Yang Zhang

What they work on

1. Characterize the artifacts that error-controlled lossy compressors introduce into scientific data and later analyses, and establish a benchmark for artifact mitigation.
2. Design deep-learning models, with transfer learning to reduce training cost and fusion of feature-specific outputs, to remove compression artifacts from raw data and features of interest.
3. Validate recovered data with tailored uncertainty quantification, then optimize, parallelize and integrate the framework into existing lossy compressors.
4. Create training modules linking healthcare problems to quantum algorithms and quantum-compatible data representations, with applications in molecular modeling and diagnostic analysis.
5. Develop hands-on modules on portable, reproducible quantum software and on hybrid quantum–classical workflows for protein structure prediction and DNA and RNA therapeutics modeling.

More about the research

Why it matters

Better artifact removal could let scientists in climate, cosmology and fusion research shrink huge datasets for storage and transfer without corrupting their results. The quantum training program aims to prepare a workforce able to apply quantum computing to drug discovery, molecular modeling, diagnostics and DNA and RNA therapeutics, using openly shared materials.

Known grants

From NIH and NSF research grants. Labs often have other funding we can't see.

Closest labs

The school

Miami University-Oxford: 5 labs on PI Pages, $29M a year on research, 71 research doctorates a year.

More about Miami University-Oxford

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