Andrew Guo
About Andrew Guo
Andrew Guo is a Postdoctoral Research Fellow at Sandia National Laboratories, specializing in computational tools for quantum computers. He holds a PhD from the University of Maryland and a Bachelor's Degree in Physics from Stanford University.
Current Role at Sandia National Laboratories
Andrew Guo holds the position of Postdoctoral Research Fellow at Sandia National Laboratories. Starting his tenure in 2023, he focuses on developing computational tools aimed at enhancing the efficiency and robustness of quantum computers. Based in Berkeley, California, he contributes to the field of quantum information research in this role.
Past Experience at Flatiron Institute
In 2021, Andrew Guo served as a Summer Research Associate at the Flatiron Institute's Center for Computational Quantum Physics in New York, United States. During this two-month tenure, he gained valuable experience in computational quantum physics, contributing to ongoing research projects.
Graduate Research Fellowship at University of Maryland
From 2016 to 2022, Andrew Guo was a Graduate Research Fellow at the University of Maryland, College Park. During these six years, he conducted extensive research, ultimately earning a PhD. His work primarily involved applying model-based approaches to solve real-world problems in quantum computing.
Academic Background and Early Internships
Andrew Guo holds a Bachelor's Degree in Physics from Stanford University, where he studied from 2012 to 2016. During his undergraduate years, he interned at Manoharan Lab in 2013 and the Columbia Laboratory for Unconventional Electronics in 2012. These early internships provided him with foundational experience in research. Additionally, he completed his high school education at Scarsdale High School, achieving an H.S. Diploma in 1970.
Specialization in Quantum Computing
Andrew Guo specializes in the field of quantum computing, focusing on developing computational tools that enhance the efficiency and robustness of quantum computers. His expertise lies in applying model-based approaches to address complex real-world problems, making significant contributions to the field.