CV
Education
- B.S. in Computer Engineering, University of Illinois Urbana-Champaign, Aug 2022 - May 2026 (Expected)
- Mathematics Minor;
- Grainger Engineering Edmund J. James Scholar;
Skills
- Languages: C/C++, Assembly, Rust, SystemVerilog, CUDA, C#, Java, Python, SQL;
- Platforms/Tools: Linux, WIN32, Git, Makefile, Linker, QEMU, GDB, UEFI, Arduino, Docker, Latex;
Research Experience (Published ONLY)
- February 2025 - May 2025: EMT-Linux Flattened Page Table (FPT) Support1
- Supervisor: Siyuan Chai, Professor Tianyin Xu
- Implemented the FPT idea in Linux through the EMT framework, reused and extended existing page-management, TLB-management, and address-translation mechanisms, while retaining backward compatibility with radix paging;
- Built a DynamoRIO-based measurement harness to quantify page-walk latency, IPC, and cache/TLB statistics, showing up to 15.3% lower page-walk latency, 3.7% fewer cycles, and 3.6% higher IPC versus baseline radix paging;
- Automated a reproducible evaluation pipeline, contributing to the acquisition of USENIX Reproducibility Badge.
- April 2024 - January 2025: MAINTVISION2
- Supervisor: Beitong Tian, Professor Klara Nahrstedt
- Developed an embedded system on ESP32-CAM to compute analog gauges’ real-time readings by computer vision, targeting analog gauges embedded in huge equipments and achieving 7.5 readings per second with \(\pm 1.81^\circ\) accuracy;
- Designed alerting and image-buffering mechanisms to notify users if reading deviates from the predefined thresholds;
- Built a communication system between devices and the server by the MQTT protocol, to enable centralized control.
Publications
Siyuan Chai, Jiyuan Zhang, Jongyul Kim, Alan Wang, Fan Chung, Jovan Stojkovic, Weiwei Jia, Dimitrios Skarlatos, Josep Torrellas, and Tianyin Xu. 2025. EMT: an OS framework for new memory translation architectures. In: Proceedings of the 19th USENIX Conference on Operating Systems Design and Implementation (OSDI ‘25). USENIX Association, USA, Article 39, 711–729. ↩
Beitong Tian, Mingyuan Wu, Ruixiao Zhang, Haozhen Zheng, Bo Chen, Yaohui Wang, Shiv Trivedi, Shanbo Zhang, Robert Bruce Kaufman, Leah Espenhahn, Gianni Pezzarossi, Mauro Sardela, John Dallesasse, and Klara Nahrstedt. “GaugeTracker: AI - Powered Cost-Effective Analog Gauge Monitoring System”. In: 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). 2024, pp. 477–483. ↩
