# Yueqiu Sun > Software Engineer at Google Location: San Francisco Bay Area, United States Profile: https://flows.cv/yueqiu ## Work Experience ### Software Engineer - Machine Learning @ Google Jan 2021 – Present | Mountain View, California, United States ### Machine Learning/Computer Vision Engineer @ Osmo Jan 2019 – Jan 2021 | Palo Alto, CA Delivered Machine Learning driven solutions for vision problems arising with new AR products. ### Graduate Student Researcher @ NYU Langone Health Jan 2018 – Jan 2019 | New York Built a supervised learning framework that reliably identifies clinical conditions for epilepsy patients based on background EEG patterns. Extract features from EEG by computing band power with Fourier transforms. Achieved above 85% AUC in classifying clinical outcomes. Visualize the findings and presented the poster at ACNS Annual Meeting. ### Graduate Student Researcher @ Simons Foundation Jan 2018 – Jan 2019 | Greater New York City Area Designed a deep learning architecture to model the link between the galaxies distribution and its underlying dark matter distribution. Formulated the task to be a semantic segmentation problem and explored the use of convolutional neural networks to perform the mapping. Outperformed the standard benchmark method of the field while having much more scaling and generalization abilities. Submitted the paper to KDD 2019 as the first author. ### Research Assistant @ New York University School of Law Jan 2018 – Jan 2018 | New York ### Undergraduate Researcher @ Shanghai University of Finance & Economics Jan 2017 – Jan 2017 | Shanghai City, China • Studied the voting behavior in the US Presidential Election using correlation analysis, linear regression models and mixed effects models. • The paper examined the relationship between 3112 counties' voter behavior and their demographic and economic characteristics, and discovered the role of the non-measurable regional characteristics in influencing the voting behavior. ### Data Scientist @ Nielsen Jan 2016 – Jan 2016 | Shanghai • Developed several machine learning algorithms including logistic regression and random forest using Python to deliver the store profiling project. Reduced prediction error from the previous model by 4.8%. • Acted as one of the three key team members in the city sequence project, a million-dollar project that evaluated the current China retail market through machine learning. Responsible for organizing data and running and testing the mixed effects model using SQL and R. • Independently designed an information extraction algorithm with SAS using regular expression, wrote a highly effective program to obtain critical information from massive amount of E-commerce product descriptions and achieved far higher accuracy rates than the existing method, proudly presented the methodology at team meeting. ### Undergraduate Researcher @ Shanghai University of Finance & Economics Jan 2016 – Jan 2016 | Shanghai City, China Compiled a report analyzing the high-frequency trading data (massive dataset) with Python, in which I used PCA, K-means to determine that the stocks of the same sector have similar performance and selected the best and worst 50 stocks based on index tracking. ## Education ### Master of Science - MS in Data Science New York University ### Statistics Shanghai University of Finance and Economics ## Contact & Social - LinkedIn: https://linkedin.com/in/yueqiusun1324 - Portfolio: https://yueqiusun.github.io/ --- Source: https://flows.cv/yueqiu JSON Resume: https://flows.cv/yueqiu/resume.json Last updated: 2026-03-29