Sarah Nadi

Sarah Nadi is an Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), where she also serves as the Associate Program Head for Undergraduate Studies in Computer Science. She holds an adjunct position at the University of Alberta and co-directs the SANAD lab, which focuses on building tools to enhance software developer productivity through empirical and data-driven methods.

• Education: BSc, The American University in Cairo
• MMath, University of Waterloo
• PhD, University of Waterloo

Her research lies at the intersection of software engineering and empirical methods, with a strong emphasis on mining software repositories (MSR) to understand and improve software development practices. Key research themes include API misuse detection, library selection and migration, code recommender systems, and the role of large language models in software engineering. She investigates how developers use APIs, especially in data-centric Python libraries and cryptography, and builds tools like MuDetect and CogniCrypt to prevent misuse. Her work on library migration includes creating benchmarks such as PyMigBench and analyzing real-world migration patterns in Python.

Her recent publications show a growing focus on AI in software engineering, with empirical evaluations of tools like GitHub Copilot and LLMs for test generation. She has also contributed significantly to understanding software variability in systems like Linux and Android, and to software integration challenges such as merge conflicts and refactoring-aware merging. Her work often combines static analysis, data mining, and user studies to build practical, evidence-based tools.

Notable recognitions include a Best Paper Award at CASCON 2009 for her work on CMDB-based root cause analysis. She has published extensively in top-tier venues including FSE, ICSE, TSE, ASE, and MSR. Her research is supported by active grants, and she is currently recruiting fully funded PhD students for Fall 2025.

• Best Paper Award, CASCON 2009

Sarah Nadi mentors students through capstone projects and research supervision. She teaches core courses such as Software Engineering (CS-UH 2012), Special Topics in Computer Science (CS-UH 3260), and the Capstone Project in Computer Science (CS-UH 4001). She leads the SANAD lab, which develops tools for API analysis, library comparison, and developer support using empirical and machine learning techniques. Her lab’s projects include LibComp, MUBench, MuDetect, and CogniCrypt, and she emphasizes open science through publicly available datasets and tools.