Publications

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Journal Articles

The rhizodynamics robot: Automated imaging system for studying long-term dynamic root growth

Published in PLOS ONE, 2023

We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months.

Recommended citation: Rajanala A, Taylor IW, McCaskey E, Pierce C, Ligon J, et al. (2023) The rhizodynamics robot: Automated imaging system for studying long-term dynamic root growth. PLOS ONE 18(12): e0295823. https://doi.org/10.1371/journal.pone.0295823
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Network-based multi-task learning models for biomarker selection and cancer outcome prediction

Published in Bioinformatics, 2019

We introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type.

Recommended citation: Zhibo Wang, Zhezhi He, Milan Shah, Teng Zhang, Deliang Fan, Wei Zhang, Network-based multi-task learning models for biomarker selection and cancer outcome prediction, Bioinformatics, Volume 36, Issue 6, March 2020, Pages 1814–1822, https://doi.org/10.1093/bioinformatics/btz809
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Conference Papers

A Portable, Fast, DCT-based Compressor for AI Accelerators

Published in Proceedings of HPDC '24, 2024

In this paper, we propose a novel, portable, DCT-based lossy compressor that can be used across a variety of AI accelerators.

Recommended citation: Milan Shah, Xiaodong Yu, Sheng Di, Michela Becchi, and Franck Cappello. 2024. A Portable, Fast, DCT-based Compressor for AI Accelerators. In Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC '24). Association for Computing Machinery, New York, NY, USA, 109–121. https://doi.org/10.1145/3625549.3658662
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GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations

Published in Proceedings of IPDPS '23, 2023

In this paper, we explore different lossy compression strategies to substantially shrink quantum circuit tensors in the QTensor package (a state-of-the-art tensor network quantum circuit simulator) while ensuring the reconstructed data satisfy the user-needed fidelity.

Recommended citation: M. Shah et al., "GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations," 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), St. Petersburg, FL, USA, 2023, pp. 757-767, doi: 10.1109/IPDPS54959.2023.00081.
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Lightweight Huffman Coding for Efficient GPU Compression

Published in Proceedings of ICS '23, 2023

Our work seeks to reduce the Huffman coding runtime with minimal-to-no impact on cuSZ's compression efficiency.

Recommended citation: Milan Shah, Xiaodong Yu, Sheng Di, Michela Becchi, and Franck Cappello. 2023. Lightweight Huffman Coding for Efficient GPU Compression. In Proceedings of the 37th ACM International Conference on Supercomputing (ICS '23). Association for Computing Machinery, New York, NY, USA, 99–110. https://doi.org/10.1145/3577193.3593736
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Accelerating Random Forest Classification on GPU and FPGA

Published in Proceedings of ICPP '22, 2023

In this work, we accelerate RF classification on GPU and FPGA.

Recommended citation: Milan Shah, Reece Neff, Hancheng Wu, Marco Minutoli, Antonino Tumeo, and Michela Becchi. 2023. Accelerating Random Forest Classification on GPU and FPGA. In Proceedings of the 51st International Conference on Parallel Processing (ICPP '22). Association for Computing Machinery, New York, NY, USA, Article 4, 1–11. https://doi.org/10.1145/3545008.3545067
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