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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Zero-to-Sidekick Neural Networks: Part 1- What are they?
Published:
Detective Spooner: “Human beings have dreams. Even dogs have dreams, but no you, you are just a machine. An imitation of life. Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece?”
Graphs in Computer Science: 10 miles wide, 1 inch deep
Published:
Remember that movie The Social Network from 2010? That was a solid one indeed. I liked the part where Andrew Garfield’s character said “I like standing next to you, Sean. It makes me look so tough.” That movie is 14 years old now. Let that sink in.
portfolio
publications
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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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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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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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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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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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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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.