Zachary Lipton
About Zachary Lipton
Zachary Lipton is a Chief Technology Officer and Raj Reddy Associate Professor of Machine Learning at Carnegie Mellon University. He specializes in integrating deep learning with theoretical concepts and directs the Approximately Correct Machine Intelligence lab.
Current Role at Abridge
Zachary Lipton serves as the Chief Technology Officer and Chief Scientific Officer at Abridge, a position he has held since 2023. In this role, he focuses on integrating modern deep learning tools with theoretical machine learning concepts. His work aims to enhance the capabilities of machine learning algorithms, particularly in the context of healthcare applications.
Academic Position at Carnegie Mellon University
Since 2024, Lipton has been a Raj Reddy Associate Professor of Machine Learning at Carnegie Mellon University. His academic work emphasizes leveraging causal structures in data to improve machine learning algorithms. He previously served as an Assistant Professor at the same institution from 2018 to 2024.
Education and Expertise
Lipton holds a Doctor of Philosophy (PhD) in Computer Science from the University of California, San Diego, where he studied from 2013 to 2017. He also earned a Master’s Degree in Computer Science from the same institution. Earlier, he completed a Bachelor of Arts in Mathematics - Economics at Columbia University from 2003 to 2007.
Previous Work Experience
Lipton has a diverse work history in machine learning and data science. He worked as a Machine Learning Scientist at Amazon for three months in 2015 and spent six years as a Visiting Scientist at Amazon AI from 2017 to 2023. He also held research intern positions at Microsoft Research Labs in Bangalore and Redmond in 2014 and 2016, respectively.
Research Contributions and Publications
Lipton co-authored 'Dive Into Deep Learning,' an interactive open-source book designed for educational purposes. He directs the Approximately Correct Machine Intelligence (ACMI) lab, which focuses on developing robust and adaptive machine learning algorithms. His research also investigates the societal impacts of machine learning systems.