Ky-Vinh Mai: Beyond the Data

By Jill Kato

The following excerpts are from an article shared by the UC Irvine School of Social Sciences.

Ky-Vinh Mai was born in a Vietnamese refugee camp in the Philippines. When his family relocated to the United States, they moved around in Orange County until finally settling in Santa Ana. As a first-generation child of refugees who worked hard to provide for their family, Mai learned from an early age how to be independent and resourceful.

“It was just me and my family, and that taught me to figure things out on my own,” the Regents’ scholar says.

These lessons of perseverance and self-reliance became the foundation of his academic success and fueled his curiosity about the world.

Now a senior at UC Irvine double-majoring in data science and language science, Mai’s path reflects his commitment to exploring big questions—ones that don’t always fit neatly into a single discipline. From his early interests in artificial intelligence (AI) to his research at the intersection of linguistics and machine learning, Mai’s academic career has been anything but conventional.

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What makes Mai stand out isn’t just his technical ability—it’s his belief that research should tackle real-world challenges. He has a particular interest in the ethical and social implications of AI.

“Technology is moving so fast, but we aren’t always equipped to regulate it or think about how it impacts people,” he says.

Mai’s approach blends his technical expertise with a sense of social responsibility. He points out that biases in AI systems can reinforce inequalities and that researchers need to think critically about diversity and representation in their work.

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Mai’s impressive technical background, strong academic record, and experience with advanced AI research landed him an internship at Los Alamos National Laboratory’s Open Science division. There, he focused on addressing critical challenges in climate modeling and materials science, working specifically on identifying and mitigating pretraining bias in AI systems.

“I chose to work specifically on climate science and material science, and to address pretraining bias in AI systems—work that could help us better understand and address climate change and the machines that model it,” Mai says. “But I also recognize that we can’t entirely separate our work from its institutional context, legacies and mixed funding sources. These are the kinds of difficult questions that scientists and researchers need to grapple with openly.”

Read the full article in the UCI School of Social Sciences news.