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  1. Nitin Vaidya. Distributed Computing (DISC) Group. Prof. Vaidya and his students perform research on topics in distributed computing, with an emphasis on design and theoretical analysis of distributed algorithms. Ongoing research addresses the following three areas:

    • Home

      Distributed Computing (DISC) Group Prof. Vaidya and his...

    • Publications

      Nitin Vaidya, Anurag Dugar, Seema Gupta, Victor Bahl,...

    • Awards

      Vartika Bhandari and Nitin H. Vaidya, in the 8th ACM...

    • Projects

      Nitin H. Vaidya (This webpage is outdated.) Home; People;...

    • DISC Seminar

      September 15, 2017, Friday (11:00 am, 239 CSL): Dr. Kishori...

    • Gallery

      "Family Tree" at MobiCom 2014 at Maui (Back, from left)...

    • Net-X

      A Wireless Networking Framework providing System eXtensions...

  2. Nitin H. Vaidya McDevitt Chair Department of Computer Science Georgetown University nitin.vaidya@georgetown.edu 332 St. Mary's Hall

  3. A leading media professional with experience of more than 30 years ranging from Journalism to Film, Digital and entertainment media Business. Successful...

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  4. Nitin Vaidya Productions is currently serving as a platform for Indian Film Industries in both India & UK. We're the first production firm who provides the services to set it up in UK.

  5. Nitin H. Vaidya (Fellow, IEEE) received the bachelor's degree from the Birla Institute of Technology and Science-Pilani, Pilani, Rajasthan, India, the master's degree from the Indian Institute of Science-Bangalore, Bengaluru, Karnataka, India, and the Ph.D. degree from the University of Massachusetts at Amherst, Amherst, MA, USA.

  6. 4866. 1998. Multi-channel MAC for ad hoc networks: Handling multi-channel hidden terminals using a single transceiver. J So, NH Vaidya. Proceedings of the 5th ACM international symposium on Mobile ad hoc …. , 2004. 1896. 2004. A rate-adaptive MAC protocol for multi-hop wireless networks.

  7. Nirupam Gupta, Shuo Liu, Nitin H. Vaidya: Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination. DSN Workshops 2021: 175-181