Liam Shaughnessy
About Liam Shaughnessy
Liam Shaughnessy is a Graduate Student Researcher at Yale University, specializing in Engineering Physics and Applied Physics. His research focuses on signal propagation through disordered media and the integration of machine learning with traditional sensing techniques.
Current Role at Yale University
Liam Shaughnessy is currently employed as a Graduate Student Researcher at Yale University. He has held this position since 2020, contributing to research in the field of Engineering Physics and Applied Physics. His work primarily involves studying signal propagation through disordered media and conducting transmission matrix measurements. This role allows him to engage in advanced research and collaborate with faculty and peers in a prestigious academic environment.
Previous Experience at University of Maryland
Before joining Yale University, Liam Shaughnessy worked at the University of Maryland Institute for Advanced Computer Studies as a Machine Learning Developer and Hardware Engineer from 2018 to 2020. In this role, he focused on merging machine learning techniques with traditional sensing methods to improve measurement techniques in optical physics. Additionally, he served as an Undergraduate Student Researcher at the University of Maryland for 11 months in 2016 and 2017, where he gained foundational research experience.
Educational Background
Liam Shaughnessy completed his Bachelor's degree in Physics at the University of Maryland from 2014 to 2018. His academic journey continued at Yale University, where he has been pursuing studies in Engineering Physics and Applied Physics since 2020. This educational background has equipped him with a solid foundation in both theoretical and practical aspects of physics and engineering.
Research Contributions
Liam Shaughnessy's research contributions include significant work in the area of signal propagation through disordered media. He is involved in transmission matrix measurements, which are critical for understanding complex systems in physics. His research also emphasizes the development of neuromorphic hardware using Field-Programmable Gate Arrays (FPGA) for machine learning applications, showcasing his ability to integrate hardware engineering with advanced computational techniques.