Summary
Feng Mi is a PhD-trained Applied AI Research Data Scientist with nine years of experience building production-ready machine learning and deep learning systems, currently advancing event extraction and disclosure recommendation at Fidelity Investments. He blends academic rigor—publishing KDD and TKDD work on fairness-aware online learning and ICBC/DLT papers on metric-learning for smart contract vulnerability detection—with hands-on engineering using PyTorch, TensorFlow, and scalable deployment practices. Feng’s background spans end-to-end solutions from object detection and cloud-integrated services to NLP and LLM-driven pipelines, and he has a track record of turning research prototypes into operational tools. Based in Redmond, he thrives in fast-paced environments that demand rapid iteration and practical impact, and he’s especially interested in AI security, generative models, and fairness in deployed systems.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer and Information Sciences, General, Doctor of Philosophy - PhD, Computer and Information Sciences, General at The University of Texas at Dallas
Bachelor of Engineering - BE, Electrical Engineering and Automation, Bachelor of Engineering - BE, Electrical Engineering and Automation at Nanjing University of Information Science and Technology
Master of Science - MS, Computer Engineering, Master of Science - MS, Computer Engineering at University of Delaware
Chinese, English