🔬 Researcher working on LLM agents, reasoning systems, scalable agent infrastructure, and post-training.
🎓 PhD from Carnegie Mellon University
🌐 Homepage: https://yuhangyao.com 📫 Email: [email protected]
DIG — Dynamic Decision Paths for Agent Collaboration https://happyeureka.github.io/dig/
GAP — Graph-Based Agent Planning with Parallel Tool Use https://github.com/WJQ7777/Graph-Agent-Planning
FedGraph — Federated Graph Learning Framework https://github.com/FedGraph/fedgraph
FedGCN — Federated Graph Convolutional Networks https://github.com/yh-yao/FedGCN
DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
GAP: Graph-Based Agent Planning with Parallel Tool Use and Reinforcement Learning
Toward Super Agent Systems with Hybrid AI Routers
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
NeurIPS 2023
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Build AI Agents from Scratch: LLM‑Driven Agent Design and Practice




