Andrew Wang

Spearheaded the development of a modular Web3-native AI agent architecture, boosting decentralized agent collaboration by 40%. Integrated Google ADK into decentralized agent training workflows, reducing cloud inference costs by 30% and accelerating on-device model execution by 45%.

Led the development of scalable AI agent frameworks for high-performance computing environments, enhancing model deployment efficiency by 35%. Developed high-performance data workflows on AWS and Cloudera-compatible environments for model training, orchestration, and inference at scale.

Developed AI models for blockchain transaction analysis, increasing detection accuracy of fraudulent activities by 35%. Implemented machine learning pipelines to process and analyze large-scale blockchain data, reducing processing time by 50%.

Engineered scalable machine learning models for natural language processing tasks, achieving a 30% increase in model performance. Optimized training processes using distributed computing techniques, reducing training time by 5%.

Assisted in the development of transformer-based models for NLP tasks, contributing to early advancements in the field. Conducted experiments to evaluate model architectures, providing insights that guided future research directions.

Studied foundational concepts in mathematics, including algebra, calculus, and statistics. Developed strong analytical and problem-solving skills applicable to various quantitative fields.

Pursued advanced studies in computer science, focusing on algorithms, data structures, and software engineering principles. Gained expertise in developing complex computational solutions and systems.