Timothy E. Long on Muck Rack

Timothy E. Long

As seen in: Macromolecular Materials and Engineering, Polymer International, Nature, Royal Society of Chemistry, Optica, Westlaw Today, Yachting Monthly
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Articles

Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization

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By Jie Chen, Timothy E. Long, Michael Wall, Todd Hufnagel
| Nature
Abstract High-throughput materials characterization is essential for accelerating materials discovery. To enable high-throughput characterization, machine learning (ML) has been a powerful tool. However, the broader application of ML in experimental settings is limited by key challenges, i.e., the scarcity of labeled experimental data and the lack of uncertainty estimation in model predictions.
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EXPERT INSIGHTS-PAGA PLAINTIFFS ARE LOSING THEIR HEADS!

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By Timothy E. Long, Michael Kolvek
| Westlaw Today
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Simulating ceramic-filled vat photopolymerization resins using Monte Carlo ray tracing

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By Timothy E. Long, J. Robert Mahan, Yiqun Fu, Nathan Raeker-Jordan
| Optica
Photocurable ceramic suspensions used in vat photopolymerization (VP) to produce composite and ceramic parts suffer from reduced resolution due to light scattering by the particles. When irradiated, the scattered light causes a redistribution of the UV intensity, resulting in a reduction in cure depth and an increase in cure width.
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