Wei Yang

Gain hands-on experience on software development and data pipeline implementation

• Integrated Weaviate Vector Database with LLMs to empower company-specific content-driven recommendations and elevate chatbot conversations
• Orchestrated 7 MuleSoft APIs, streamlining delta updates ingestion into Lucidworks Fusion for the Partner Locator, ensuring a seamless user experience.
• Engineered data pipelines infused with Machine Learning algorithms, driving personalized user experiences and optimizing marketing conversion rates
• Tailored AEM components to deliver customized questionnaires, complete with category support and robust reporting functionalities

Language features pattern detection with Deep Learning models, data parsing and statistics modeling

• Cleaned and structured 94,581 earnings call transcripts to explore 96 language factors influencing stock return
• Achieved 72% accuracy on text classification with domain-adapted BERT language model using Pytorch
• Conducted text analysis, sentiment analysis, and text mining utilizing NLP techniques with SpaCy and NLTK
• Set up and maintained remote GPU on Ubuntu for Machine Learning and Deep Learning tasks
• Developed tutorials on implementing deep learning models using related Python packages

Gain hands-on experience on necessary tools and understanding of develop environment

• Built a cost analysis module for the application with Excel advanced functionalities
• Extracted structured data with MySQL, cleaned the unstructured textual data with Python
• Manipulated, visualized and presented the statistical results with Tableau Dashboard
• Explored regression models to provide a baseline of elevators’ failure rate for the Operations Department

Develop a web application for furniture and rental information exchange

• Led team of four students to design and develop a web application with Node.js and Express
• Designed document schema on MongoDB and wrapped CRUD operations as RESTful APIs
• Implemented Login and user-specific functions such as account signup and authentication system
• Developed features for rental and furniture information exchange such as comments, search, dashboard

Develop a web application for furniture and rental information exchange

• Created and implemented recommender engine with users, items, and interaction records from JD.com
• Integrated multiple memory-based and model-based collaborative filtering algorithms to make recommendations
• Simulated on 7,000 pre-defined user-item interaction samples and attained 77% Top-10 Accuracy

Develop a mathematical model based on public data and metrics to measure and predict the vibrancy of the city in the U.S.

• Constructed a vibrancy index to interpret and predict the prosperity trend of cities in the U.S.
• Explored, collected, and blended data from Google POIs, Instagram, Zillow, Bureau of Labor Statistics with Pandas
• Performed feature engineering on panel data, and fine-tuned models for prediction with XGBoost and Keras

Data scraping, Parsing and information extraction, topic modeling with Clustering algorithms and Neural Networks

• Scraped, cleaned and structured Job Descriptions textual data from INDEED.com
• Filtered noisy data by selecting clusters from K-means algorithm and visualized skillsets distribution
• Categorized, analyzed skillsets trend after Topic Modeling and Aspect Extraction Deep Neural Network

Feature engineering, Data analysis and modeling

• Performed data cleaning and sentiment analysis for over 20 million related Tweets in second granularity
• Delivered feature engineering for financial indicators like MACD and RSI etc
• Analyzed, finetuned, and back-tested for over five types of deep learning model to increase the portfolio return