Malachi Schram

According to our database, Malachi Schram authored at least 26 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, T. Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, E. Fol, S. Furletov, Y. Gao, James Giroux, N. C. Gunawardhana Waduge, O. Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, H. Rajendran, K. Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

Comput. Softw. Big Sci., December, 2024

Robust errant beam prognostics with conditional modeling for particle accelerators.

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta

Mach. Learn. Sci. Technol., March, 2024

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory.

Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh M. Halappanavar, Frank Liu, Peng Li

Trans. Mach. Learn. Res., 2024

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions.

Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave

Mach. Learn. Sci. Technol., 2024

SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver.

Daniel Lersch, Malachi Schram, Zhenyu Dai, Kishansingh Rajput, Xingfu Wu, N. Sato, J. Taylor Childers

CoRR, 2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach.

Yu Wang, Yuxuan Yin, Karthik Somayaji N. S., Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Dataset for Investigating Anomalies in Compute Clusters.

Diana McSpadden, Yasir Alanazi, Bryan Hess, Laura Hild, Mark Jones, Yiyang Lu, Ahmed Mohammed, Wesley Moore, Jie Ren, Malachi Schram, Evgenia Smirni

Dataset, October, 2023

Dataset for Investigating Anomalies in Compute Clusters.

Diana McSpadden, Yasir Alanazi, Bryan Hess, Laura Hild, Mark Jones, Yiyang Lub, Ahmed Mohammed, Wesley Moore, Jie Ren, Malachi Schram, Evgenia Smirni

CoRR, 2023

Uncertainty Aware Deep Learning for Particle Accelerators.

Kishansingh Rajput, Malachi Schram, Karthik Somayaji N. S.

CoRR, 2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia.

Diana McSpadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

CoRR, 2023

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, Anja Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Yuan Gao, James Giroux, N. C. Gunawardhana Waduge, R. Harish, O. Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, Martin L. Purschke, K. Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, Nobuo Sato, T. Satogata, German Sborlini, Malachi Schram, David Shih, J. Singh, R. Singh, Andrzej Siódmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, Abdel Nasser Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, Anselm Vossen, Eric Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

CoRR, 2023

Multi-module based CVAE to predict HVCM faults in the SNS accelerator.

Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau

CoRR, 2023

AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution.

Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders.

Majdi I. Radaideh, Chris Pappas, Jared Walden, Dan Lu, Lasitha Vidyaratne, Thomas Britton, Kishansingh Rajput, Malachi Schram, Sarah Cousineau

Digit. Signal Process., 2022

2021

Identifying build orientation of 3D-printed materials using convolutional neural networks.

Jan Strube, Malachi Schram, Sabiha Rustam, Zachary Kennedy, Tamás Varga

Stat. Anal. Data Min., 2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn).

Francis J. Alexander, James A. Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert J. Debusschere, Anthony M. DeGennaro, Nikoli Dryden, J. Austin Ellis, Ian T. Foster, Cristina Garcia-Cardona, Sayan Ghosh, Peter Harrington, Yunzhi Huang, Shantenu Jha, Travis Johnston, Ai Kagawa, Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishnaiah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan T. Ward, Paul M. Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung-Jun Yoon

Int. J. High Perform. Comput. Appl., 2021

Artificial Intelligence and Machine Learning in Nuclear Physics.

Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler

CoRR, 2021

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator.

Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit A. Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske

CoRR, 2021

Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster.

D. Kafkes, Malachi Schram

CoRR, 2021

2019

TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows.

Joshua Suetterlein, Ryan D. Friese, Nathan R. Tallent, Malachi Schram

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Optimizing Distributed Data-Intensive Workflows.

Ryan D. Friese, Nathan R. Tallent, Malachi Schram, Mahantesh Halappanavar, Kevin J. Barker

Proceedings of the IEEE International Conference on Cluster Computing, 2018

Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows.

Alok Singh, Ilkay Altintas, Malachi Schram, Nathan R. Tallent

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties.

Ryan D. Friese, Mahantesh Halappanavar, Arun V. Sathanur, Malachi Schram, Darren J. Kerbyson, Luis de la Torre

Proceedings of the Job Scheduling Strategies for Parallel Processing, 2017

Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows.

Alok Singh, Eric G. Stephan, Malachi Schram, Ilkay Altintas

Proceedings of the 13th IEEE International Conference on e-Science, 2017

2016

Leveraging large sensor streams for robust cloud control.

Alok Singh, Eric G. Stephan, Todd Elsethagen, Matt MacDuff, Bibi Raju, Malachi Schram, Kerstin Kleese van Dam, Darren J. Kerbyson, Ilkay Altintas

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015

Towards efficient scheduling of data intensive high energy physics workflows.

Mahantesh Halappanavar, Malachi Schram, Luis de la Torre, Kevin J. Barker, Nathan R. Tallent, Darren J. Kerbyson

Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science, 2015