Keita Iwabuchi
About Keita Iwabuchi
Keita Iwabuchi is a Data Scientist at Lawrence Livermore National Laboratory, where he has worked since 2020. He specializes in large-scale graph analytics and machine learning, and has a strong academic background with a Master's and Ph.D. in Computer Science from Tokyo Institute of Technology.
Work at Lawrence Livermore National Laboratory
Keita Iwabuchi has held multiple positions at Lawrence Livermore National Laboratory (LLNL) since 2014. He began as an Intern/Collaborator from 2014 to 2017, where he contributed to various projects. Following this, he served as a Postdoctoral Researcher from 2017 to 2020, focusing on advanced research initiatives. Since 2020, he has been employed as a Data Scientist, continuing his work in Livermore, California. His roles at LLNL have involved significant contributions to research and development in the fields of graph analytics and machine learning.
Education and Expertise
Keita Iwabuchi earned his Master's Degree in Computer Science from the Tokyo Institute of Technology, completing his studies from 2012 to 2014. He furthered his education at the same institution, obtaining a Ph.D. in Science from 2014 to 2017. His academic background provides a strong foundation for his current work, where he specializes in designing algorithms and data structures focused on memory management and I/O for large-scale data processing.
Background
Keita Iwabuchi's professional journey began with his education at the Tokyo Institute of Technology, where he developed a strong interest in computer science. His initial work experience at Lawrence Livermore National Laboratory as an intern laid the groundwork for his subsequent roles. Over the years, he has transitioned from an intern to a postdoctoral researcher and now a data scientist, reflecting his growth and expertise in the field.
Research Focus and Specialization
In his current role as a Data Scientist, Keita Iwabuchi focuses on large-scale graph analytics and machine learning. His research and development projects aim to enhance data processing capabilities. He specializes in creating efficient algorithms and data structures that optimize memory management and input/output operations, which are critical for handling large datasets effectively.