Caleb Miller
About Caleb Miller
Caleb Miller is a Graduate Research Intern at Lawrence Livermore National Laboratory, where he has worked since 2019. He specializes in adaptive importance sampling techniques and focuses his research on Monte Carlo methods for data fusion and optimal control.
Work at Lawrence Livermore National Laboratory
Caleb Miller has been a Graduate Research Intern at Lawrence Livermore National Laboratory since 2019. His role involves engaging in advanced research projects, applying his expertise in adaptive importance sampling techniques. He previously participated in the Data Science Summer Institute at the same laboratory in 2019, where he contributed to data science initiatives for three months. His ongoing work at Lawrence Livermore National Laboratory reflects a commitment to research excellence in a leading scientific environment.
Education and Expertise
Caleb Miller studied at Los Medanos College from 2010 to 2012, where he began his academic journey in mathematics. He then attended California Polytechnic State University, earning a Master of Science (MS) in Mathematics from 2012 to 2015. Currently, he is a Ph.D. candidate at the University of Colorado Boulder, focusing on Applied Mathematics. His research emphasizes Monte Carlo methods, particularly for applications in data fusion and optimal control, showcasing his specialization in adaptive importance sampling techniques.
Background
Caleb Miller's academic and professional background includes significant experience in mathematics and research. He worked as a Grader and Math Researcher at California Polytechnic State University from 2012 to 2016. Additionally, he served as a Tutor at Los Medanos College for one year. His diverse roles in educational institutions have contributed to his development as a researcher and educator in the field of mathematics.
Research Focus and Contributions
During his Ph.D. studies at the University of Colorado Boulder, Caleb Miller has concentrated on Monte Carlo methods, which are crucial for various applications in data fusion and optimal control. His research on adaptive importance sampling techniques aims to enhance the efficiency and accuracy of these methods. This focus reflects his commitment to advancing mathematical applications in real-world scenarios.