Henry Ehrenberg
About Henry Ehrenberg
Henry Ehrenberg is a co-founder of Snorkel AI, where he has worked since 2019. He previously held positions at Facebook and Stanford University, contributing significantly to the development of machine learning technologies and data programming.
Work at Snorkel AI
Henry Ehrenberg is a Co-Founder at Snorkel AI, a position he has held since 2019. He has played a significant role in the development of Snorkel Flow, a platform designed for the rapid creation of training data for machine learning models. Ehrenberg has also contributed to the open-source project Snorkel, which is widely used in both academia and industry for data labeling and management. His work has focused on programmatic labeling and weak supervision techniques, which are essential for the functionality of the Snorkel platform.
Education and Expertise
Ehrenberg holds a Bachelor of Science (BS) in Applied Mathematics from Yale University, where he studied from 2011 to 2015. He furthered his education at Stanford University, earning a Master of Science (MS) in Computational and Mathematical Engineering with a focus on Data Science from 2015 to 2017. His academic background provides a strong foundation for his expertise in data science and machine learning, particularly in the areas of data programming and weak supervision.
Background
Before co-founding Snorkel AI, Ehrenberg gained extensive experience in research and data science. He worked as a Research Assistant at Yale School of Medicine and the Weizmann Institute of Science, where he contributed to image processing and computational biology projects. He also held positions at Bluenose Analytics, Inc. as a Data Science Intern and at Facebook as a Quantitative Engineering Intern and later as a Senior Applied Research Scientist. His diverse background has equipped him with a comprehensive understanding of data analysis and machine learning applications.
Research Contributions
Ehrenberg has co-authored multiple papers that contribute to the field of data programming and machine learning. Notably, he co-authored 'Data Programming With DDLite: Putting Humans in a Different Part of the Loop,' published in SIGMOD, and 'Snorkel: Rapid Training Data Creation With Weak Supervision,' published in VLDB. His research has been integral to the foundational concepts behind Snorkel AI's technology, and he has contributed to over 60 peer-reviewed publications as part of the Snorkel research project.