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Ning Xuan has held various roles throughout their work experience. They began as an Undergraduate Student Research Assistant at Stony Brook University in 2011. In 2012, they worked as a Research Assistant at the National Institutes of Health, where they focused on using optical coherence tomography to detect physiological changes in the brain vascular vessel of mice. They also developed algorithms for image processing using MATLAB.

In 2013, Ning Xuan held two research assistant positions. They worked at the Heffner Biomedical Imaging Laboratory, where they conducted research on an unspecified topic from March to May. They also worked at the G.H. Sergievsky Center and Taub Institute from June to August.

From 2014 to the present, Ning Xuan has been employed at Radlink, Inc. as a Senior Software Engineer. In this role, they implemented machine learning techniques for medical image analysis and developed products for segmentation, landmark detection, and transformation on various modalities of medical images. They also collaborated with the CTO and surgeons' advisory board to create a computer vision API from scratch and worked closely with surgeons to develop automation applications using deep learning.

In 2023, Ning Xuan will start a new position as a Senior Data Scientist at PictorLabs Inc.

Ning Xuan obtained a Bachelor's degree in Biomedical Engineering from Stony Brook University (SUNY) between 2008 and 2012. Following this, Ning Xuan pursued a Master of Science (M.S.) degree in Bioengineering and Biomedical Engineering at Columbia University from 2012 to 2013.

In terms of additional certifications, Ning Xuan completed various AI-related courses on Coursera. These certifications include "AI For Medical Treatment," "AI for Medicine Specialization," "AI for Medical Diagnosis," "AI for Medical Prognosis," "Convolutional Neural Networks," "Sequence Models," "Structuring Machine Learning Projects," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," and "Neural Networks and Deep Learning". The completion dates range from May 2018 to March 2021.