Luke Conibear

Luke Conibear

Senior Machine Learning Engineer @ Tomorrow.io

About Luke Conibear

Luke Conibear is a Senior Machine Learning Engineer at Tomorrow.io, specializing in the intersection of machine learning and environmental science. He holds a PhD in Ambient Air Quality and Human Health from the University of Leeds and has contributed to various open-source projects on GitHub.

Work at Tomorrow.io

Luke Conibear has been employed at Tomorrow.io as a Senior Machine Learning Engineer since 2022. In this role, he focuses on applying machine learning techniques to enhance weather-related services. His work contributes to the development of innovative solutions that integrate environmental data with predictive analytics.

Education and Expertise

Luke Conibear holds a Doctor of Philosophy (PhD) in Ambient Air Quality and Human Health from the University of Leeds, which he completed from 2015 to 2018. He also earned a Master of Science (MS) in Bioenergy from the same institution in 2015. Earlier, he obtained a Bachelor of Engineering (BEng) in Mechanical Engineering from University College London (UCL) from 2007 to 2010. His educational background supports his expertise in machine learning and environmental science.

Background

Before joining Tomorrow.io, Luke Conibear worked at the University of Leeds. He served as a Research Fellow from 2018 to 2021, focusing on interdisciplinary research that combines machine learning with environmental science. He then transitioned to the role of Research Software Engineer (Machine Learning) for a brief period in 2021 to 2022. His academic and research experiences have shaped his current professional focus.

Open Source Contributions

Luke Conibear actively contributes to open-source projects on GitHub, where he showcases his skills in machine learning and software development. His contributions reflect his commitment to advancing the field and sharing knowledge with the broader community.

Personal Website and Insights

Luke Conibear maintains a personal website where he shares insights and projects related to machine learning and environmental science. This platform serves as a resource for those interested in his work and the intersection of these fields.

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