Ya Ju Fan
About Ya Ju Fan
Ya Ju Fan is a Computational Scientist with a PhD in Industrial & Systems Engineering from Rutgers University and an MS in Decision Science/Operations Research from the University of Wisconsin-Madison. She has over a decade of experience at Lawrence Livermore National Laboratory, focusing on optimization models, algorithms, and advanced machine learning methods.
Work at Lawrence Livermore National Laboratory
Ya Ju Fan has been employed at Lawrence Livermore National Laboratory since 2013, holding the position of Computational Scientist. In this role, she focuses on the development of advanced machine learning methods aimed at event detection and pattern recognition. Prior to her current position, she worked at the same laboratory as a Postdoctoral Researcher from 2010 to 2013, where she contributed to various research initiatives.
Education and Expertise
Ya Ju Fan earned her PhD in Industrial & Systems Engineering from Rutgers University, where she studied from 2006 to 2010. She also holds a Master of Science degree in Decision Science/Operations Research from the University of Wisconsin-Madison, completed between 2002 and 2005. Her educational background provides her with a strong foundation in optimization models and algorithms, which she applies in her current research.
Background
Ya Ju Fan has a robust academic and professional background in computational science and engineering. Her studies in Industrial & Systems Engineering and Decision Science/Operations Research have equipped her with the skills necessary to interpret complex mathematical concepts and apply them to practical problems. This background supports her work in developing innovative solutions in the field of computational science.
Achievements
Throughout her career, Ya Ju Fan has made significant contributions to the field of computational science, particularly in the areas of optimization and machine learning. Her work involves selecting and implementing optimization models and algorithms, which are essential for solving complex problems. Additionally, her focus on developing advanced machine learning methods enhances capabilities in event detection and pattern recognition.