Shuyue Yu
Ph.D. Student
(co-advised by Prof. Ethan Katz-Basset and Prof. Gil Zussman)
Computer Science
Columbia University
Office: CEPSR 801
Email: sy3011[at]columbia.edu
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.
Education
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
Publications and Peer-Reviewed Posters/Talks
- Shuyue Yu, Thomas Koch, Ilgar Mammadov, Hangpu Cao, Gil Zussman, and Ethan Katz-Bassett. 2025. Internet Service Usage and Delivery As Seen From a Residential Network. Proc. ACM Meas. Anal. Comput. Syst. 9, 2, Article 41 (2025).
- 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.
- 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. Talk: The Role of DNS in Residential Internet Use. In DNS OARC 41.
Research Projects
- Understanding Residential Internet Usage
Sept 2021 – now
○ Built a pipeline that continuously collects and anonymizes network traces of graduate students, faculty, and their families living in residential buildings of Columbia University.
○ Constructed an Internet traffic map of services and infrastructure accessed, and the routes and performance to them.
○ Investigated the use of DNS and the violations of DNS TTL in the setting of residential networks, discovering that 80% of Microsoft traffic is sent more than 5 minutes after TTL expiration. - Opportunistic Weather Sensing and Network Slice Admission Control
Jan 2022 – now
○ Developed a pipeline that continuously collects measurements of link attenuation from a city-wide wireless network in NYC.
○ Designed network slice admission control algorithms to adapt to link attenuation caused by weather.
○ Built a simulator with real-world user requests and link attenuation to evaluate algorithm performance. - Audio Recovery from Early 20th-Century Voice Recording Postcards
June 2020– May 2021
○ Recovered audio by registering four scans of an audio postcard from 1905 at different orientations, recovering its surface shape, and traversing a spiral path to extract the encoded audio.
○ Improved the audio quality by creating artificial test data and implementing new methods such as Thin Plate Spline to better align the warped images. - Cascading Failure Simulation
Sept 2019 – June 2020
○ Simulated the cascading failures on multiple IEEE power networks to compare the performance of the approach with tree partition and unified controller against the traditional approach.
○ Explored and evaluated various constraint relaxation policies with simulations.
Work Experience
- Netflix
Intern, May 2024 – Aug 2024
○ Developed and evaluated approaches to generate synthetic network throughput traces to test what-if scenarios for live streaming.
○ Investigated the differences between Video-on-Demand and live streaming and uncovered issues to help improve the accuracy of the live simulator. - 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. - 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.
○ Pre-processed movie script data with NLTK and grammar-check APIs.
Awards
- Summer Undergraduate Research Fellowship, Caltech, 2020
