Jiaming Chang

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Hello! My name is Jiaming Chang, and I am currently a Master’s student in Data Science at Johns Hopkins University.

I am interested in how humans and intelligent systems learn, reason, and make decisions under uncertainty. Through projects spanning data analysis, statistical modeling, and machine learning, I have become increasingly interested in questions of learning, reasoning, and decision-making, particularly in how uncertainty can be represented and incorporated into the decision process. My current academic interests lie at the intersection of statistics, artificial intelligence, and decision science, with growing interests in Bayesian methods and causal inference.

More broadly, I am fascinated by questions surrounding intelligence, knowledge, and human understanding. This has led me to explore philosophy, particularly the traditions of Kant, Hegel, Marx, Heidegger, and Wittgenstein, as well as philosophy of science. I view these perspectives not as alternatives to scientific inquiry, but as complementary ways of thinking about how we model, interpret, and act within the world.

I enjoy exploring unfamiliar ideas, connecting concepts across disciplines, and pursuing questions whose answers are not immediately obvious. In the long run, I hope to contribute to the development of theories and systems that help humans and intelligent machines make better decisions in complex and uncertain environments.