Columbia University

Shuyue Yu

Ph.D. Student
(co-advised by Prof. Ethan Katz-Basset and Prof. Gil Zussman)

Computer Science
Columbia University

Office: 489 CSB
Email: sy3011[at]

I am currently a Ph.D. student in the Department of Computer Science at Columbia University. I received my B.S. in Computer Science and Information and Data Science from California Institute of Technology in 2021. My research interests include Internet measurement, networked systems, design and analysis of network algorithms, and data analysis.


Columbia University

  • M.S./Ph.D. in Computer Science, Sept. 2021 – Present
    Co-advised by Prof. Ethan Katz-Bassett and Prof. Gil Zussman

California Institute of Technology

  • B.S. in Computer Science & Information and Data Science, Sept 2017-June 2021, GPA: 4.2/4.3


  • Thomas Koch, Shuyue Yu, Sharad Agarwal, Ethan Katz-Bassett, and Ryan Beckett. 2023. PAINTER: Ingress Traffic Engineering and Routing for Enterprise Cloud Networks. In ACM SIGCOMM 2023.
  • Chen Liang, Linqi Guo, Alessandro Zocca, Shuyue Yu, Steven H. Low, Adam Wierman. 2021. An integrated approach for failure mitigation & localization in power systems. Electric Power Systems Research 190 (2021), 100613.

Posters and Talks

  • Shuyue Yu, Thomas Koch, Gil Zussman, and Ethan Katz-Bassett. 2023. Poster: An Internet Traffic Map of Service Delivery Patterns as Seen from a Residential Network. In N2Women Workshop 2023.
  • Shuyue Yu, Thomas Koch, Gil Zussman, and Ethan Katz-Bassett. 2023. The Role of DNS in Residential Internet Use. In DNS OARC 41.


  • Summer Undergraduate Research Fellowship, Caltech, 2020

Work Experience

  • Microsoft | Garage Internship
    Software Engineer Intern, June 2019 – Sep 2019
    ○ Developed an innovative data visualization application for HoloLens and virtual reality that will change the way security teams think about vulnerabilities in a service environment.
    ○ Built from the ground up with custom microservices, a bot for communication, and 3D models, using technologies including Unity, C#, Seneca, and Node.js.
    ○ Delivered a quality product within 12 weeks that allows security teams to interact with and be immersed in the data that is central to their work.
  • RivetAI, INC.
    Data Science Intern, June 2018 – Aug 2018
    ○ Trained a deep learning model for story generation and evaluated the generative AI models (e.g. Seq2seq,VRNN, Adversarial Learning) and their variations.
    ○ Tested the methods for preprocessing data with NLTK.
    ○ Researched the grammar-check APIs and integrated their results with Python