Alvin Cheung

Room 785, Soda Hall

Mailing address
387 Soda Hall
Berkeley, CA 94720-1776

I am an associate professor in the Computer Science Division at UC Berkeley EECS. I am a member of the Data Systems and Foundations group, Programming Systems group, Sky Lab, SLICE Lab, and a faculty affiliate in the Berkeley Institute for Data Science. I also serve as an advisor to the Data Science Discovery Program, and technical advisor to several companies.

My research interests include data management, programming languages, and building software systems. My group aims to help end users, from data scientists to coding experts, to easily extract insights from large amounts of data. We also develop techniques and tools that make it easy to build large-scale, efficient, and manageable data processing pipelines, where such pipelines range from traditional computer science applications (web, cloud) to emerging application domains in data science (physical sciences, healthcare, social sciences) and beyond.

Some research themes:

If you are a Berkeley student interested in doing research in these areas and have done well in CS186 or CS164 please send me an email mentioning which class(es) you have taken, and include your resume and unofficial transcript.

I regularly teach the undergraduate data management class at Berkeley. Check out the recorded videos if you are interested.

We thank the NSF, DOE, ONR, ARO, Adobe, Google, Intel, Meta, and VMware for their generous support of our group's research, and transferring our technology to real-world products. We are also grateful for the early career awards and other recognitions from the data management and programming systems research communities.

I was earlier on the CSE faculty at the University of Washington and an affiliate in the UW eScience Institute, and before that a graduate student in the MIT database group and the computer-aided programming group, working with Professors Sam Madden and Armando Solar-Lezama. I worked on tools that make use of programming language techniques to improve application performance.

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