Mohammad Safarzadeh
Welcome to my page.
I work on the evaluation of code-generation LLMs, reasoning models, and retrieval-augmented generation systems, with a focus on making benchmarks more reliable, detecting data leakage, and improving NL2SQL evaluation. At Oracle, I also work on conflict resolution in RAG pipelines for financial-domain applications, alongside large language models, generative AI systems, and domain adaptation for high-impact use cases including healthcare.
Before Oracle, I worked at Perceive, where I focused on quantization-aware training, efficient neural network inference, and lightweight models for edge deployment.
Before that, I was at FICO, building machine learning models for credit card fraud detection and other high-stakes decision systems.
My academic background is in astrophysics. I earned my PhD from Johns Hopkins University and held postdoctoral research positions at ASU, UCSC, Harvard, and NASA before moving into applied machine learning. That research training still shapes how I approach modeling, experimentation, and scientific rigor in modern AI systems.
