Samantha Robertson
About Samantha Robertson
Samantha Robertson is a Machine Learning Engineer at Abridge, where she has worked since 2024, focusing on AI integration in healthcare. She holds a PhD in Electrical Engineering and Computer Science from the University of California, Berkeley, and has previous experience as a research intern at Google and Apple.
Work at Abridge
Samantha Robertson has been employed at Abridge as a Machine Learning Engineer since 2024. In her role, she contributes to the development of tools that enhance machine learning evaluation practices. She is involved in integrating AI-generated summaries with electronic medical records, aiming to improve clinical documentation. Additionally, she is part of a team that is pioneering generative AI applications specifically designed for healthcare settings.
Education and Expertise
Samantha Robertson earned her Doctor of Philosophy (PhD) in Electrical Engineering & Computer Science from the University of California, Berkeley, where she studied from 2019 to 2023. Prior to this, she completed her Bachelor of Science (BS) in Mathematical and Computational Science at Stanford University from 2015 to 2019. Her educational background provides a strong foundation in machine learning and computational techniques.
Previous Work Experience
Before joining Abridge, Samantha Robertson gained valuable experience through internships. She worked as a Research Intern at Google for three months in 2021 and as a Machine Learning Research Intern at Apple for four months in 2022. Additionally, she served as an Undergraduate Research Assistant at Stanford University from 2017 to 2019, where she contributed to various research projects.
Research Contributions
During her tenure at Abridge, Samantha Robertson has made significant contributions to the development of tools that enhance machine learning evaluation practices. Her work focuses on improving the integration of AI technologies within healthcare, specifically through the use of AI-generated summaries to streamline clinical documentation processes.