Sarah Wooders


I'm a first year PhD student in UC Berkeley's RISELab, where I work on systems for machine learning. My current work is focused on real-time feature stores. Before Berkeley, I founded Glisten AI, which builds AI to categorize and tag product data and was part of Y Combinator's W20 batch.

My undergraduate degree is from MIT, where I studied computer science and math and did research at CSAIL in the Supertech Group. While at MIT, I directed Code for Good, helped organize HackMIT, and interned at MemSQL, MobLab, and Bloomberg.

Press / blogs

[RISELab] Feature Stores: The Data Side of ML Pipelines
[Techcrunch] Glisten uses computer vision to break products into their most important parts
[MIT News] Taking the lead in shaping the future of computing and artificial intelligence
[MemSQL] Faster Data Loading with Adaptive Compression
[Underscore] Fashion Brought to You by AI


Allparel Text search for fashion images.
Keychain Distributed Authentication on the Ethereum Blockchain.
FashionModel Mapping Images of Clothes to an Embedding Space.


Kaler, Tim, Brian Wheatman, and Sarah Wooders. "High-throughput image alignment for connectomics using frugal snap judgments: poster." Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. 2019.