Michael Wu

I'm 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  /  Resume  /  LinkedIn  /  GitHub

Profile Picture
Industry
Google, Software Engineer Intern
Summer 2024

I will be joining the ChromeOS team at Kirkland, Washington this summer.

Apple, Software Engineer Intern
Spring 2024

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

Cerevox, Software Engineer Intern
Fall 2023

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.

Research

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

Mapping the Pathways: A Comparative Analysis of AI/ML/DS Prerequisite Structures in R1 Institutions in the United States
Rose Niousha, Dev Ahluwalia, Michael Wu, Lisa Zhang, Narges Norouzi
Pending ITiCSE 2024
arXiv / pdf / code

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.

Service
Berkeley EECS Logo TA, CS 188: Artificial Intelligence, Spring 2024

TA, CS 188: Artificial Intelligence, Fall 2023

TA, CS 188: Artificial Intelligence, Summer 2023

Tutor, CS 61B: Data Structures, Spring 2023

Tutor, CS 61A: Structure and Interpretation of Computer Programs, Fall 2022

Academic Intern, CS 70: Discrete Mathematics and Probability Theory, Spring 2023

Academic Intern, CS 61A: Structure and Interpretation of Computer Programs, Spring 2022
Berkeley Haas Logo TA, UGBA 102B: Managerial Accounting, Spring 2024

Head TA, UGBA 131: Corporate Finance and Financial Statement Analysis, Summer 2023
HKN Crest Service Officer, Eta Kappa Nu (HKN) Honor Society, Fall 2022

Student Relations Officer, Eta Kappa Nu (HKN) Honor Society, Spring 2022

Assistant Student Relations Officer, Eta Kappa Nu (HKN) Honor Society, Spring 2024

Website template from Jon Barron.