Inho Choi

Ph.D. Student National University of Singpoare, School of Computing

inhochoi@comp.nus.edu.sg

📢 I am graduating soon this year (2025) and currently on job market. I'd be happy to connect and chat!

I am a Ph.D. student in Computer Science at the National University of Singapore, advised by Jialin Li. I also closely collaborate with Irene Zhang and Dan Ports.

My research interests lie at the intersection of distributed systems, datacenter networks, dataplane operating systems, and machine learning for systems. Currently, I am focusing on designing high-performance systems that leverage programmable hardware and ML-driven optimization to address challenges in modern datacenters.

Education

National University of Singapore

Singapore

Ph.D. Student in Computer Science (Advisor: Jialin Li)

Aug 2019 — Present

Yonsei University

Seoul, South Korea

Bachelor's Degree in Computer Science

Mar 2012 — Aug 2019

Uppsala University

Uppsala, Sweden

Exchange Student in Information Technology

Aug 2017 - Jan 2018

Work in Progress (projects that I’m currently leading)

- ML-native Dataplane Operating Systems [Current Main Project]

We design an ML-native dataplane OS architecture for automatic parameter tuning. Performance tuning remains a persistent challenge in modern datacenters, especially at microsecond scales. We are exploring how machine learning can be natively integrated into the OS dataplane, with case studies showing substantial performance gains through dynamic parameter optimization. Through this work, I actively perform comprehensive full-stack performance analysis and optimization, spanning from hardware-level signals (PCIe, memory access patterns, cache efficiency, DDIO) and inter-device data exchange to device drivers, OS components, and application layers.

- Dynamic Layer-4 Load Balancing with Microsecond-Scale TCP Migration [Full Paper Under Review]

Layer-4 load balancers are a popular solution to high tail latencies but perform poorly under unpredictable workloads and traffic bursts because they statically assign connections to servers. We propose the first dynamic L4 load balancer with μs-scale stateful connection migration. Our system leverages two trends — programmable switches and kernel-bypass — to efficiently implement TCP migration without packet loss, while maintaining transparency to clients.

Publications

[APSYS ‘25] ML-native Dataplane Operating Systems
Inho Choi, Anand Bonde, Jing Liu, Joshua Fried, Irene Zhang, Jialin Li.
16th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2025)

[ArXiv] A Primer on RecoNIC: RDMA-enabled Compute Offloading on SmartNIC
Guanwen Zhong, Aditya Kolekar, Burin Amornpaisannon, Inho Choi, Haris Javaid, Mario Baldi.
ArXiv, Dec, 2023

[APSYS ‘23] Capybara: μSecond-scale live TCP migration
Inho Choi, Nimish Wadekar, Raj Joshi, Joshua Fried, Dan R. K. Ports, Irene Zhang, Jialin Li.
14th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2023)
[ PDF ]

[SIGCOMM ‘23] Network Load Balancing with In-network Reordering Support for RDMA
Cha Hwan Song, Xin Zhe Khooi, Raj Joshi, Inho Choi, Jialin Li, and Mun Choon Chan.
Proceedings of the 2023 ACM SIGCOMM Conference
[ PDF | Talk ]

[NSDI ‘23] Hydra: Serialization-Free Network Ordering for Strongly Consistent Distributed Applications
Inho Choi, Ellis Michael, Yunfan Li, Dan Ports, and Jialin Li.
Proceedings of the 20th USENIX Conference on Network Systems Design and Implementation
[ PDF | Talk | Slide ]

[S&P ‘20] A Stealthier Partitioning Attack against Bitcoin Peer-to-Peer Network
Muoi Tran, Inho Choi, Gi Jun Moon, Viet-Anh Vu, and Min Suk Kang.
In Proceedings of IEEE Symposium on Security and Privacy, May 2020.
[ PDF | Talk | Website ]

[UbiComp Workshop ‘17] Multimodal Data Collection Framework for Mental Stress Monitoring
Saewon Kye, Junhyung Moon, Juneil Lee, Inho Choi, Dongmi Cheon, and Kyoungwoo Lee.
In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. (Workshop Paper)
[ PDF ]

Experiences

Microsoft Research - PhD Research Intern

Redmond, WA, USA

Systems Research Group (Mentor: Irene Zhang)

June 2025 — Sep 2025

Conducted research on ML-native dataplane OS architecture for automatic parameter tuning to address performance optimization challenges in modern datacenters at microsecond scales. Designed system architecture integrating machine learning natively into the OS dataplane for dynamic parameter optimization. Developed and evaluated the approach to demonstrate benefits of automated tuning across diverse workloads. Published initial findings at APSys '25, with full-system development currently in progress.

Microsoft Research - PhD Research Intern

Redmond, WA, USA

Systems Research Group (Mentor: Dan Ports)

June 2024 — Aug 2024

Conducted research on dynamic Layer-4 load balancing to address tail latency issues in unpredictable workloads and traffic bursts caused by static TCP connection assignment between client and server. Designed and implemented microsecond-scale TCP connection migration system leveraging programmable switches and kernel-bypass technologies, achieving packet-loss-free migration while maintaining client transparency. Published initial findings at APSys '23, with full paper currently under submission.

AMD - PhD Research Intern

Singapore

Xilinx - FPGA / System Design Lab (Mentor: Guanwen Zhong)

May 2023 — Aug 2023

Contributed to research on hardware accelerator architectures for datacenter networks, specializing in RDMA protocol optimization and computation offloading on FPGA-based SmartNIC platforms. Participated in developing offloading techniques to enhance network stack performance while minimizing host CPU overhead. Participated in implementing and validating solutions through FPGA prototype and performance evaluation, with findings published on ArXiv.

National University of Singapore - Research Intern

Singapore

Systems & Network Security Lab (Advisor: Min Suk Kang)

Sep 2018 — Feb 2019

Contributed to research on network-level security vulnerabilities in Bitcoin's peer-to-peer network, analyzing how adversarial autonomous systems can execute stealth attacks without routing manipulations. Participated in developing and evaluating the EREBUS attack, demonstrating partition vulnerabilities in Bitcoin nodes and defense mechanisms through protocol modifications. Co-authored paper published at IEEE S&P '20, advancing cryptocurrency network security.

Metlife - Summer Intern

Seoul, Korea

IT Planning Team

July 2018 — Aug 2018

Analyzed IT infrastructure and database server architecture at MetLife Korea, investigating OAuth 2.0 protocol adoption for security enhancement. Presented findings through internal seminar.

Yonsei University - Research Intern

Seoul, Korea

Dependable Computing Lab (Advisor: Kyoungwoo Lee)

Feb 2017 — May 2018

Contributed to development of a multimodal stress monitoring framework for analyzing people's physiological and behavioral reactions to stressors. Participated in designing and conducting experiments and implementing real-time signal processing framework. Co-authored paper published at UbiComp Workshop '17, presenting the framework and experimental findings.