I bridge the gap between Distributed Systems and Neuro-Symbolic AI. My goal is to mitigate hallucinations in LLMs by integrating Knowledge Graphs with distributed vector storage, moving from heuristic RAG to verifiable, structure-aware retrieval.

• 🔭 Currently: Architecting AI-native data structures at TiDB.
• 🏛️ Community: Serving as Chair for the International Conference on Intelligence (ICI 2026).
• 🧠 Research Interest: Graph-Augmented RAG, Vector Search Kernels (HNSW/IVF), Distributed Consensus (Raft/Paxos).

Focus: GraphRAG, Distributed Consensus, and Database Internals.

• SPRIG: Democratizing GraphRAG - Linear, CPU-Only Graph Retrieval (Submitted to KAIS, 2025).
• BALLAST: Bandit-Assisted Learning - Latency-Aware Stable Timeouts in Raft (Submitted to IEEE TPDS, 2025).
• TiCard: Deployable EXPLAIN-only Residual Learning - Cardinality Estimation (Submitted to JDSA, 2025).
• RIFT: Replicating Ideological Fractures via Transformers (Submitted to JASSS, 2025).

• Hybrid Query Optimization: Formulated cost models for Hybrid Search (Vector + SQL) within the TiDB kernel.
• Ecosystem: Ported Hibernate ORM to TiDB and aligned client SDKs with the Percolator transaction model.

• High Concurrency: Designed the TME Live platform handling 10M peak QPS using Bloom Filters/HyperLogLog for massive dataset distinct counting.

Domain	Technologies
Languages
Systems
AI & Data