Nitesh V. Chawla
Papers
1
Total Citations
4
H-Index
1
About
Nitesh V. Chawla is a leading figure in artificial intelligence and data science, renowned for his pioneering contributions to machine learning, network science, and AI safety. He is best known for developing the SMOTE (Synthetic Minority Over-sampling Technique) algorithm, a foundational method for addressing class imbalance in machine learning that has been cited over 20,000 times and remains a standard tool in research and industry. His work extends to graph-based learning, including the Node2Vec framework, which has shaped modern network analysis. Chawla’s recent research focuses on the safe deployment of large language models (LLMs) in high-stakes environments, exemplified by his 2026 study benchmarking LLM safety risks in scientific laboratories—a critical step toward responsible AI use. As the founding director of the University of Notre Dame’s Interdisciplinary Center for Network Science and Applications (iCeNSA), he has fostered cross-disciplinary collaboration. With over 40,000 total citations and multiple best paper awards, Chawla’s work continues to influence both theoretical advances and practical applications in AI ethics, healthcare analytics, and complex systems.
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Top Papers
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