Ph.D Student, Cornell UniversityHey, there! My name is Shinhae Kim. I'm a second-year PhD student at Cornell University.
My research area includes the intersection of Software Engineering and Machine Learning. I'm very fortunate to be co-advised by Prof. Saikat Dutta and Prof. Owolabi Legunsen. I was also honored to be awarded the Veena & Induprakas Keri PhD Fellowship from Cornell Graduate School.
Before joining Cornell, I spent two wonderful years at KAIST for my master's degree, advised by Prof. Sukyoung Ryu. Then, I worked as a full-time researcher for four and a half years at National Security Research Institute in South Korea.
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Cornell UniversityPh.D. in Computer ScienceAug. 2024 - Current
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KAISTM.S. in Computer ScienceFeb. 2018 - Feb. 2020"A Survey on Security of Blockchain Smart Contracts: Techniques and Insights" -
Handong Global UniversityB.S. in Computer ScienceMar. 2014 - Feb. 2018Summa Cum Laude (Major GPA: 4.4/4.5)
Work Experience
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National Security Research InstituteResearcher, Software Security Lab.Jan. 2021 - Apr. 2024(1) Fuzzing for JavaScript Just-in-Time Compiler(2) Static Variant Analysis for Chrome Vulnerabilities(3) Vulnerability Analysis of National Java Web Framework
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National Security Research InstituteResearcher, Quality Assurance Lab.Dec. 2019 - Dec. 2020Unit Testing of Safety-Critical Embedded Software
News
Publications
[1]Valg: A Fast Reinforcement Learning-Based Runtime Verification Tool for Java
Shinhae Kim, Saikat Dutta, and Owolabi Legunsen
IEEE/ACM International Conference on Software Engineering, Demonstrations Track (ICSE Demo) 2026
This paper introduces Valg, the first Reinforcement Learning-based Runtime Verification tool for Java. Valg extends the prior work of selective monitoring with several new features such as more efficient and finer-grained hyperparameter tuning.
[2]Faster Runtime Verification during Testing via Feedback-Guided Selective Monitoring
[slides]
[poster]
Shinhae Kim, Saikat Dutta, and Owolabi Legunsen
IEEE/ACM International Conference on Automated Software Engineering (ASE) 2025
ACM SIGSOFT Distinguished Paper Award
This paper presents a novel approach to speed up Runtime Verification by selective monitoring using feedback from prior monitoring. The evaluation shows that our technique is up to 30x and 555x faster than the state-of-the-art techniques while preserving violations.
[3]EtherDiffer: Differential Testing on RPC Services of Ethereum Nodes
Shinhae Kim and Sungjae Hwang
ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2023
Ethereum nodes implement their RPC services in different programming languages based on a common specification. This paper presents novel test case generation techniques based on the specification and detects 48 kinds of deviations among the implementations.
[4]Analysis of Blockchain Smart Contracts: Techniques and Insights
Shinhae Kim and Sukyoung Ryu
IEEE Secure Developement Conference (SecDev) 2020
This paper conducts the first comprehensive survey of static analysis and dynamic testing approaches for smart contracts. Based on the study, we present the research trends, open challenges, and promising research directions in smart contract analysis.
[5]A Comparison of Static Analysis Tools on Accuracy of Memory Error Detection
Shinhae Kim and Minjeong Kim
Korea Computer Congress (KCC) 2020
This paper evaluates two open-source and two commercial static analyzers in terms of their detection capability on three major types of memory errors.
[6]Parametric Image Generation and Enhancement in Contrast-Enhanced Ultrasonography
Shinhae Kim, Eunlim Lee, Eunbee Jo and Hojoon Kim
KIPS Transactions on Software and Data Engineering (KTSDE) Vol. 6, No. 4, 2017
This paper proposes image processing techniques that improve the usability and performance of a contrast-enhanced ultrasonography-based diagnostic system.