Towards efficient scheduling of data intensive high energy physics workflows. - Mahantesh Halappanavar; Malachi Schram; Luis de la Torre 001; Kevin J. Barker; Nathan R. Tallent; Darren J. Kerbyson (WORKS@SC, 2015)

Leveraging large sensor streams for robust cloud control. - Alok Singh 004; Eric G. Stephan; Todd Elsethagen; Matt MacDuff; Bibi Raju; Malachi Schram; Kerstin Kleese van Dam; Darren J. Kerbyson; Ilkay Altintas (IEEE BigData, 2016)

Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows. - Alok Singh 004; Eric G. Stephan; Malachi Schram; Ilkay Altintas (eScience, 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 001 (JSSPP, 2017)

Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows. - Alok Singh 004; Ilkay Altintas; Malachi Schram; Nathan R. Tallent (IEEE BigData, 2018)

Optimizing Distributed Data-Intensive Workflows. - Ryan D. Friese; Nathan R. Tallent; Malachi Schram; Mahantesh Halappanavar; Kevin J. Barker (CLUSTER, 2018)

TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows. - Joshua Suetterlein; Ryan D. Friese; Nathan R. Tallent; Malachi Schram (IEEE BigData, 2019)

Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster. - D. Kafkes; Malachi Schram (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)

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 W. P. Poon; et al. (CoRR, 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 001; Sutanay Choudhury; Bert J. Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; et al. (Int. J. High Perform. Comput. Appl., 2021)

Identifying build orientation of 3D-printed materials using convolutional neural networks. - Jan Strube 001; Malachi Schram; Sabiha Rustam; Zachary Kennedy; Tamás Varga (Stat. Anal. Data Min., 2021)

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders. - Majdi I. Radaideh; Chris Pappas; Jared Walden; Dan Lu 001; Lasitha Vidyaratne; Thomas Britton; Kishansingh Rajput; Malachi Schram; Sarah Cousineau (Digit. Signal Process., 2022)

AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. - Yu Wang 167; Ján Drgona; Jiaxin Zhang 005; Karthik Somayaji Nanjangud Suryanarayana; Malachi Schram; Frank Liu 001; Peng Li 001 (AAAI, 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 001; Pradeep Ramuhalli; Sarah Cousineau (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; et al. (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)

Uncertainty Aware Deep Learning for Particle Accelerators. - Kishansingh Rajput; Malachi Schram; Karthik Somayaji N. S. (CoRR, 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 015; Malachi Schram; Evgenia Smirni (CoRR, 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 015; Malachi Schram; Evgenia Smirni (Zenodo, 2023)

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach. - Yu Wang 167; Yuxuan Yin; Karthik Somayaji N. S.; Ján Drgona; Malachi Schram; Mahantesh Halappanavar; Frank Liu 001; Peng Li 001 (AAAI, 2024)

Location Invariant Flood Prediction using Fourier Neural Operator. - Chetan Kumar; Diana McSpadden; Steven Goldenberg; Malachi Schram; Heather Richter; Yidi Wang; Binata Roy; Jonathan L. Goodall (ICMLA, 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)

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. - Steven Goldenberg; Malachi Schram; Kishansingh Rajput; Thomas Britton; Chris Pappas; Dan Lu 001; Jared Walden; Majdi I. Radaideh; Sarah Cousineau; Sudarshan Harave (Mach. Learn. Sci. Technol., 2024)

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. - Karthik Somayaji N. S.; Yu Wang 167; Malachi Schram; Ján Drgona; Mahantesh M. Halappanavar; Frank Liu 001; Peng Li 001 (Trans. Mach. Learn. Res., 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., 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; et al. (Comput. Softw. Big Sci., 2024)

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver. - Daniel Lersch; Malachi Schram; Zhenyu Dai; Kishansingh Rajput; Nobuo Sato; Xingfu Wu; J. Taylor Childers; Steven Goldenberg (Mach. Learn. Sci. Technol., 2025)

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators. - Kishansingh Rajput; Malachi Schram; Auralee Edelen; Jonathan Colen; Armen Kasparian; Ryan Roussel; Adam Carpenter; He Zhang; Jay Benesch (Mach. Learn. Sci. Technol., 2025)

Establishing Machine Learning Operations for Continual Learning in Computing Clusters: A Framework for Monitoring and Optimizing Cluster Behavior. - Diana McSpadden; Mark Jones; Ahmed Hossam Mohammed; Bryan Hess; Malachi Schram (IEEE Softw., 2025)

Outlook Towards Deployable Continual Learning for Particle Accelerators. - Kishansingh Rajput; Sen Lin; Auralee Edelen; Willem Blokland; Malachi Schram (CoRR, 2025)

Geometric GNNs for Charged Particle Tracking at GlueX. - Ahmed Hossam Mohammed; Kishansingh Rajput; Simon Taylor; Denis Furletov; Sergey Furletov; Malachi Schram (CoRR, 2025)