Hi! I'm Chang,

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CST 3-118, 111 College Pl, Syracuse, NY 13244

cliu57@syr.edu

a PhD student in the Department of Electrical Engineering and Computer Science at Syracuse University. It’s my fortune to be advised by Kristopher Micinski.

My research explores the core principles of secure and verifiable programming, focusing on compilers and reverse engineering. I am working on a common assumption in the field: that compilation is an inherently information-lossy and irreversible process, and my research develops methods to recover high-level abstractions directly from binary code.

I have developed scalable systems for curating large datasets of machine executables and benchmarked machine learning algorithms on a range of binary analysis tasks. These work have been published in NeurIPS DB Track and are being actively used by the research community.

Publications

  1. NeurIPS
    Assemblage: Automatic Binary Dataset Construction for Machine Learning
    Chang Liu*, Rebecca Saul*, Yihao Sun, Edward Raff, Maya Fuchs, Townsend Southard Pantano, James Holt, and Kristopher Micinski
    In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2024
    Code PDF
  2. NeurIPS
    Is Function Similarity Over-Engineered? Building a Benchmark
    Rebecca Saul, Chang Liu, Noah Fleischmann, Richard J Zak, Kristopher Micinski, Edward Raff, and James Holt
    In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2024
    Code PDF

Services

Reviewer

2025: AAAI AICS Workshop
2026: NeurIPS Datasets & Benchmarks Track, AAAI AICS Workshop