Stanford UniversityAbout me
AI researcher at Stanford University focused on evaluating and improving the reliability of LLMs. I develop benchmarks for testing AI systems on complex reasoning tasks, particularly in legal domains like unemployment insurance adjudication and statutory analysis. I have extensive research experience working on model vulnerabilities including backdoor attacks and adversarial robustness in both NLP and Computer Vision, as well as experience as a data engineer building data pipelines and scaling cloud infrastructure.
Recent News
Mar. 2026: I will be presenting our paper on benchmarking legal AI systems at CS&Law 2026.
Dec. 2025: I presented our work on data poisoning in fine-tuned LLMs at IEEE Big Data 2025.
Aug. 2025: I joined Stanford University as an AI Research Fellow.
Jun. 2025: I presented our paper on efficent image encoding for QNNs at CVPR 2025.
Dec. 2024: I presented our paper on LLMs Empowering Phishing Attacks at IEEE Big Data.
Nov. 2024: Fordham News published an article covering my interview on our lead research.
Oct. 2024: Our paper on optimizing lead testing for children in NYC was published by The Journal of Urban Health.
Sep. 2024: I presented our paper on Imbalanced Datasets at the 28th KES conference.
April. 2024: I won the third place award at the Fordham Three Minute Thesis Competition for our research paper on Lead Testing for Childeren in New York City. [Competition Results]
Publications
AI Benchmarking and Evaluation
M. Afane, E. Hariri, D. Ouyang, and D. Ho, “Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys”, CS&Law, [Paper].
M. Afane, K. Laufer, W. Wei, Y. Mao, J. Farooq, Y. Wang, and J. Chen. “Quantum-Audit: Evaluating the Reasoning Limits of LLMs on Quantum Computing”, Submitted to KDD, [Paper].
M. Afane, J. Chen, et al. “SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models”. IEEE BigData, [Paper].
M. Afane, J. Chen, et al. “Next-Generation Phishing: How LLM Agents Empower Cyber Attackers”, IEEE BigData. [Paper].
Machine Learning for Public Health
M. Afane and J. Chen, “Optimizing Blood Lead Level Testing for Children in New York City”, Journal of Urban Health, [Paper].
M. Afane, Y. Wang, and J. Chen, “Can LLMs Help Allocate Public Health Resources? A Case Study on Childhood Lead Testing”, IEEE BigData
Quantum Machine Learning
M. Afane, G. Ebbrecht, Y. Wang, J. Chen, and J. Farooq. “ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networkss”, CVPR. [Paper]
M. Afane, Q. Long, H. Shen, Y. Mao, and J. Chen. “Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision”, Quantum Machine Intelligence, [Paper].