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Guidance on decontaminating face masks: U-M researchers contribute to national effort
  1. Guidance on decontaminating face masks: U-M researchers contribute to national effort

    Collaborative website launched while U-M researchers continue advanced testing.

  2. Rackham Predoctoral Fellowship for design of robust, reliable and repairable software systems

    Subarno Banerjee uses program analysis to improve software systems’ safety and security.

  3. Predoctoral Fellowship for mathematically provable hardware design

    Goel designs algorithms that can automatically demonstrate the correctness of hardware systems.

  4. Researchers to use brain scans to understand gender bias in software development

    The team will use fMRI to identify some of the underlying processes that occur when a code reviewer weighs in on a piece of software and its author.

  5. Programming around Moore’s Law with automatic code translation

    Most programs in use today have to be completely rewritten at a very low level to reap the benefits of hardware acceleration. This system demonstrates how to make that translation automatic.

  6. Big data, small footprint

    How changing the rules of computing could lighten Big Data’s impact on the internet.

    The post Big data, small footprint appeared first on Michigan Engineering News.

  7. Todd Austin Named S. Jack Hu Collegiate Professor of Computer Science and Engineering

    Prof. Austin is a creative, outside-the-box thinker who has produced a body of work that has had extraordinary impact in the area of computer architecture.

  8. Five papers by CSE researchers presented at NSDI

    The teams designed systems for faster and more efficient distributed and large-scale computing.

  9. Real-time monitor tracks the growing use of network filters for censorship

    The team says their framework can scalably and semi-automatically monitor the use of filtering technologies for censorship at global scale.

  10. Emotion recognition has a privacy problem – here’s how to fix it

    Researchers have demonstrated the ability to “unlearn” sensitive identifying data from audio used to train machine learning models.