Stanford
Stanford Research Computing

Zhiyong Zhang, Research Software Developer

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Computing interests and specialties:

  • Quantum Computing
  • Artificial Intelligence (AI) and Deep Learning (ML)
  • Python/R/CUDA/MPI
  • Computational Chemistry
  • Renewable Energy
  • Genomics analysis
  • Exascale and emerging architectures in HPC

Came to us from:

Profile

Zhiyong Zhang is a computational and R&D scientist with decades of experience in the application of computational science for research. He works with researchers from virtually all disciplines to resolve computational and research challenges. As Stanford Research Computing’s resident expert in HPC code optimization, Zhiyong has developed and taught HPC workshops on a range of topics, from basic to advanced, including a series of Nvidia Deep Learning Institute workshops. 

With advanced degrees in Chemical Physics and Computer and Information Sciences from Ohio State University, Zhiyong brings a blend of theoretical knowledge and practical expertise to the table. His academic background equips him with a deep understanding of both the scientific and computational aspects of research, making him an invaluable resource for your projects.

As a NVIDIA Deep Learning Institute Ambassador, Zhiyong has a profound understanding of AI and ML. He has developed and taught numerous workshops on these subjects, helping researchers harness the power of AI for their projects. 

Just like his fellow Research Computing consultant-instructors (they currently number seven), Zhiyong aims to foster community among research software coders, systems architects, and researchers.

Zhiyong is also helping to foster a cross-institutional community of developers and application researchers, with the aim to create protein folding ML models that will revolutionize drug discovery and other applications.

His enthusiasm can be contagious. In Zhiyong’s own words, "The progress being made with these technologies is inspiring, and that keeps me motivated to help Stanford researchers in any field to make the best use of our newest and most powerful systems.”

Notable Projects

  •  The Human Sleep Project. This initiative aims to uncover new insights into human sleep patterns and disorders.
  • Protein folding. Improve algorithms and computational efficiencies on HPC.
  • Large scale and memory efficient distributed ML models.