Category: EECS: Computer Science and Engineering
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AI system discovers visual categories while adapting to new contexts
Open ad-hoc categorization approach combines language guidance with visual clustering to learn contextualized features for flexible image interpretation.
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AI leaderboards can be trustworthy by following these tips
Methods for ranking chess players and athletes don’t always translate to AI. U-M researchers identify and outline best practices.
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Improving AI models: Automated tool detects silent errors in deep learning training
TrainCheck uses training invariants to find the root cause of hard-to-detect errors before they cause downstream problems, saving time and resources.
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Waymo co-CEO Dmitri Dolgov discusses “prescience and patience”
The CSE alum details his journey from mathematical abstractions to vehicles safely navigating city streets.
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Advanced computer architecture and interdisciplinary computing
NAE profile: Daniel E. Atkins, interdisciplinary engineering, computer science and engineering
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A new era in AI health research: A Q&A with Jenna Wiens
A co-director of U-M’s AI & Digital Health Innovation discusses the custom-built digital environment that will power tomorrow’s health AI research.
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Mark Daskin and Mark Guzdial named AAAS Fellows
Fellowship in the AAAS is one of the highest honors accorded to US researchers.
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Digital-learning platform improves reading growth by 9 percentile points, bridging learning gaps
Implementing a conventional reading curriculum as digital, map-like lessons with voice recording features made it engaging for students of all reading levels.
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Boosting AI model size and training speed with lightwave-connected chips
AI growth is capped by data transfer rates between computing chips, but transferring data with light could remove the ceiling.
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Up to 30% of the power used to train AI is wasted. Here’s how to fix it.
Smarter use of processor speeds saves energy without compromising training speed and performance.
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Accounting for bias in medical data helps prevent AI from amplifying racial disparity
Some sick Black patients are likely labeled as “healthy” in AI datasets due to inequitable medical testing.
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Four election vulnerabilities uncovered by a Michigan Engineer
All have solutions, some are implemented.