Daniel Gardner

Background

About Daniel Gardner

I’m a Mechanical Engineering PhD candidate with a passion for solving complex problems at the intersection of physics, data, and engineering. My research blends rigorous experimental design and automation with advanced statistical modeling and simulation to tackle challenges in thermal and energy systems, with a particular focus on stochastic nucleation phenomena. This work aims to enhance predictive reliability and enable robust uncertainty quantification, unlocking performance gains in reliability-critical applications. Through hands-on experience at Idaho National Laboratory and Sandia National Laboratories, coupled with my doctoral work at Utah State University, I’ve developed a versatile and applied skill set. At USU, I designed and built an automated experimental system using LabVIEW for system control and data acquisition, and developed specialized statistical approaches based on Bayesian inference and bias-corrected estimation to significantly improve prediction accuracy for thermal energy storage applications. I'm equally comfortable designing and building an automated experiment as I am developing a Monte Carlo simulation or creating a sophisticated statistical inference pipeline. Beyond the technical work, I’ve mentored undergraduate researchers, served as a journal reviewer, and value working carefully, creatively, and collaboratively. Now, I’m looking to bring this integrated approach and analytical rigor to an R&D environment — ideally one that values deep technical thinking, creativity, and mission-driven impact. Let’s connect if you're interested in collaborative problem-solving in complex physical systems.