Andrew Wang

Spearheaded the development of a modular Web3-native AI agent architecture, boosting decentralized agent collaboration by 40% and integrating Google ADK to reduce cloud inference costs by 30%. Engineered high-throughput big data pipelines using Apache Spark and built retrieval-augmented generation (RAG) pipelines, reducing hallucination rates by 35%. Optimized GPU-backed infrastructure and deploye

Led the development of scalable AI agent frameworks, increasing deployment efficiency by 35% and creating high-performance data workflows on AWS for model training and inference. Developed RESTful APIs using Django, Flask, and FastAPI to serve machine learning models, reducing integration time by 40%. Deployed full MLOps pipelines using Kubernetes and MLflow, cutting model deployment time by 50%.

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%. Integrated GCP services, including BigQuery, into blockchain analytics pipelines, accelerating fraud detection model throughput by 40%.

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%. Developed APIs for seamless integration of AI models into client applications, improving adoption rates by 25%.

Assisted in the development of transformer-based models for NLP tasks and conducted experiments to evaluate model architectures, guiding future research directions. Collaborated with senior researchers to publish findings in academic conferences and developed scripts for data preprocessing and model evaluation, streamlining the research workflow.

Pursued advanced studies in computer science, delving into complex algorithms, data structures, and software engineering principles. Developed expertise in theoretical and applied aspects of computing.

Completed a comprehensive curriculum in mathematics, focusing on foundational theories and problem-solving techniques. Gained strong analytical skills applicable to various quantitative fields.