Michael Wu

Hi! I'm currently a master's student in the Electrical Engineering & Computer Sciences Department at UC Berkeley. I'm advised by Prabal Dutta.

Previously, I was an undergraduate student at UC Berkeley, where I graduated in 3 years with high honors, double-majoring in Electrical Engineering & Computer Science (EECS) and Business Administration as a part of the Management, Entrepreneurship, and Technology (M.E.T.) program.

If you're interested in collaborating, send me an email!

Email  /  LinkedIn  /  GitHub

I worked on the ChromeOS Commerce team in Kirkland, Washington.

I worked on the Automation team in the Cellular Firmware division in San Diego, California.

Cerevox is a managed pipeline that makes it simple for developers to ingest unstructured data and transform it to be LLM-ready, making it easy for SMBs to utilize their unstructured data in conjunction with large language models (LLMs).

As a software engineer intern, I worked on implementing a custom parser to convert semi-structured data into client-specific structures via abstract syntax trees. I also worked on an open source project to provide API integration to the LangChain framework, which operates downstream to Cerevox's services.

I'm interested in the applications of data science and machine learning towards towards education.

This study focuses on the challenges in artificial intelligence, machine learning, and data science education. Their complexity and extensive prerequisites limit student access. Improving early accessibility is key to enabling earlier research engagement and possibly enhancing retention.

We seek to analyze how the structure of prerequisites influences the accessibility of courses in these fields. The goal is to understand the impact of different educational strategies on making artificial intelligence, machine learning, and data science more accessible for students.

Website template from Jon Barron.