Jingyuan Zhu
SWE in AIML @ Apple
Ph.D in CS @ UMich
Santa Clara, CA
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I’m a Software Engineer in the Applebot team (AIML - Machine Learning Platform Infrastructure). I received my PhD in Computer Science from the University of Michigan in October 2025, advised by Prof. Harsha V. Madhyastha. My research interests lie in the area of Web Systems. In my PhD, I focused on studying and deriving solutions for link rot on the modern web. I investigated the limitations of current approaches, built systems that automatically revive dead links and improve the fidelity of archived web pages, and developed techniques to optimize the trade-off between web-archival efficiency, fidelity, and storage overhead.
Before my PhD, I earned dual B.S.E. degrees in Computer Science from the University of Michigan and in Electrical and Computer Engineering from Shanghai Jiao Tong University.
Outside of work and research, I have a wide range of interests. I enjoy watching sports—especially soccer (a devoted Barcelona fan) and Formula 1 (a proud Tifosi). I also like playing video games across many genres—pretty much anything except FPS🤣 When not in front of a screen, I enjoy running and am training for my first half marathon🏃♂️
Publications
- Toward Better Efficiency vs. Fidelity Tradeoffs in Web ArchivesIn Proceedings of the 2025 ACM Internet Measurement Conference. IMC25
- Sprinter: Speeding up High-Fidelity Crawling of the Modern WebIn 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 2024). NSDI24
- Reviving Dead Links on the Web with FABLEIn Proceedings of the 23nd ACM Internet Measurement Conference. IMC23
- Making links on your web pages last longer than youIn Proceedings of the 21st ACM Workshop on Hot Topics in Networks. HotNets22
- Characterizing "permanently dead" links on WikipediaIn Proceedings of the 22nd ACM Internet Measurement Conference. IMC22
- Jawa: Web Archival in the Era of {JavaScript}In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22). OSDI22
- CPU microarchitectural performance characterization of cloud video transcodingIn 2020 IEEE International Symposium on Workload Characterization (IISWC). IISWC20