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Kyle Chard

ScholarBERT: Bigger is Not Always Better



Zhi Hong , Aswathy Ajith , Gregory Pauloski , Eamon Duede , Carl Malamud , Roger Magoulas , Kyle Chard , Ian Foster

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* 16 pages. 4 figures. 8 tables 
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Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing



Logan Ward , Ganesh Sivaraman , J. Gregory Pauloski , Yadu Babuji , Ryan Chard , Naveen Dandu , Paul C. Redfern , Rajeev S. Assary , Kyle Chard , Larry A. Curtiss , Rajeev Thakur , Ian Foster

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* camera-ready version for ML in HPC Environments 2021 
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KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks



J. Gregory Pauloski , Qi Huang , Lei Huang , Shivaram Venkataraman , Kyle Chard , Ian Foster , Zhao Zhang

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* To be published in the proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21) 
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AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text



Zhi Hong , J. Gregory Pauloski , Logan Ward , Kyle Chard , Ben Blaiszik , Ian Foster

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* 17 single-column pages, 6 figures, and 6 tables 
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Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis



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

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



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

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* 11 pages, 5 figures 
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Enabling real-time multi-messenger astrophysics discoveries with deep learning



E. A. Huerta , Gabrielle Allen , Igor Andreoni , Javier M. Antelis , Etienne Bachelet , Bruce Berriman , Federica Bianco , Rahul Biswas , Matias Carrasco , Kyle Chard , Minsik Cho , Philip S. Cowperthwaite , Zachariah B. Etienne , Maya Fishbach , Francisco Förster , Daniel George , Tom Gibbs , Matthew Graham , William Gropp , Robert Gruendl , Anushri Gupta , Roland Haas , Sarah Habib , Elise Jennings , Margaret W. G. Johnson , Erik Katsavounidis , Daniel S. Katz , Asad Khan , Volodymyr Kindratenko , William T. C. Kramer , Xin Liu , Ashish Mahabal , Zsuzsa Marka , Kenton McHenry , Jonah Miller , Claudia Moreno , Mark Neubauer , Steve Oberlin , Alexander R. Olivas , Donald Petravick , Adam Rebei , Shawn Rosofsky , Milton Ruiz , Aaron Saxton , Bernard F. Schutz , Alex Schwing , Ed Seidel , Stuart L. Shapiro , Hongyu Shen , Yue Shen , Leo Singer , Brigitta M. Sipőcz , Lunan Sun , John Towns , Antonios Tsokaros , Wei Wei , Jack Wells , Timothy J. Williams , Jinjun Xiong , Zhizhen Zhao

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* Nature Reviews Physics volume 1, pages 600-608 (2019) 
 * Invited Expert Recommendation for Nature Reviews Physics. The art work produced by E. A. Huerta and Shawn Rosofsky for this article was used by Carl Conway to design the cover of the October 2019 issue of Nature Reviews Physics 
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Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era



Gabrielle Allen , Igor Andreoni , Etienne Bachelet , G. Bruce Berriman , Federica B. Bianco , Rahul Biswas , Matias Carrasco Kind , Kyle Chard , Minsik Cho , Philip S. Cowperthwaite , Zachariah B. Etienne , Daniel George , Tom Gibbs , Matthew Graham , William Gropp , Anushri Gupta , Roland Haas , E. A. Huerta , Elise Jennings , Daniel S. Katz , Asad Khan , Volodymyr Kindratenko , William T. C. Kramer , Xin Liu , Ashish Mahabal , Kenton McHenry , J. M. Miller , M. S. Neubauer , Steve Oberlin , Alexander R. Olivas Jr , Shawn Rosofsky , Milton Ruiz , Aaron Saxton , Bernard Schutz , Alex Schwing , Ed Seidel , Stuart L. Shapiro , Hongyu Shen , Yue Shen , Brigitta M. Sipőcz , Lunan Sun , John Towns , Antonios Tsokaros , Wei Wei , Jack Wells , Timothy J. Williams , Jinjun Xiong , Zhizhen Zhao

* 15 pages, no figures. White paper based on the "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at NCSA, October 17-19, 2018 http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/ 
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DLHub: Model and Data Serving for Science



Ryan Chard , Zhuozhao Li , Kyle Chard , Logan Ward , Yadu Babuji , Anna Woodard , Steve Tuecke , Ben Blaiszik , Michael J. Franklin , Ian Foster

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* 10 pages, 8 figures, conference paper 
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