Publications

Peer-reviewed papers on systems for real-time video conferencing.

• RTCBench: A Scalable Benchmarking Framework for Multi-Party Video Conferencing

  Evan Ram^*, Tuan Tran^*, Youngwook Son, and Sangtae Ha

  Proceedings of the 34th ACM International Conference on Multimedia (MM '26)

  Full paper; acceptance rate: 23.9% (1,703/7,103).

  RTCBench is an open-source, distributed framework that benchmarks multi-party video conferencing systems such as Jitsi Meet and Janus at scale. Lightweight Go clients emulate large conferences against production media servers, so a system's capacity and quality can be measured rather than guessed.

  PaperCode

  BibTeX

  @inproceedings{ram2026rtcbench,
    author    = {Ram, Evan and Tran, Tuan and Son, Youngwook and Ha, Sangtae},
    title     = {RTCBench: A Scalable Benchmarking Framework for Multi-Party Video Conferencing},
    booktitle = {Proceedings of the 34th ACM International Conference on Multimedia (MM '26)},
    year      = {2026},
    publisher = {Association for Computing Machinery},
    doi       = {10.1145/3767308.3835424},
    url       = {https://doi.org/10.1145/3767308.3835424}
  }

• DeepSFU: Scalable Deepfake Detection for Video Conferencing

  Tuan Tran, Shirin Ebadi, S. M. H. Hosseini, Woongsub Shin, Evan Ram, Youngwook Son, Seyeon Kim, Nam Bui, Kyunghan Lee, Eric Keller, and Sangtae Ha

  Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM '26)

  Full paper; acceptance rate: 21.2% (109/513).

  DeepSFU brings scalable, real-time deepfake detection to video conferencing, checking every participant in a live call. It cuts per-frame processing latency by 143.7x and raises conferencing capacity by 26.2x.

  Paper

  BibTeX

  @inproceedings{tran2026deepsfu,
    author    = {Tran, Tuan and Ebadi, Shirin and Hosseini, S. M. H. and Shin, Woongsub and Ram, Evan and Son, Youngwook and Kim, Seyeon and Bui, Nam and Lee, Kyunghan and Keller, Eric and Ha, Sangtae},
    title     = {DeepSFU: Scalable Deepfake Detection for Video Conferencing},
    booktitle = {Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM '26)},
    year      = {2026},
    publisher = {Association for Computing Machinery},
    doi       = {10.1145/3789240.3829184},
    url       = {https://doi.org/10.1145/3789240.3829184}
  }

^*Equal contribution