As a computer scientist, I've explored my interest by taking classes such as artificial intelligence, robotics, and mobile app development. I recently interned at George Mason University doing research in machine learning and human-computer interaction, and at digital advocacy startup Phone2Action on the engineering team. I also enjoy teaching computer science to elementary school girls and attending hackathons.
It wasn't until I joined tjMedia my sophomore year that I realized I love journalism. I'm currently an editor-in-chief for my school newspaper, tjTODAY, where I lead our print publication, write articles, and design spreads. I also write and design for Girl Genius, Each Mind, Momentum Magazine, and Dear Asian Youth. My favorite part about writing is learning about the different perspectives of the people and communities I write about.
As I continued on the path through both fields, I soon realized I didn't have to choose just one.
Especially during confusing times like these, with rampant disinformation and political polarization, it's important for journalists to continue reporting while also maintaining trust with their readers. With computer science, I can use tools such as data analysis to give readers a better sense of the big picture, and algorithms to better understand bias in the news we read. With journalism, I can better understand others' perspectives and effectively communicate ideas.
Computational journalism is the mix between journalism, algorithms, and data to better gather and display information while delivering more accurate news, without bias. Computational journalism can unravel complex stories in a more efficient and understandable way, and encourages better journalism and less political polarization.
With Tableau, I've investigated stories such as mental health funding at my high school and trends in Gen Z compared to previous generations. As part of my senior research, I'm currently analyzing event selection bias across news outlets using text analysis, machine learning, and data visualizations that highlight differences in the way various news outlets cover specific events.
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