Roland Green
About Roland Green
Roland Green is a Senior Member of Technical Staff at Cerebras Systems, where he has worked since 2020. He holds a PhD in Computer Engineering from Purdue University and has extensive experience in research and engineering roles across various institutions.
Work at Cerebras Systems
Roland Green has been employed at Cerebras Systems as a Senior Member of Technical Staff since 2020. In this role, he has contributed to the development of performance debug and regression analysis tools. He has also designed and developed an MLIR unit testing framework and managed MLIR generalizability projects. His work focuses on enhancing the efficiency and effectiveness of machine learning infrastructure.
Education and Expertise
Roland Green holds a Doctor of Philosophy (PhD) in Computer Engineering from Purdue University, where he studied from 2017 to 2020. Prior to this, he earned a Bachelor of Science in Electrical and Electronics Engineering from The University of Texas at San Antonio, studying from 2015 to 2017. He also completed a Bachelor's Degree in Engineering Science at Trinity University in 2014. His educational background provides a strong foundation in engineering and computer science.
Background
Roland Green has a diverse professional background that includes various roles in research and technical support. He began his career as an Undergraduate Research Assistant at The University of Texas at San Antonio in 2017. He has also held internships at Harland Clarke and worked as an Undergraduate Senior Associate and Analyst at Dell. His experience spans multiple institutions, including Trinity University and Purdue University, where he served as a Teaching Assistant and Doctoral Fellow, respectively.
Achievements
During his tenure at Cerebras Systems, Roland Green has developed significant tools and frameworks that contribute to the company's machine learning capabilities. His work on performance debug and regression analysis tools, as well as the MLIR unit testing framework, showcases his technical skills and commitment to advancing technology in the field. Additionally, he has managed projects aimed at improving MLIR generalizability, further demonstrating his expertise.