Hiprel Group
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Research Directions and Objectives
Our group is dedicated to advancing Neuro-Symbolic AI , bridging the strengths of learning-based and logic-based approaches. We focus on developing AI techniques that enhance the scalability and efficiency of automated reasoning systems, while also enabling AI models themselves to acquire reasoning and verification capabilities.
Beyond fundamental research, we develop specialized Neuro-Symbolic AI methods for reliable software, focusing on enhancing the reliability and robustness of systems ranging from large-scale cloud infrastructures to modern AI applications (which themselves can be viewed as complex software systems). We also explore verifiable code generation to make software development more trustworthy, transparent, and error-resistant.
Beyond fundamental research, we develop specialized Neuro-Symbolic AI methods for reliable software, focusing on enhancing the reliability and robustness of systems ranging from large-scale cloud infrastructures to modern AI applications (which themselves can be viewed as complex software systems). We also explore verifiable code generation to make software development more trustworthy, transparent, and error-resistant.
Current Group Members
Faculty
PhD Students
Zichen Xie (Summer 2025 - )
Research: Neuro-Symbolic AI for Verifiable Code Generation;
Improving reasoning and verification capability of LLMs
Master Students
Chaitanya Rajendra Shahane (Fall 2024 - )
Research: LLMs for Software Testing, Software Testing for Deep Learning Libraries
Undergraudate Students
Internship Students
Publications from Our Group
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Mrigank Pawagi , Lize Shao , Hyeonmin Lee, Yixin Sun, Wenxi Wang
RFCScope: Detecting Logical Ambiguities in Internet Protocol Specifications
The 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025)
(To appear)