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2020 – today
- 2025
[j8]Diana McSpadden
, Mark Jones, Ahmed Hossam Mohammed, Bryan Hess, Malachi Schram:
Establishing Machine Learning Operations for Continual Learning in Computing Clusters: A Framework for Monitoring and Optimizing Cluster Behavior. IEEE Softw. 42(1): 42-50 (2025)- 2024
- [j7]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:
Artificial Intelligence for the Electron Ion Collider (AI4EIC). Comput. Softw. Big Sci. 8(1): 5 (2024) - [j6]Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta:
Robust errant beam prognostics with conditional modeling for particle accelerators. Mach. Learn. Sci. Technol. 5(1): 15044 (2024) - [j5]Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave:
Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. Mach. Learn. Sci. Technol. 5(4): 45009 (2024) - [j4]Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh M. Halappanavar, Frank Liu, Peng Li:
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. Trans. Mach. Learn. Res. 2024 (2024) - [c9]Yu Wang, Yuxuan Yin, Karthik Somayaji N. S., Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li:
Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach. AAAI 2024: 15698-15705 - [i14]Daniel Lersch, Malachi Schram, Zhenyu Dai, Kishansingh Rajput, Xingfu Wu, N. Sato, J. Taylor Childers:
SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver. CoRR abs/2407.00051 (2024) - 2023
- [c8]Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li:
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. AAAI 2023: 10244-10252 - [d1]Diana McSpadden, Yasir Alanazi, Bryan Hess, Laura Hild, Mark Jones, Yiyang Lu, Ahmed Mohammed, Wesley Moore, Jie Ren, Malachi Schram, Evgenia Smirni:
Dataset for Investigating Anomalies in Compute Clusters. Zenodo, 2023
- [i13]Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau:
Multi-module based CVAE to predict HVCM faults in the SNS accelerator. CoRR abs/2304.10639 (2023) - [i12]Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave:
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions. CoRR abs/2307.02367 (2023) - [i11]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:
Artificial Intelligence for the Electron Ion Collider (AI4EIC). CoRR abs/2307.08593 (2023) - [i10]Diana McSpadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter:
A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia. CoRR abs/2307.14185 (2023) - [i9]Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li:
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. CoRR abs/2308.13011 (2023) - [i8]Kishansingh Rajput, Malachi Schram, Karthik Somayaji N. S.:
Uncertainty Aware Deep Learning for Particle Accelerators. CoRR abs/2309.14502 (2023) - [i7]Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li:
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach. CoRR abs/2310.13110 (2023) - [i6]Diana McSpadden, Yasir Alanazi, Bryan Hess, Laura Hild, Mark Jones, Yiyang Lub, Ahmed Mohammed, Wesley Moore, Jie Ren, Malachi Schram, Evgenia Smirni:
Dataset for Investigating Anomalies in Compute Clusters. CoRR abs/2311.16129 (2023) - [i5]Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta:
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators. CoRR abs/2312.10040 (2023) - 2022
- [j3]Majdi I. Radaideh, Chris Pappas, Jared Walden, Dan Lu, Lasitha Vidyaratne, Thomas Britton, Kishansingh Rajput, Malachi Schram, Sarah Cousineau:
Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders. Digit. Signal Process. 130: 103704 (2022) - 2021
- [j2]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:
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [j1]Jan Strube, Malachi Schram, Sabiha Rustam, Zachary Kennedy, Tamás Varga:
Identifying build orientation of 3D-printed materials using convolutional neural networks. Stat. Anal. Data Min. 14(6): 575-582 (2021) - [i4]D. Kafkes, Malachi Schram:
Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster. CoRR abs/2105.12847 (2021) - [i3]Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit A. Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske:
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator. CoRR abs/2110.12006 (2021) - [i2]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:
Artificial Intelligence and Machine Learning in Nuclear Physics. CoRR abs/2112.02309 (2021)
2010 – 2019
- 2019
- [c7]Joshua Suetterlein, Ryan D. Friese, Nathan R. Tallent, Malachi Schram:
TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows. IEEE BigData 2019: 383-394 - 2018
- [c6]Alok Singh, Ilkay Altintas, Malachi Schram, Nathan R. Tallent:
Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows. IEEE BigData 2018: 3905-3914 - [c5]Ryan D. Friese, Nathan R. Tallent, Malachi Schram, Mahantesh Halappanavar, Kevin J. Barker:
Optimizing Distributed Data-Intensive Workflows. CLUSTER 2018: 279-289 - [i1]Alok Singh, Eric G. Stephan, Malachi Schram, Ilkay Altintas:
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows. CoRR abs/1804.06062 (2018) - 2017
- [c4]Alok Singh, Eric G. Stephan, Malachi Schram, Ilkay Altintas:
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows. eScience 2017: 586-591 - [c3]Ryan D. Friese, Mahantesh Halappanavar, Arun V. Sathanur, Malachi Schram, Darren J. Kerbyson, Luis de la Torre:
Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties. JSSPP 2017: 103-121 - 2016
- [c2]Alok Singh, Eric G. Stephan, Todd Elsethagen, Matt MacDuff, Bibi Raju, Malachi Schram, Kerstin Kleese van Dam, Darren J. Kerbyson, Ilkay Altintas:
Leveraging large sensor streams for robust cloud control. IEEE BigData 2016: 2115-2120 - 2015
- [c1]Mahantesh Halappanavar, Malachi Schram, Luis de la Torre, Kevin J. Barker, Nathan R. Tallent, Darren J. Kerbyson:
Towards efficient scheduling of data intensive high energy physics workflows. WORKS@SC 2015: 3:1-3:9
Coauthor Index
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last updated on 2024-12-22 19:56 CET by the dblp team
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