Jack Mc Arthur
About Jack Mc Arthur
Jack McArthur is an undergraduate research assistant in the Qiu Group at Yale University, where he conducts CO2-recycling research and teaches courses on energy materials. He has a background in physics and chemistry, having coauthored a book chapter on machine learning in nanotechnology research.
Undergraduate Research Assistant at Yale University
Jack Mc Arthur currently serves as an Undergraduate Research Assistant in the Qiu Group at Yale University. His research focuses on CO2-recycling, contributing to advancements in environmental sustainability. He has been involved in this role since 2020, utilizing resources from the National Energy Research Scientific Computing Center (NERSC) to enhance his research capabilities.
Education and Expertise
Jack Mc Arthur studied at Yale University, where he pursued a double major in Physics and Chemistry, along with an Energy Studies Certificate, from 2018 to 2022. He also attended the North Carolina School of Science and Mathematics, achieving a High School Diploma from 2016 to 2018. His academic background equips him with a strong foundation in scientific principles and interdisciplinary studies.
Teaching Experience at Yale University
In addition to his research roles, Jack Mc Arthur has served as a Science and Quantitative Reasoning Tutor at Yale University since 2020. He also worked as an Undergraduate Learning Assistant for five months in 2021. His teaching responsibilities include instructing a course on the optical and electronic properties of energy materials, demonstrating his commitment to education and mentorship.
Previous Experience in Education
Prior to his time at Yale, Jack Mc Arthur worked as a Chemistry Teaching Assistant at the North Carolina School of Science and Mathematics from 2017 to 2018. This role involved supporting students in their chemistry studies, further developing his teaching skills and understanding of scientific concepts.
Research Contributions and Projects
Jack Mc Arthur is involved in coauthoring a book chapter on machine learning in nanotechnology research. He has also implemented methods to streamline field-theoretic computations in Python for semiconductor research, showcasing his technical skills and contributions to the scientific community.