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Draft:Mohamed Afane
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Comment: In accordance with Wikipedia's Conflict of interest guideline, I disclose that I have a conflict of interest regarding the subject of this article. Moafane (talk) 08:03, 28 January 2026 (UTC)
Mohamed Afane is a machine learning researcher and data scientist specializing in artificial intelligence applications in legal systems and public policy. He is a Research Fellow at Stanford Law School's Regulation, Evaluation, and Governance Lab (RegLab), where his work focuses on benchmarking large language models and AI systems to assess their reliability and reasoning performance in legal contexts, with particular attention to unemployment insurance adjudication, benefits determination, and statutory analysis.
Education
[edit]Afane holds a Master of Science in Data Science from Fordham University (2025) and a Master of Science in Energy Engineering from the University of Illinois Chicago (2023).
Research
[edit]Afane's research addresses AI robustness and reasoning capabilities in high-stakes legal applications. His work includes developing evaluation frameworks for legal retrieval-augmented generation (RAG) systems and creating methodologies to identify overconfidence in AI-powered legal decision-making tools. At Stanford RegLab, he developed a framework for conducting statutory surveys across all 50 U.S. states that identified gaps in Department of Labor unemployment insurance reports.
Selected Publications
[edit]"Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys" (ACM CS&Law, 2026)
"ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks" (CVPR, 2025)
"SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models" (IEEE Big Data, 2025)
External links
[edit]References
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