Yunge Li
• https://www.linkedin.com/in/yunge-stella-li/ I'm Yunge, a current data scientist at Meta. I'm writing to apply for contributor / write for DDI Medium and DDI Main Site! Below is my key professional portfolios - 1. Selected projects 1) [Medium Blogs] Research papers on audience-targeting experiment methodology (clustering, etc) to overcome heterogeneous treatment effects (HTE) and network effects. 2) Development of automated RCA chatbot engined by causal network and LangChain for anomalies in package size in Amazon NA sort centers. 3) [Under Review for IEEE-FMLDS 2025] Developed and deployed a package mix optimization algorithm via gradient-based forecast alignment for large-scale sortation centers 2. Industry Experience (4.5 years) My career focus has been on data mining, causal inference, and machine learning to solve complex problems in user growth and safety for social media platforms and supply chain operation. 1) Meta – Data Scientist, Facebook Creation: data mining, causal inference, and ML modeling to drive user growth and engagement. 2) TikTok – Data Scientist, Trust and Safety: applied large-scale data analysis and modeling to enhance user safety and policy enforcement. 3) Twitch (Subsidiary of Amazon) – Data Scientist, Community Discovery and Growth: designed recommendation and discovery models for creator–viewer engagement. 4) Amazon – Data Scientist, Supply Chain Forecasting: built statistical and ML models to optimize forecasting for global logistics. 3. Academic Research Healthcare Informatics Lab, Carnegie Mellon University (Jun 2020 – Apr 2021) under Prof. Rema Padman, in collaboration with Prof. Ofir Ben-Assuli and Prof. Tsipi Heart (Ono Academic College, Israel). Project: Readmission prediction on historical diagnosis data of congestive heart failure patients. Contributions: data processing, ML model development, hyperparameter tuning, and drafting of the initial manuscript. 4. Prior Reviewing Experience 1) Amazon Robotics Conference 2025 – Reviewed a paper on a framework for automated root cause analysis in sortation centers, focusing on causal discovery algorithms. 2) Amazon Machine Learning Conference 2025 – Reviewed two papers on operational research, optimization models, and deep learning. I hope to share my views and experiences from my data science practices, and also discuss with the excellent fellows in DDI community!