Biography

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 heterogeneous digital twin workflow. 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.

Interests
  • High-Performance Computing
  • Exascale Data Management
  • In-Situ Processing
  • Digital Twin Workflow
Education

  • Ph.D. in Computer Science, 2024

    The University of Utah

  • B.E. in Telecommunication Engineering, 2018

    Beijing University of Posts and Telecommunications

Skills

Coding

C/C++, Python, MPI, CUDA

HPC Platforms

Perlmutter@NERSC, Polaris@ALCF
Frontera/ Longhorn/ Lonestar6@TACC
Casper/ Cheyenne@NCAR

Languages

English, Chinese

Experience

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

Projects

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.
News
National Data Platform (NDP)
DataSpaces
An Extreme Scale Data Management Framework
Paper Code
DataSpaces
Openfold
A faithful but trainable PyTorch reproduction of DeepMind’s AlphaFold 2.
Paper Code
Openfold

Selected Publications

Bo Zhang, Philip E Davis, Zhao Zhang, Keita Teranishi, Manish Parashar (2024). Dual Channel Dual Staging: Hierarchical and Portable Staging for GPU-Based In-Situ Workflow. In HiPC 2024 (Accepted).

Cite

Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-en Andrew Ban, Shiyang Chen, Minjia Zhang, Conglong Li, Shuaiwen Leon Song, Yuxiong He, Peter K Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, Mohammed AlQuraishi (2024). OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods.

Cite DOI

Bo Zhang, Philip E Davis, Nicolas Morales, Zhao Zhang, Keita Teranishi, Manish Parashar (2023). Optimizing Data Movement for GPU-Based In-Situ Workflow using GPUDirect RDMA. In Euro-Par 2023 (Best Paper Candidate).

Cite DOI

Bo Zhang, Pradeep Subedi, Philip E Davis, Francesco Rizzi, Keita Teranishi, Manish Parashar (2022). Assembling Portable In-Situ Workflow from Heterogeneous Components using Data Reorganization. In CCGrid 2022.

Cite DOI

Contact

  • bozhang AT sci.utah.edu
  • 72 S Central Campus Drive, Room 2646, Salt Lake City, Utah 84112