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Showing News articles tagged with Intelligent systems

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  • A prototype robot with an ultraviolet C germicidal lamp at the top.
    Inspired by a hospital stay, a Tufts team creates a better and cheaper ultraviolet C light device for indoor spaces.
  • Tyler Morris, EG21
    Tyler Morris, EG21, and Graduate Dean of Engineering Karen Panetta utilized machine learning to help musicians record music cleanly, distinguishing between various instruments and identifying extraneous noises.
  • Ab Mosca presenting on their research
    PhD candidate Ab Mosca received a School of Engineering Outstanding Graduate Contributor to Engineering Education Award, and accepted a new teaching position at Northeastern University.
  • Chart showing increase in size of deep learning recommendation models
    Associate Professor Mark Hempstead and colleagues were nominated for the ISPASS 2021 Best Paper Award for their research on distributing deep learning recommendation models across multiple servers.
  • Usman Khan
    With the profusion of data being generated by us and our devices, distributed learning offers a new way of analyzing data while preserving more privacy, says Associate Professor Usman Khan.
  • Alum Joshua Rapp
    Joshua Rapp, E14, parleyed his interests in music and electrical engineering into a career studying computational imaging.
  • Marty Allen, an associate teaching professor, outside on campus

    In his new computer science course, Associate Teaching Professor Marty Allen challenges students to ponder the bigger and messier questions raised by technology.

  • the application of a smart bandage
    On the 100th anniversary of the Band-Aid, Professor Sameer Sonkusale and the Nano Lab are working to make smart bandages that actively monitor and deliver precisely targeted treatments to chronic wounds.
  • Computer memory chip
    Associate Professor Mark Hempstead and colleagues proposed a new near-memory hardware/software solution that could provide significant memory energy savings.
  • Lung X-ray images, from left, of COVID-19, normal, and viral pneumonia patients.
    Software developed at Tufts has been successful at identifying COVID-19 pneumonia in more than 99 percent of the X-ray images it processes.

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