Hello! I am Bonan Yang, a Computer Science Ph.D. candidate at Southern Illinois University Carbondale, advised by Dr. Gunes Ercal. I specialize in complex network analysis, knowledge graph systems, and graph machine learning. My work spans representation learning on large-scale graphs, semantic reasoning over knowledge bases, and building graph infrastructure for real-world applications.
Graph representation learning, knowledge graph systems, scalable graph infrastructure.
Specializations: Graph Neural Networks (GCN, GraphSAGE, GAT, Graph Autoencoders, Spatio-temporal GNNs), Knowledge Graphs, Complex Network Analysis, NLP & LLM, GraphRAG, Vector Databases, End-to-End ML Deployment
Languages & Databases: Python, C/C++, R, SQL, Cypher, Neo4j, Milvus, Pinecone, HDFS
Platforms & Tools: Linux, Docker, AWS, GCP, Apache Spark, PyTorch, PyG, Transformers, vLLM/SGLang, CUDA, Git, NetworkX, Gephi
Jan 2026: Released HPO Clinical Knowledge Graph — HPO-KG built from Human Phenotype Ontology for rare disease diagnosis. 19934 phenotype nodes, 13012 disease nodes, 200000+ edges.
Large-scale Wikipedia Knowledge Graph (2025 – Present)
Processed 18.8M page nodes and 747M link edges with HDFS and Spark. Generated entity embeddings with FastText, indexed in Milvus for semantic search. Supporting downstream GraphRAG and QA systems.
Course Graph Visualization Platform (2023 – Present)
Full-stack web app (Flask + Neo4j + Cytoscape.js) for visualizing course prerequisite networks. Deployed with Docker, Nginx, and SSL. Live at coursegraphs.com
GNN-Based Molecular Toxicity Prediction (2025)
Implemented GCN and GAT in PyTorch/PyG for multi-label toxicity prediction on Tox21 dataset (7.8K compounds). Achieved 0.81 ROC-AUC under severe class imbalance (1:22).
3D Object Quality Assessment via Graph Learning (2024 – Present)
Transformed 3D printing point clouds into graphs. Designed Graph Autoencoder with contrastive learning for similarity retrieval and deviation localization in personalized manufacturing.
Curriculum-Industry Skill Gap Analysis (2025 – Present)
Built knowledge graph integrating O*NET skill ontology with STEM course syllabi (462 courses, 3556 skills, 770 jobs). Applied semantic encoding and multi-hop reasoning for course-skill-job alignment. Providing data-driven insights for Illinois State Board of Education.
