I am a graduate student researcher at University of Washington, applying for a PhD in Computer Science in Fall 2024. I am fortunate to be supervised by Abhishek Gupta and Natasha Jaques. Before that, I was a Software Development Engineer II at the Amazon AGI Data Prep team, focusing on developing data preparation tools and infrastructure used for large language model (LLM) training. I graduated from Carnegie Mellon Univ 568C ersity with a B.S. in Neuroscience and Computer Science (Fun fact: I was the only person graduated with a second major in CS in the Neuroscience department).
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I like to model everything. Some might call a reductionist. But I cannot resist the appeal of describing the mundane everyday complex phenomenons, such as weather, grocery price, or interpersonal relations, in terms of elegant mathematical symbols and functions with certain input space and output space. Studying computer science and mathematics allows me to do that in a more rigorous fashion. It teaches me a language to describe a problem space so that my ideas can be communicated universally. Studying neuroscience allows me to see both the complexiticity and simplicity of the world I live in. We can describe our minds as simple as an ensemble of electrical pulses, but the complexity lies in that our coordination, our neuroplasticity and biological efficiency (compared to its power-hungry machine counterpart).
I believe that real science does not have boundary of subjects. There are techniques, or tools, (I like to imagine things in a visual sense) that might be potentially helpful to solve a problem, and these tools might be labeled with academic subjects such as mathematics, economics, cognitive science, psychology, biology and physics. We should be open to learn to any tools that would be useful to our goal.