Maohao Ran

As a machine learning engineer, I leveraged large language models (LLMs), retrieval-augmented generation (RAG), and fine-tuning techniques to develop SmartQuery. By integrating LangChain and Llama_Index, SmartQuery converts natural language queries into SQL statements, empowering users to explore databases and generate visualizations without deep SQL knowledge. My expertise in LLMs and advanced NLP frameworks has been instrumental in creating a user-friendly solution that revolutionizes data analysis.

This platform leveraged IoT devices for data acquisition and device management and incorporated machine learning models—specifically RNN and SVM—to detect and classify pest infestations accurately. The system's streamlined database schema and intuitive interface significantly improved our agricultural clients' operational efficiency and pest control.

My role involved the maintenance and enhancement of BMW's WebAssembly back-end compiler. I contributed significantly to the compiler's optimization by pinpointing and addressing performance bottlenecks, showcasing my deep understanding of compiler techniques and commitment to high-performance computing.

As the tech leader, I guided the development of an AI-based system designed to monitor and predict air quality. The system achieved an 80% accuracy rate in air quality forecasting, a testament to the machine learning algorithms implemented and the team's collaborative effort.

I led the development of a comprehensive hospital medical system designed to optimize patient services and streamline operations. The project significantly improved the efficiency and quality of patient care by applying
domain-driven design principles and developing a robust software architecture.