Malachi Schram

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 27

Top Topics

Machine Learning

High Energy Physics

Scientific Workflows

Top Venues

Publications

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)
Daniel Lersch, Malachi Schram, Zhenyu Dai, Kishansingh Rajput, Xingfu Wu, N. Sato, J. Taylor Childers
SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver. CoRR (2024)
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) (2024)
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, M. 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, D. Shih, J. Singh, R. Singh, A. Siodmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, A.-N. 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) (2024)
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)
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 (2023)
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 (2023)
C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, A. 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, Y. Gao, J. 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, M. 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, M. L. Purschke, K. Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, N. Sato, T. Satogata, German Sborlini, Malachi Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, A.-N. Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita
Artificial Intelligence for the Electron Ion Collider (AI4EIC). CoRR (2023)
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)
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 (2023)
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 (2023)
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 (2023)
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 (2023)
N. S. Karthik Somayaji, Yu Wang, Malachi Schram, Ján Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. CoRR (2023)
Kishansingh Rajput, Malachi Schram, N. S. Karthik Somayaji
Uncertainty Aware Deep Learning for Particle Accelerators. CoRR (2023)
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 (2022)
Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator. CoRR (2021)
Francis J. Alexander, James 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 Ramakrishniah, 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) (2021)
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) (2021)
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 (2021)
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)
Ryan D. Friese, Nathan R. Tallent, Malachi Schram, Mahantesh Halappanavar, Kevin J. Barker
Optimizing Distributed Data-Intensive Workflows. CLUSTER (2018)
Alok Singh, Ilkay Altintas, Malachi Schram, Nathan R. Tallent
Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows. IEEE BigData (2018)
Alok Singh, Eric G. Stephan, Malachi Schram, Ilkay Altintas
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows. CoRR (2018)
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)
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)
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)
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)