Xuhui (Daniel) Zhan is an Applied Scientist and Machine Learning Engineer specializing in NLP, computer vision, multimodal AI, and MLOps. He currently builds production ML systems at Treverse LLC, including a Go-based multilingual translation service on AWS, an internal AI/ML platform for standardized CI/CD and monitoring, an edge-first vision pre-sorting pipeline for high-speed barcode decoding, and recommender models that generate personalized item suggestions for hundreds of thousands of users.

Previously, at Vanderbilt University’s AI Negotiation Lab, Xuhui led AI and data science research to develop an LLM pipeline that auto-codes negotiation transcripts, cutting annotation costs by over 99% while increasing human–model agreement from 30% to 80%. His work has been presented at venues including the 2025 AI Negotiation Summit (Harvard/MIT). At Vanderbilt’s Network and Data Science Lab, he designed fusion frameworks integrating vision, graph, and language modalities, refactored LLaVA to reduce compute costs by 25% while matching state-of-the-art performance.

As a Professional Research Assistant, he improved ancient-mortar classification accuracy from 60% to 97% using Vision Transformers and created a vision-only provenance algorithm that reduced analysis time from months to minutes. Earlier, as an Algorithm Development Engineer, he deployed a computer vision storage-detection system to ~30 warehouses, increasing pick accuracy by 15% and reducing manual checks by 80%, while also optimizing ROS localization algorithms and standardizing deployment toolchains.

Xuhui holds a Master’s in Data Science from Vanderbilt University (GPA 3.98/4.0) and a Bachelor’s degree in Data Science with First Class Honours and Highest Distinction from Beijing Normal–Hong Kong Baptist University.

“Any sufficiently advanced technology is indistinguishable from magic.”

— Arthur C. Clarke, Clarke’s Third Law