Announcing the Autonomous Research for Real-World Science (ARROWS) Workshop
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News
A Beginner’s Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
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We teamed up with Intel to create A Beginner’s Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning. This in-depth guide equips AI developers and other software professionals with the skills to make intelligent measurement decisions — from deciding at-the-wall versus on-device measurements, sampling strategies, where to look for errors, and when proxy measures are sufficient. These are vital first steps in pinpointing which optimizations and model choices have the greatest impact on sustainability.
Machine Learning for Reparameterization of Multi-scale Closures
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I’ll be at NeurIPS talking about Machine Learning for Reparameterization of Multi-scale Closures in the ML for Physical Science workshop.
HTEM AI-XRD: Preliminary Results in Large-scale deployment of AI interpretation of X-Ray Diffraction Spectra
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I’ll be at PEARC talking about our conference paper on machine learning for autonomous workflows in X-Ray Diffraction Intepretation.
AI for Materials Synthesis: From data analysis to guiding experiment
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I’ll be at DICE at INL talking about our work in AI for materials synthesis.
Machine Learning for XRD Spectra Interpretation in High-Throughput Material Science
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I’ll be at ICLR talking about our conference paper on machine learning for Advanced Building Construction.
Machine Learning for XRD Spectra Interpretation in High-Throughput Material Science
Published:
I’ll be at ICLR talking about our conference paper on machine learning for X-Ray Diffraction Intepretation.