Alex Ratner
Project Lead, Snorkel

Alex Ratner is the project lead of Snorkel, a system for programmatically building and managing training datasets for machine learning, and (starting in 2020) an assistant professor of computer science at the University of Washington. Previously, he completed his PhD in CS advised by Christopher Ré at Stanford, where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows, such as creating and managing training data, and applying this to real-world problems in medicine, knowledge base construction, and more. At Stanford, he started and led the Snorkel project, which has been deployed at large technology companies like Google, academic labs, and government agencies and was recognized in VLDB 2018 (“Best Of”).

Sessions

11:05am–11:45am Thursday, September 12, 2019

Location: 230 C

Building and managing training datasets for ML with Snorkel

Implementing AI

Secondary topics: Data, Data Networks, Data Quality, Health and Medicine

Alex Ratner (Snorkel)

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Alex Ratner explores programmatic approaches to building, managing, and modeling training data for machine learning (ML) using the open source framework Snorkel. Training data is increasingly one of the key bottlenecks to using modern ML, and Alex outlines recent systems and algorithmic and theoretical advances in building and managing training data for ML. Read more.