Nick Langellier
About Nick Langellier
Nick Langellier is a Senior Machine Learning Engineer at VideaHealth, where he has worked since 2022. He holds a PhD in Physics from Harvard University and has a background in engineering physics.
Work at VideaHealth
Nick Langellier currently serves as a Senior Machine Learning Engineer at VideaHealth, a position he has held since 2022. In this role, he focuses on developing and implementing machine learning solutions to enhance healthcare technology. Prior to this, he worked as a Data Scientist at VideaHealth from 2021 to 2022, where he contributed to data analysis and modeling efforts aimed at improving healthcare outcomes. Both roles are based in Boston, Massachusetts.
Education and Expertise
Nick Langellier holds a Doctor of Philosophy (PhD) in Physics from Harvard University, which he completed between 2011 and 2020. His research during this period included a focus on trojan detection in integrated circuits using machine learning techniques. He also earned a Master of Arts (MA) in Physics from Harvard University from 2011 to 2013. Prior to his graduate studies, he obtained a Bachelor of Science (BS) in Engineering Physics from the University of Illinois at Urbana-Champaign, completing his degree in 2011.
Background
Nick Langellier has a diverse academic and professional background in physics and machine learning. He began his academic journey at the University of Illinois at Urbana-Champaign, where he studied Engineering Physics. After completing his undergraduate degree, he pursued graduate studies at Harvard University, where he engaged in research related to machine learning applications in astrophysics. His career includes roles as a Graduate Student at Harvard University and as a Postdoctoral Researcher at the University of Maryland.
Research Contributions
During his time at Harvard University, Nick Langellier conducted significant research on the application of machine learning techniques to exoplanet astrophysics. His work involved exploring innovative methods to analyze complex data sets in the field of astrophysics. Additionally, he focused on the development of machine learning models for trojan detection in integrated circuits, contributing to advancements in both machine learning and electronic security.