AI to check, support, and validate the accuracy and reliability of the data. That matters because the data helps us understand whether our time and resources align with the commitments we set for key projects.

What stood out most was the balance between technical concepts and practical use. The program did not just introduce AI tools in theory; it showed how to apply them to real workplace problems. I learned how to think about AI-driven techniques, balance model complexity with operational constraints, and use tools like Google Colab with more confidence.

Before the program, I had done some vibe coding and prompt engineering on my own, but it was self-directed. I was looking for a more rigorous, course-based environment. The labs gave me a structured way to apply what I had already explored and build those skills through practical examples.

The main project I am applying these skills to is timesheet analysis for our department. We are trying to understand how people’s time is allocated across projects based on the operational commitments set at the beginning of the year. In simple terms, we want to make sure we are fulfilling our commitments to each project.

This work used to rely heavily on Excel. Now, AI gives me another way to validate the data and check the results. That has become a huge asset in the biweekly work I do, because it helps me strengthen the validity of the analysis and complete the work more efficiently.

One of the most practical outcomes was being able to bring something useful back to my team. After the program, I gave a presentation to our group on how to use Google Colab, which was a tool we had not been familiar with before. Now, everyone on the team is using Colab.

I really enjoyed building connections with my classmates. The group discussions added tremendous value because everyone came from different professional backgrounds and brought a different view of how AI is being adopted in their field.

Hearing how other people use AI broadened my perspective. It gave me a stronger appreciation for how relevant AI has become across industries, including my own. It also motivated me to keep learning and to think more deeply about what responsible AI adoption can look like in different work environments.

Completing the Applied AI in Data Analytics Program was an incredibly valuable experience for me, both professionally and personally. It gave me a practical and accessible introduction to AI applications and real-world data work, with a strong focus on critical thinking, collaboration, and responsible AI use.

I would strongly recommend it to anyone who wants to broaden their skill set and prepare for the future of work. I would also encourage future participants to dedicate meaningful time to the lab components. Getting familiar with Google Colab well before the final project is very helpful.