Dr. Zhang’s key contributions include:

Graph-Based Metadata Management: His work on GraphMeta, IOGP, and AKIN has laid the foundation for efficient metadata organization and retrieval in complex scientific computing environments. Scientific Data Discovery: He has made substantial advancements through projects like DART, MIQS, and IDIOMS, significantly improving metadata indexing and querying in parallel object-centric storage environments. Activeness-Based Data Retention: His novel approach, ActiveDR, optimizes storage based on user activity and access patterns, addressing long-term storage challenges in data-intensive, heterogeneous environments. Currently, Dr. Zhang is leading research initiatives at LBNL on I/O optimization for GNN training, accelerating AI-powered data search, and LLM/RAG-powered scientific data discovery.

Prior to joining LBNL, Dr. Zhang held positions as a Senior Member of Technical Staff at Oracle Corporation and a Research Assistant at Texas Tech University. He has authored numerous publications in top-tier conferences and journals, including SC, PACT, CCGRID, and IEEE TPDS. He actively serves as invited paper reviewer or program committee members in prestigious journals/conferences like TPDS, SC, IPDPS, CCGrid, and HiPC.

Dr. Zhang obtained his Ph.D. in Computer Science from Texas Tech University and his BSc in Computer Science from Hebei University of Science and Technology. With his strong expertise in data management, HPC, and AI, he is committed to advancing scientific computing infrastructure to support groundbreaking research and discovery.