Daoce Wang 王道策

Daoce Wang
Assistant Professor
Department of Computer Science
University of Nebraska Omaha
Email: daocwang@iu.edu
Phone: 352‑871‑4124
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Biography

Dr. Daoce Wang is an Assistant Professor in the Department of Computer Science at the University of Nebraska Omaha. He received his B.S., M.S., and Ph.D. degrees in Computer Science/Engineering from the University of Electronic Science and Technology of China (UESTC, 2018), the University of Florida (UFL, 2020), and Indiana University Bloomington (IUB, 2025, advised by Dr. Dingwen Tao and Dr. Fengguang Song). Dr. Daoce Wang was a summer research intern at Los Alamos National Laboratory in 2021, 2022, 2023, and 2024. His research interests include high-performance computing (HPC), scientific data management and analytics, scientific data compression and visualization, and high-performance machine learning. Over the past five years, he has published over ten papers in top-tier venues in computer systems and AI, including SC, PPoPP, TPDS, HPDC, ICS, EuroSys, and NeurIPS.

Research

Education

Selected Publications

CSUR ‘25

Sheng Di et al.
A Survey on Error-Bounded Lossy Compression for Scientific Datasets.
ACM Computing Surveys, impact factor: 23.8

ICS ‘25

Wenqi Jia, Zhewen Hu, Youyuan Liu, Boyuan Zhang, Jinzhen Wang, Jinyang Liu, Wei Niu, Stavros Kalafatis, Junzhou Huang, Sian Jin, Daoce Wang, Jiannan Tian, and Miao Yin.
NeurLZ: An Online Neural Learning-based Method to Enhance Scientific Lossy Compression.
ACM International Conference on Supercomputing 2025, Salt Lake City, UT, USA, June 8–11, 2025.

PPoPP ‘25

Baixi Sun, Weijin Liu, J. Gregory Pauloski, Jiannan Tian, Jinda Jia, Daoce Wang, Mingkai Zheng, Sheng Di, Sian Jin, Zhao Zhang, Xiaodong Yu, Guangming Tan, and Dingwen Tao.
COMPSO: Optimizing Gradient Compression for Distributed Training with Second-Order Optimizers.
30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Las Vegas, NV, USA, March 1-5, 2025.

NeurIPS ‘24

Jinda Jia, Cong Xie, Hanlin Lu, Daoce Wang, Hao Feng, Chengming Zhang, Baixi Sun, Haibin Lin, Zhi Zhang, Xin Liu, and Dingwen Tao.
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training.
The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 9–12, 2024.

SC ‘24

Daoce Wang, Pascal Grosset, Jesus Pulido, Tushar M. Athawale, Jiannan Tian, Kai Zhao, Zarija Lukic, Axel Huebl, Zhe Wang, James Ahrens, and Dingwen Tao.
A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization. Artifacts Available BadgeArtifacts Evaluated - Functional BadgeResults Reproduced Badge
The International Conference for High Performance Computing, Networking, Storage, and Analysis, Atlanta, GA, USA, November 17–22, 2024.

TPDS ‘24

Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, Sian Jin, Kai Zhao, James Ahrens, and Dingwen Tao.
TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulation.
IEEE Transactions on Parallel and Distributed Systems, impact factor: 5.3

EUROSYS ‘24

Sian Jin, Sheng Di, Frédéric Vivien, Daoce Wang, Yves Robert, Dingwen Tao, and Franck Cappello.
Concealing Compression-Accelerated I/O for HPC Applications through In Situ Task Scheduling. Artifacts Available BadgeArtifacts Evaluated - Functional BadgeResults Reproduced Badge
Proceedings of the Nineteenth European Conference on Computer Systems, Athens, Greece, April 22–25, 2024.

SC ‘23

Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, Sian Jin, Houjun Tang, Jean Sexton, Sheng Di, Zarija Luki, Kai Zhao, Bo Fang, Franck Cappello, James Ahrens, and Dingwen Tao.
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications. Artifacts Available BadgeArtifacts Evaluated - Functional BadgeResults Reproduced Badge
The International Conference for High Performance Computing, Networking, Storage, and Analysis, Denver, Colorado, USA, November 12–17, 2023.

HPDC ‘22

Daoce Wang, Jesus Pulido, Pascal Grosset, Sian Jin, Jiannan Tian, James Ahrens, and Dingwen Tao.
TAC: Optimizing Error-Bounded Lossy Compression for Three Dimensional Adaptive Mesh Refinement Simulations.
ACM International Symposium on High-Performance Parallel and Distributed Computing, Minneapolis, Minnesota, USA, June 27–July 1, 2022.

CLUSTER ‘21

Bo Fang*, Daoce Wang*, Sian Jin, Quincey Koziol, Zhao Zhang, Qiang Guan, Suren Byna, Sriram Krishnamoorthy, and Dingwen Tao. (* equal contribution)
Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights.
The 2021 IEEE International Conference on Cluster Computing, Portland, OR, USA, September 7-10, 2021.

Workshop & Posters

Doctoral Showcase

Daoce Wang, Dingwen Tao.
Designing Efficient Data Reduction Approaches for Multi-resolution Simulations on HPC Systems.
SC ’24 Doctoral Showcase.

DRBSD-10

Yanni Etchi, Daoce Wang, Pascal Grosset, Terece Turton, James Ahrens, and David Rogers.
An Exploration of How Volume Rendering is Impacted by Lossy Data Reduction.
The 10th International Workshop on Data Analysis and Reduction for Big Scientific Data (in conjunction with SC 24), Atlanta, GA, USA, Nov 18, 2024. Best Paper Runner-up

DRBSD-10

Qing Zheng, Brian Atkinson, Daoce Wang, Jason Lee, John Patchett, Dominic Manno, and Gary Grider.
Accelerating Viz Pipelines Using Near-Data Computing: An Early Experience.
The 10th International Workshop on Data Analysis and Reduction for Big Scientific Data (in conjunction with SC 24), Atlanta, GA, USA, Nov 18, 2024.

DRBSD-9

Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, James Ahrens, and Dingwen Tao.
Analyzing Impact of Data Reduction Techniques on Visualization for AMR Applications Using AMReX Framework.
The 9th International Workshop on Data Analysis and Reduction for Big Scientific Data (in conjunction with SC 23), Denver, CO, USA, Nov 12, 2023.

SC ‘21 Poster

Daoce Wang, Jesus Pulido, Pascal Grosset, Sian Jin, Jiannan Tian, James Ahrens, and Dingwen Tao.
In-Situ Data Reduction for AMR-Based Cosmology Simulations.
ACM Student Research Competition: Graduate Posters at SC 21.

Experience

Research Intern, Los Alamos National Laboratory

2024 May – 2024 August

Research Intern, Los Alamos National Laboratory

2023 May – 2023 August

Research Intern, Los Alamos National Laboratory

2022 June – 2022 August

Research Intern, Los Alamos National Laboratory

2021 June – 2021 August

Reviewer/Sub-reviewer