Spencer G.
About Spencer G.
Spencer G. is a Research Fellow at The National Institutes of Health in Bethesda, Maryland, where he has worked since 2019. He specializes in the segmentation of images from tissue samples and develops deep learning algorithms to improve biomedical imaging protocols.
Work at National Institutes of Health
Spencer G. has been a Research Fellow at the National Institutes of Health (NIH) since 2019. Located in Bethesda, Maryland, Spencer focuses on the segmentation of images from tissue samples within the Germain Lab. This role involves developing deep learning algorithms and user-facing software aimed at enhancing biomedical imaging protocols. Spencer's work contributes to advancements in medical research and diagnostic processes.
Education and Expertise
Spencer G. earned a Bachelor of Arts in Computer Science from Macalester College, completing the program from 2015 to 2019. During undergraduate studies, Spencer minored in mathematics, chemistry, and biology, which provided a strong interdisciplinary foundation. This educational background supports Spencer's current research focus and expertise in software development and machine learning.
Background in Academia
Before joining the NIH, Spencer G. worked at Macalester College from 2016 to 2019. In this role, Spencer served as a Mathematics/Science Tutor, Supervisor, and Representative, providing academic support to fellow students. Additionally, Spencer worked as an Organic Chemistry Lab Teaching Assistant for three months in 2017, further enhancing teaching and mentoring skills.
Experience at FDA
In 2018, Spencer G. gained experience as a Research Student at the Food and Drug Administration (FDA) in White Oak, Maryland. This two-month position allowed Spencer to engage with regulatory science and contribute to research efforts within the agency, expanding the practical application of academic knowledge in a governmental context.
Career Aspirations
Spencer G. seeks positions that leverage expertise in software development and machine learning for societal benefit. This goal reflects a commitment to applying technical skills in ways that positively impact public health and enhance biomedical research methodologies.