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Robert Underwood

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Assistant Computer Scientist

Argonne National Laboratory

United States

Research Interests

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Science Preserving Data Approximation and Input Output Optimization Using Artificial Intelligence for Real-time High-resolution Tomography on Integrated Research Infrastructure

Open Date: 2023-04-01

Close Date: 2023-09-01

Articles (20)

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Collaborators (9)

Zhaorui Zhang

Research Assistant Professor

The Hong Kong Polytechnic University

HONG KONG

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Kai Zhao

Assistant Professor

Florida State University

UNITED STATES

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Rick Stevens

University of Chicago

UNITED STATES

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Jinyang Liu

Associate Professor

Shanghai Jiao Tong University

CHINA

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Jiannan Tian

Assistant Professor

Oakland University

UNITED STATES

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Tomohiro Ueno

Research Scientist

RIKEN Center for Computational Science

JAPAN

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Miaoqi Chu

Argonne National Laboratory

UNITED STATES

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Sian Jin

Assistant Professor

Temple University

UNITED STATES

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Yafan Huang

Assistant Professor

University of Maryland

UNITED STATES

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Robert Underwood

Research Interests

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Recent Grants

Articles (20)

OPAL: On-demand Progressive Accelerated Scientific Lossy Compression

TZ: Achieving High-Ratio Scientific Data Compression on GPUs with Global Data Decomposition

EAIRA: Establishing a methodology for evaluating LLMs as scientific research assistants

Bridging Information Theory and Practice for Scientific Lossy Compression

A Survey on Error-Bounded Lossy Compression for Scientific Datasets

LCP: Enhancing Scientific Data Management with L ossy C ompression for P articles

Benchmarking Cutting-Edge Scientific Error-Bounded Lossy Compressors on Correlation-Based Rate-Distortion

Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing

Automated MCQA Benchmarking at Scale: Evaluating Reasoning Traces as Retrieval Sources for Domain Adaptation of Small Language Models

Beyond End-to-End: Understanding the Limits of LLMs in Scientific Problem Solving

Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant

What to Support When You’re Compressing

lsCOMP: Efficient Light Source Compression

Sensitivity and Impacts on Parallel Compression of Prediction of Lossy Compression Ratios for Scientific Data

A Memory-efficient and Computation-balanced Lossy Compressor on Wafer-Scale Engine

To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O

DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models

EvoStore: Towards Scalable Storage of Evolving Learning Models

CUSZ-<i>i</i>: High-Ratio Scientific Lossy Compression on GPUs with Optimized Multi-Level Interpolation

CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction

Collaborators (9)

Zhaorui Zhang

Kai Zhao

Rick Stevens

Jinyang Liu

Jiannan Tian

Tomohiro Ueno

Miaoqi Chu

Sian Jin

Yafan Huang

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