Konstantinos Parasyris
About Konstantinos Parasyris
Konstantinos Parasyris is a computer scientist currently working at Lawrence Livermore National Laboratory, where he focuses on reducing energy consumption in modern CPU systems. He has a background in research from various institutions and holds a PhD from the University of Thessaly.
Work at Lawrence Livermore National Laboratory
Konstantinos Parasyris currently serves as a Computer Scientist at Lawrence Livermore National Laboratory, a position he has held since 2021. Prior to this role, he worked as a Postdoctoral Researcher at the same institution from 2020 to 2021. His work focuses on innovative approaches to energy consumption in CPU systems, contributing to the laboratory's mission of advancing scientific research and technology.
Education and Expertise
Konstantinos Parasyris completed his education at the University of Thessaly, where he earned a Bachelor's degree in Computer & Communication Engineering from 2007 to 2013. He furthered his studies with a Master's degree in Electrical and Computer Engineering from 2015 to 2018, followed by a Doctor of Philosophy (PhD) in the same field. His academic background provides a strong foundation for his research in energy-efficient computing.
Background in Research
Before joining Lawrence Livermore National Laboratory, Konstantinos Parasyris worked as a Researcher at the Centre for Research & Technology Hellas (CERTH) from 2013 to 2016. He then continued his research career at the University of Thessaly from 2016 to 2018. His experience in these roles has equipped him with a diverse skill set in computer science and engineering.
Research Focus on Energy Efficiency
Konstantinos Parasyris specializes in reducing energy consumption in modern CPU systems through Significance Aware Computing. His research investigates the varying significance of computations within programs, aiming to optimize energy use. He also explores applications in Approximate and Unreliable Computing, focusing on enhancing performance and energy efficiency in computing systems.