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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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modeling is the parametrization of the force field. There are a large number of force fields in existence and significant efforts are spent on their development and improvement. However, to-date, development
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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/ThreeBodyTB.jl), cluster expansion, classical potential development, and machine learning. In addition to work on specific problems, I work on developing new first principles-based modeling approaches, including
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to consider multidimensional landscapes. The goal of this research project is to develop models that can be used to evaluate the stability and predict transitions as cell populations progress from pluripotent
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driven flows; Combustion; Computational fluid dynamics; Fire modeling; Heat transfer; Large eddy simulation; Numerical combustion; Thermal radiation; Turbulent flows; Eligibility citizenship Open to U.S
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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address
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and data-driven models [2, 3]. Metric Identification: Identifying key quality metrics for various "digital objects" throughout the ICME development lifecycle. Uncertainty Quantification & Propagation
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using a data-driven landscape model. PNAS 109,19262-19267 Bhadriraju K, et al. (2016) Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies. Stem Cell Research (17
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are interested in project ideas that focus on the development and application of measurement techniques, physics-based or data-driven models, and standards to better understand and improve metals-based AM. Our AM