# JINCHENG RAO > Former Machine Learning Intern@Forest Vision | Duke University Risk engineering / Computer Science | University of Liverpool Mathematics Profile: https://flows.cv/jinchengraocom I am currently a master’s student at Duke University, majoring in Risk Engineering with a minor in Computer Science. With a strong background in mathematics and software development, I am passionate about building intelligent, scalable systems that combine machine learning algorithms with robust engineering design. My experience spans the full spectrum of the data and development pipeline—from designing GPU-accelerated 3D clustering algorithms at Forest Vision, to optimizing Transformer and CNN models for classification and anomaly detection at Duke. I have also developed full-stack web applications using Node.js, Express, and MongoDB, and implemented quantitative trading strategies at Huatai International that achieved consistent back-tested returns. Fluent in Python, JavaScript, PyTorch, TensorFlow, SQL, and TypeScript, I enjoy transforming ideas into efficient, interpretable, and production-ready solutions. My academic foundation in stochastic modeling, optimization, and machine learning complements my hands-on experience with data processing, API integration, and system design. Curious by nature and driven by impact, I’m particularly interested in roles that bridge machine learning engineering, data-driven software development, and algorithmic decision systems—where thoughtful engineering meets applied intelligence. ## Work Experience ### Machine Learning Intern @ Forest Vision Jan 2025 – Jan 2025 | Seattle, WA • Achieved precise segmentation of log bark regions by leveraging SAM for semantic segmentation, using automatically labeled positive (wood core) and negative (bark) sample points • Proposed and implemented a log explosion diagram based on force-repulsion iteration to achieve 2D diffusion projection of 3D log point clouds, effectively preventing overlap between logs • Improved cluster refinement performance by replacing CPU-based KD-Tree with FAISS GPU-accelerated nearest neighbor search, resulting in significantly faster processing ### Research Assistant @ Duke University Jan 2025 – Jan 2025 | Durham, North Carolina, United States • Built end-to-end intrusion detection models on NSL-KDD dataset using PyTorch and Scikit-learn • Designed Bi-LSTM and Transformer Encoder architectures for sequential anomaly detection • Implemented label encoding, normalization, and sliding-window sequence generation for temporal modeling • Achieved 73.9% test accuracy, outperforming Transformer baseline by 24% in detection precision • Visualized attention weights and confusion matrices to interpret model behavior and failure patterns • Proposed SMOTE and focal-loss techniques to handle class imbalance across 22 attack categories ### Research Assistant @ Duke University Jan 2024 – Jan 2024 | Durham, North Carolina, United States • Processed U.S. Census datasets (NC & FL) to clean and standardize millions of name-ethnicity records • Optimized ML models (XGBoost, LightGBM, CNN, AdaBoost), achieving 75.67% accuracy after tuning • Improved CNN architecture (dropout, filter sizes, optimizers), boosting accuracy by ~10% • Visualized classification performance using confusion matrices, loss curves, and feature importance plots ### Research Assistant @ University of Liverpool Jan 2023 – Jan 2023 | Liverpool, England, United Kingdom • Established a Convolution Neural Network based on 30,000 tumor pictures using PyTorch, achieved accurate identification of tumor edges and determined malignancy based on their contours • Optimized the Deep Q Network (DQN) to identify the optimal coefficient for the binary polynomial ### Full Stack Engineer intern @ Xi'an Jiaotong-Liverpool University Jan 2021 – Jan 2021 | Suzhou, Jiangsu, China • Developed a full-stack school database system using JavaScript for both front-end and back-end • Built the front-end with HTML, CSS, and vanilla JS, providing role-based portals for teachers and students • Created the back-end with Node.js and Express.js, allowing secure grade upload and retrieval • Integrated a MongoDB database to store user credentials, course records, and grades with proper access control • Implemented role-based authentication via session management for secure data access • Applied best practices in modular design, documentation, and testing to improve maintainability ## Education ### Master of Engineering - MEng Duke University ### Bachelor of Science - BS University of Liverpool ### Bachelor of Science - BS University of Liverpool ### Bachelor of Science - BS Xi'an Jiaotong-Liverpool University ### High School Diploma No.1 Middle School of Lu'an ## Contact & Social - LinkedIn: https://linkedin.com/in/jincheng-rao-5a613b25b - GitHub: https://github.com/jefferyraooji - Portfolio: https://jinchengrao.com - Email: mailto:jinchengrao76@gmail.com --- Source: https://flows.cv/jinchengraocom JSON Resume: https://flows.cv/jinchengraocom/resume.json Last updated: 2026-04-17