Michigan Engineering | University of Michigan CE undergrad Amulya Parmar designed a machine learning algorithm to curb fake news as part of the Tavtech Fellowship program.
Blog
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Most powerful laser in the US to be built at Michigan
Using extreme light to explore quantum dynamics, advance medicine and more.
The post Most powerful laser in the US to be built at Michigan appeared first on Michigan Engineering News.
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The “Magic Ratio” that could power tomorrow’s solar cells
A Q&A with Rachel Goldman
The post The “Magic Ratio” that could power tomorrow’s solar cells appeared first on Michigan Engineering News.
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U-M team to build synthetic neurons – first challenge in making synthetic cells
Seven U.S. research institutions look to build synthetic cells.
The post U-M team to build synthetic neurons – first challenge in making synthetic cells appeared first on Michigan Engineering News.
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Electrify goes to Detroit
Electrify hosted its first Detroit Tech Camp at the Michigan Engineering Zone this summer to give Detroit-area students greater access to engage with science, technology, engineering, and mathematics.
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New attack on autonomous vehicle sensors creates fake obstacles
Up to this point, no attacks had been discovered targeting a car’s LiDAR system—but a major new finding from researchers at the University of Michigan has demonstrated what that might look like.
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Rewriting the rules for supercomputers
Machine learning will teach the world’s fastest machines to work smarter, not harder.
The post Rewriting the rules for supercomputers appeared first on Engineering Research News.
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Errors found in a bedrock contract bidding model—and how to solve them. A Q&A with Photios Ioannou
A flaw has been uncovered in construction’s best known and most cited competitive bidding model.
The post Errors found in a bedrock contract bidding model—and how to solve them. A Q&A with Photios Ioannou appeared first on Michigan Engineering News.
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Taking machine-learning models in health care from concept to bedside
The authors provide an overview of common challenges to implementing ML in a health-care setting, and describe the necessity of breaking down the silos in ML.
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Creating more efficient data centers for AI
Tang’s project will redesign data center systems to support large-scale use of hardware accelerators to meet future computational demand.
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