Search Results for author: Ryan Chard

Found 16 papers, 6 papers with code

Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers

no code implementations25 Aug 2023 Alexander Brace, Rafael Vescovi, Ryan Chard, Nickolaus D. Saint, Arvind Ramanathan, Nestor J. Zaluzec, Ian Foster

The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day.

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

1 code implementation17 Aug 2023 Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, E. A. Huerta, Volodymyr Kindratenko, Jordan Fuhrman, Maryellen Giger, Ryan Chard, Kibaek Kim, Ravi Madduri

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.

Federated Learning Privacy Preserving

AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources

no code implementations9 Apr 2023 Anakha V Babu, Tekin Bicer, Saugat Kandel, Tao Zhou, Daniel J. Ching, Steven Henke, Siniša Veseli, Ryan Chard, Antonino Miceli, Mathew Joseph Cherukara

We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography.

Retrieval

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

2 code implementations15 Mar 2023 Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson, Ryan Chard, Yadu Babuji, Ganesh Sivaraman, Sutanay Choudhury, Kyle Chard, Rajeev Thakur, Ian Foster

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators.

Management

OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science

no code implementations13 Feb 2023 Maksim Levental, Arham Khan, Ryan Chard, Kazutomo Yoshii, Kyle Chard, Ian Foster

In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing.

Low-latency processing

Deep learning at the edge enables real-time streaming ptychographic imaging

no code implementations20 Sep 2022 Anakha V Babu, Tao Zhou, Saugat Kandel, Tekin Bicer, Zhengchun Liu, William Judge, Daniel J. Ching, Yi Jiang, Sinisa Veseli, Steven Henke, Ryan Chard, YuDong Yao, Ekaterina Sirazitdinova, Geetika Gupta, Martin V. Holt, Ian T. Foster, Antonino Miceli, Mathew J. Cherukara

Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.

Globus Automation Services: Research process automation across the space-time continuum

no code implementations19 Aug 2022 Ryan Chard, Jim Pruyne, Kurt McKee, Josh Bryan, Brigitte Raumann, Rachana Ananthakrishnan, Kyle Chard, Ian Foster

We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, \emph{flows}, and the execution of such flows in heterogeneous research environments.

Management

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

1 code implementation1 Jul 2022 Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian Foster

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation.

Management

Ultrafast Focus Detection for Automated Microscopy

no code implementations26 Aug 2021 Maksim Levental, Ryan Chard, Kyle Chard, Ian Foster, Gregg A. Wildenberg

Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus.

Semantic Segmentation

Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

no code implementations15 Dec 2020 E. A. Huerta, Asad Khan, Xiaobo Huang, Minyang Tian, Maksim Levental, Ryan Chard, Wei Wei, Maeve Heflin, Daniel S. Katz, Volodymyr Kindratenko, Dawei Mu, Ben Blaiszik, Ian Foster

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.

Distributed Computing Gravitational Wave Detection

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

1 code implementation16 Oct 2020 Maksim Levental, Ryan Chard, Joseph A. Libera, Kyle Chard, Aarthi Koripelly, Jakob R. Elias, Marcus Schwarting, Ben Blaiszik, Marius Stan, Santanu Chaudhuri, Ian Foster

Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more.

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

BIG-bench Machine Learning

funcX: A Federated Function Serving Fabric for Science

no code implementations7 May 2020 Ryan Chard, Yadu Babuji, Zhuozhao Li, Tyler Skluzacek, Anna Woodard, Ben Blaiszik, Ian Foster, Kyle Chard

These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e. g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available.

Distributed, Parallel, and Cluster Computing

DLHub: Model and Data Serving for Science

no code implementations27 Nov 2018 Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster

Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.

Distributed Computing

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