Faculty Candidate Seminar|
Accelerating Machine Learning with Training Data Management Systems
Monday, March 11, 2019|
10:30am - 11:30am
Add to Google Calendar
About the Event
One of the key bottlenecks in building machine learning systems is creating and managing the massive training datasets that today’s models learn from. In this talk, I will describe my work on data management systems that let users specify training datasets in higher-level, faster, and more flexible ways, leading to applications that can be built in hours or days, rather than months or years.
Alex Ratner is a 5th year Ph.D. candidate advised by Christopher Ré in the Computer Science department at Stanford, where he is supported by a Stanford Bio-X fellowship. His research focuses 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. He leads the Snorkel project (snorkel.stanford.edu), which has been deployed at large technology companies, academic labs, and government agencies, and his work has been recognized in VLDB 2018 (“Best Of”).
Open to: Public