PeopleofSuperintelligence

Researcher

Xiang Chen

Xiang Chen is an electrical engineer whose work connects machine learning with telecom automation and video analysis. He earned a master's degree at the University of Florida in 2011 and a doctorate in electrical engineering at the University of Washington in 2015. He joined Tupl in 2016 and coauthored a 2017 paper on human detection in a mobile surveillance cloud. His Tupl patent applications, published in 2018 and 2019, described staged machine learning for system faults and automatic classification of customer-support messages. As of October 2026, he is an affiliate professor in the University of Washington's Department of Electrical & Computer Engineering.

Works
3
Citations
79
h-index
3
Badges

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2011–2015

    Degrees in electrical engineering

    At the University of Florida, Gainesville, he earns a master's degree in electrical and computer engineering in 2011. He completes a PhD in electrical engineering at the University of Washington in 2015.

  2. 2015

    Video analysis over wireless networks

    In 2015, he coauthors “Quality-of-content (QoC)-driven rate allocation for video analysis in mobile surveillance networks” with Jenq-Neng Hwang, Kuan-Hui Lee and Ricardo L. de Queiroz at the IEEE International Workshop on Multimedia Signal Processing.

  3. 2016

    Joining Tupl

    He joins Tupl in 2016, a company that develops software automation using AI and machine learning for telecom operators.

  4. 2017

    Human detection in surveillance video

    In 2017, he coauthors “A Quality-of-Content-Based Joint Source and Channel Coding for Human Detections in a Mobile Surveillance Cloud” with Jenq-Neng Hwang, De Meng, Kuan-Hui Lee, Ricardo L. de Queiroz and Fu-Ming Yeh. The paper appears in IEEE Transactions on Circuits and Systems for Video Technology.

  5. September 2018

    Staged machine learning for faults

    In September 2018, a Tupl patent application names him as an inventor with Pablo Tapia and Rafael Guerrero. Titled “Automated decision making using staged machine learning,” it describes a sequence of models that classify system problems into increasingly specific categories.

  6. July 2019

    Classifying customer-support messages

    In July 2019, a Tupl patent application names him and Pablo Tapia as inventors of “User support system with automatic message categorization.” It uses machine learning to classify issues in customer messages and direct them to people or automated systems equipped to handle them.

  7. As of October 2026

    Affiliate professor at Washington

    As of October 2026, he is an affiliate professor in the University of Washington's Department of Electrical & Computer Engineering.

Career

  1. NowTuplData Scientist and R&D Manager, Artificial Intelligence Team
  2. NowUniversity of WashingtonAffiliate Professor, Electrical & Computer Engineering

Education

  1. 2015University of WashingtonPhD in electrical engineering
  2. 2011University of FloridaMaster’s degree in electrical and computer engineering

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Checked career sources: ee.washington.edu.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Xiang Chen yet. Send us a free-licensed photo.

  1. Xiang Chen | UW Department of Electrical & Computer Engineering ee.washington.edu. Checked 27 October 2026.
  2. Xiang Chen csauthors.net. Checked 27 October 2026.
  3. Affiliate Faculty | Person Tags | UW Department of Electrical & Computer Engineering ece.uw.edu. Checked 27 October 2026.
  4. US20180268258A1 - Automated decision making using staged machine learning - Google Patents patents.google.com. Checked 27 October 2026.
  5. WO2019140242A1 - User support system with automatic message categorization - Google Patents patents.google.com. Checked 27 October 2026.