Experience

My research focuses on two key areas: AI for Software Engineering (AI4SE) and Software Engineering for AI (SE4AI). In AI4SE, I apply various machine learning techniques to enhance testing and security, including ML-guided vulnerability detection and explanation-guided symbolic execution across smart contracts and systems code. In SE4AI, I bring software engineering rigor to AI systems through model and data testing, as well as the development of explanation techniques—areas where I aim to innovate by building on traditional software engineering program analysis and testing techniques.

Developed and Enhanced Curriculum: Integrated advanced topics such as MLOps, ModelOps, and AI Ops into the SOFTWARE ENGINEERING: MANAGEMENT, MAINTENANCE, AND QUALITY ASSURANCE course, increasing curriculum relevance and boosting student engagement by 20%. Facilitated Professional Development: Conducted specialized workshops for industry professionals, enhancing their understanding and application of AI operational concepts, resulting in a 30% increase in confidence with MLOps tools.

Received mentorship from a Google Research scientist on defining research problems in Security Analysis and Mechanistic Interpretability.