Bo Zhang is a Post-Doctoral Research Associate in the Scientific Computing and Imaging (SCI) Institute at The University of Utah. He received his Ph.D. in Computer Science from The University of Utah in 2024, under the supervision of Prof. Manish Parashar. He received his bachelor degree in telecommunication engineering from Beijing University of Posts and Telecommunications in 2018. His research interests include high-performance computing, extreme-scale data management, in-situ processing and scalable deep learning on supercomputers. In the past five years, he mainly worked on the workflow-level I/O abstraction for GPU-dense supercomputers.

Bo is on the academic job market for tenure-track positions starting from Fall 2025. Feel free to reach out if your department has open positions.

  • High-Performance Computing
  • Exascale Data Management
  • In-Situ Processing
  • Scalable Deep Learning on HPC
  • Ph.D. in Computer Science, 2024

    The University of Utah

  • B.E. in Telecommunication Engineering, 2018

    Beijing University of Posts and Telecommunications



C/C++, Python, MPI, CUDA


English, Chinese


Scientific Computing and Imaging Institute, The University of Utah
Post-Doctoral Research Associate
April 2024 – Present Salt Lake City, Utah
Scientific Computing and Imaging Institute, The University of Utah
Graduate Research Assistant
January 2022 – April 2024 Salt Lake City, Utah
Collaborate with Sandia National Lab, Oak Ridege National Lab and NASA.
Samsung Semiconductor, Inc.
Intern, Neural Processor Lab Research Scientist
May 2023 – August 2023 San Jose, California
Texas Advanced Computing Center
Research Engineering/Scientist Professional
May 2022 – August 2022 Austin, Texas
National Center for Atmospheric Research
Summer Internships in Parallel Computational Science
May 2021 – July 2021 Boulder, Colorado
Rutgers Discovery Informatics Institute, Rutgers University
Graduate Research Assistant
August 2018 – December 2021 Piscataway, New Jersey
Collaborate with Sandia National Lab. (Transfer to The University of Utah with my advisor, Dr. Manish Parashar.)


National Data Platform (NDP)
A federated and extensible data ecosystem to promote collaboration, innovation, and equitable use of data on top of existing cyberinfrastructure capabilities.
An Extreme Scale Data Management Framework
A faithful but trainable PyTorch reproduction of DeepMind’s AlphaFold 2.

Selected Publications

(2024). OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods.

Cite DOI

(2023). Optimizing Data Movement for GPU-Based In-Situ Workflow using GPUDirect RDMA. In Euro-Par 2023 (Best Paper Candidate).

Cite DOI

(2022). Assembling Portable In-Situ Workflow from Heterogeneous Components using Data Reorganization. In CCGrid 2022.

Cite DOI


  • bozhang AT
  • 72 S Central Campus Drive, Room 3760, Salt Lake City, Utah 84112