This summer I volunteered as a teaching assistant for Dr. Wang’s “AI and BigData for Cancer Biology” program, the course met once a week for a month where high school students dove into the intersection of coding and cancer research. It was easily one of the most rewarding things I’ve done all year.
The goal of the program was simple: show students that the same Python code they use to make a game or a calculator can also be used to explore real biological data. We mixed in bits of upper-level cancer biology whenever it was needed — just enough to make the coding meaningful.
Most mornings started with short lessons on Python: reading data, cleaning it up, making graphs, and running simple statistical tests. In the afternoons, we tied those skills back to real problems. One of the highlights was a group project where students used the Genotype-Tissue Expression (GTEx) dataset to look at integrin gene expression across different human organs. They ran ANOVA and t-tests, made plots, and learned how to ask, “What does this actually tell us?”
By the end of the program, each student designed a small independent research project — something personal that connected coding to cancer biology. Some explored gene expression patterns, others looked at signaling pathways, but all learned how data can tell a biological story.
What stood out to me most was their curiosity. Once they realized they could actually use coding to explore biology, they took off running. I mostly played the role of guide — helping debug code, answer biology questions, and celebrate the little victories along the way.
It was inspiring to see high school students engage with complex ideas so fearlessly. Programs like this remind me why I love both teaching and research — because curiosity, when given the right tools, grows fast.
Go ahead check out their blogs from this summer!
Each of them have also research a cancer and wrote their first blog on cancer research.