651 66730

KHOO Siau Cheng

Associate Professor
Co-Director, NUS Business Analytics Centre (BAC)

  • Ph.D. (Computer Science, Yale University, 1992)

Siau-Cheng Khoo is an Associate Professor in Department of Computer Science and Co-director of the NUS Business Analytics Centre. His research focuses on improving productivity of software developers, and he has expertise in both the formal and practical aspects of programming languages and software engineering. He has designed and developed static program analysis and dynamic program optimization techniques that improve programmer productivity. He has abundant knowledge in inferring of semantic attributes for code and in applying sound and optimized code search techniques. He has also applied advanced programming language theories to the design and implementation of a domain-specific language that helps investors analyze financial data. He has been doing extensive research in adapting and inventing new dynamic analyses that enable discovery of dynamic program behaviours and program bugs via data-mining techniques, which has been termed “specification mining.” His research has been published in top-tier conferences, such as OOPSLA, PLDI, ICSE, ASE, KDD, and ICDE. He has much experience in supervising graduate students and leading research projects. He has successfully completed and is working on multiple research projects funded by DSTA, MOE (Tier 1 & 2), and A*Star. Together with colleagues from NUS Business School, Siau-Cheng helped to set up the NUS Business Analytics Centre to oversee the operation of Master of Science programme in Business Analytics. The Centre was established in 2013 with close collaboration with Economic Development Board of Singapore and IBM. Its primary goal is to train Business Analysts to meet the industry need for professionals who can apply analytics technology to business problems. Siau-Cheng is currently the Co-director of the Centre.


  • Programming Languages

  • Code analytics

  • Software Engineering

  • Specification Mining

  • Programming Analysis

  • Transformation and Optimisation


Scalable, Precise and Configurable Neural Network Verification

This project proposes a novel platform for verifying neural networks (NNs), prioritizing scalability and precision. Unlike existing methods, it enables segmentation of NNs for applying varied verification techniques, enhancing configurability. Additionally, it investigates innovative approaches to boost verification accuracy while maintaining efficiency.




  • Sihan Xu, Aishwarya Sivaraman, Siau-Cheng Khoo, Jing Xu: GEMS: An Extract method Refactoring Recommender. ISSRE 2017: 24-34
  • Quang-Trung Ta, Ton Chanh Le, Siau-Cheng Khoo, Wei-Ngan Chin: Automated Mutual Explicit Induction Proof in Separation Logic. FM 2016: 659-676
  • Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo: Vector abstraction and concretization for scalable detection of refactorings. FSE 2014: 86-97
  • Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo: Scalable detection of missed cross-function refactorings. ISSTA 2014: 138-148
  • Zhiqiang Zuo, Siau-Cheng Khoo, Chengnian Sun: Efficient predicated bug signature mining via hierarchical instrumentation. ISSTA 2014: 215-224
  • Chengnian Sun, Siau-Cheng Khoo: Mining succinct predicated bug signatures. ESEC/SIGSOFT FSE 2013: 576-586
  • Sandeep Kumar, Siau-Cheng Khoo, Abhik Roychoudhury, David Lo: Inferring class level specifications for distributed systems. ICSE 2012: 914-924
  • Wei-Ngan Chin and Siau-Cheng Khoo. Calculating sized types. Higher-Order and Symbolic Computation 14 2-3, pp 261-300.



Programming Methodology


In the News

6 February 2023
6 February 2023 ­­– NUS Computing Ph.D. student Zhong Yuyi won second place in the Student Research Competition at the ...