David Fieser holds a PhD in Mechanical and Aerospace Engineering from the University of Tennessee, Knoxville. His research focuses on ultrafast laser materials processing, including femtosecond laser ablation in liquid for the synthesis of high entropy alloy nanoparticles and two temperature modeling of laser interactions with metals. His broader interests span computational materials science and materials informatics, with an emphasis on thermodynamic descriptors for high entropy alloys and oxides, phase prediction, and machine learning for pulsed and ultrafast laser processing. He develops open source scientific software, including descriptor calculators and fatigue analysis tools designed for reproducibility and integration with AI agent workflows, and he is committed to making research tools accessible, transparent, and citable.
David Fieser holds a PhD in Mechanical and Aerospace Engineering from the University of Tennessee, Knoxville. His research focuses on ultrafast laser materials processing, including femtosecond laser ablation in liquid for the synthesis of high entropy alloy nanoparticles and two temperature modeling of laser interactions with metals. His broader interests span computational materials science and materials informatics, with an emphasis on thermodynamic descriptors for high entropy alloys and oxides, phase prediction, and machine learning for pulsed and ultrafast laser processing. He develops open source scientific software, including descriptor calculators and fatigue analysis tools designed for reproducibility and integration with AI agent workflows, and he is committed to making research tools accessible, transparent, and citable.