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Yongchao Liu — Scaling Data Intelligence

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Dr. Yongchao Liu is a Staff Engineer at Ant Group (since 07/2017), leading research and development of foundational AI technologies for large-scale data intelligence, with extensive expertise in large-scale graph learning, Graph RAG, foundation models, and AI systems engineering. He authored the seminal, globally recognized survey on Graph Retrieval-Augmented Generation (ACM TOIS, ESI Highly Cited Paper), positioning him at the forefront of RAG & Agents and foundation models. Across changing data, his work has pursued one goal: making large-scale data searchable, matchable, and understandable — first for biological sequences, then for the user and transaction graphs of a hundred-million-customer fintech platform, and now for knowledge and language in the era of large models. The method that carries the goal across eras is Compact Computing: center on the data, keep representations compressed and sparse, and co-design algorithms with systems for full-stack performance — set out in full in a technical report.

Prior to joining Ant Group, he was a Research Scientist II (research faculty) in the School of Computational Science & Engineering at the Georgia Institute of Technology (01/2015–07/2017), and a Postdoctoral Researcher at the Institute of Computer Science, University of Mainz (11/2011–01/2015). He earned his Ph.D. in computer engineering from Nanyang Technological University in 2012 (supervised by Dr. Bertil Schmidt and Dr. Douglas Maskell), and his Master and Bachelor degrees in computer science from Nankai University in 2008 and 2005, respectively.

At Ant Group, he led the development of a novel distributed graph intelligence computing system — GeaLearning, the first distributed and scalable graph learning system built upon the vertex-centric graph processing paradigm — and broke the world record on the Stanford Open Graph Benchmark proteins leaderboard in 2021. He has published widely in top journals and conferences, including Bioinformatics, ACM TOIS/TACO/TKDD, IEEE TPDS/TCBB, NeurIPS, ICLR, AAAI, ACL, SIGMOD, ICDE, KDD, WWW, EuroSys, PACT and IPDPS. His recognitions include Best Paper Awards at IEEE ASAP (2009 and 2015), a Best Student Paper Award at KSEM 2024, the Program to Empower Partnerships with Industry Award from the U.S. South Big Data Hub (2016), and a Best Paper Award recommendation at IEEE Cluster 2014. He was awarded Innovative Talent (in artificial intelligence) of Hangzhou 521 Program for Global Talents Introduction in 2019, recognized as Hangzhou High-Level Talents of Category C (Provincial-level Leading Talent) in 2020, and appointed as an Industry Mentor by the Institute of Software, Chinese Academy of Sciences in 2024. His current interests focus on graph intelligence, Graph RAG & agents, foundation models, and the algorithm–system co-design that makes them efficient at industrial scale.

Research threads: T1 Sequence Intelligence T2 Graph Intelligence T3 Foundation Models T4 Users & Trust T5 RAG & Agents

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