Our research interests
- Automatically detecting, diagnosing and fixing bugs/vulnerabilities in software
- Machine learning for code
- Machine learning for medicine
- Analyzing machine learning systems for intepretability and debugging
Our research interests
Code for "An Empirical Study of Deep Learning Models for Vulnerability Detection", published in ICSE 2023.
Detect numerical instability in ML applications using learned invariants (Soft Assertions) without modifying model logic. ACM FSE 2025.
Python 1
Detect numerical instability in ML applications using learned invariants (Soft Assertions) without modifying model logic. ACM FSE 2025.
Replication package for "Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection", ICSE 2024.
Code for "An Empirical Study of Deep Learning Models for Vulnerability Detection", published in ICSE 2023.
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