Hanqi Guo
Ohio State University, OH·Computer Science
Large scientific simulations now produce more data than computers can store or move. A single fusion simulation can produce 200 petabytes, and a climate simulation can produce 260 terabytes every 16 seconds.
- Fields
Computer Science
Engineering
Mathematics & Statistics
- Topics
lossy compression
data compression
topological data analysis
- Grants
- 2 known grants, through Aug 2027
What they work on
- Develop lossy compression algorithms for scalar and vector field data that preserve topological features, such as critical points, for analysis and visualization in cosmology, climate and fusion simulations.
- Define and enforce topology-aware constraints within lossy compressors so compressed data supports both in situ and post hoc analysis at extreme scale, released as public software.
- Build programming interfaces and a compressor generator in the FZ framework so scientists can compose specialized lossy compressors in Python, refactoring the SZ compressor into mix-and-match modules.
- Create visualization, quality assessment and graphical interface tools, building on Z-checker, QCAT, ParaView and VTK, that search for optimal module combinations and show compression ratio, speed and quality trade-offs.
More about the research
Lossy compression shrinks data by discarding some detail, but general tools may erase the very features scientists care about. They may also not fit what a given scientist needs in size, speed and accuracy. These projects build compression methods and software that keep scientific data useful while making it small.
Why it matters
Scientists running large simulations in cosmology, climate and fusion energy could store and move far less data without losing the features their discoveries depend on. FZ could let research groups build compressors tailored to their needs without duplicating years of specialized coding, easing storage and network bottlenecks on supercomputers.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Chengming Zhang
University of Houston, TX·Computer Science
Adapt GPU error-bounded lossy compressors such as cuSZ to real scientific workflows, supporting diverse data formats and compression quality targets, with quality checked by tools like QCAT.
Yang Zhang
Miami University-Oxford, OH·Computer Science
Characterize the artifacts that error-controlled lossy compressors introduce into scientific data and later analyses, and establish a benchmark for artifact mitigation.
Heike Jagode
University of Tennessee-Knoxville, TN·Computer Science
Add performance and power monitoring for AI hardware to the MINCER infrastructure so researchers can make informed choices about optimizing and configuring AI systems.
The school
Ohio State University: 71 labs on PI Pages, $1.6B a year on research, 882 research doctorates a year.
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