12 citations · 29 across the 14 of their papers we have counts for
15 papers
SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production
Armen Kasparian, Kishansingh Rajput, Malachi Schram +1
Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities. However, deploying and maintaining ML models i…
Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science
Kishansingh Rajput, Malachi Schram, Brian Sammuli +1
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distributi…
Toward an event-level analysis of hadron structure using differential programming
Kevin Braga, Markus Diefenthaler, Steven Goldenberg +7
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at…
Geometric GNNs for Charged Particle Tracking at GlueX
Ahmed Hossam Mohammed, Kishansingh Rajput, Simon Taylor +3
Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many…
Outlook Towards Deployable Continual Learning for Particle Accelerators
Kishansingh Rajput, Sen Lin, Auralee Edelen +2
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires…
Explainable physics-based constraints on reinforcement learning for accelerator controls
Jonathan Colen, Malachi Schram, Kishansingh Rajput +1
We present a reinforcement learning (RL) framework for controlling particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is…
Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Kishansingh Rajput, Malachi Schram, Auralee Edelen +6
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between t…
SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver
Daniel Lersch, Malachi Schram, Zhenyu Dai +4
Large scale, inverse problem solving deep learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse…
Evaluating DTW Measures via a Synthesis Framework for Time-Series Data
Kishansingh Rajput, Duong Binh Nguyen, Guoning Chen
Time-series data originate from various applications that describe specific observations or quantities of interest over time. Their analysis often involves the comparison across di…
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
Kishansingh Rajput, Malachi Schram, Willem Blokland +5
Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and…
Uncertainty Aware Deep Learning for Particle Accelerators
Kishansingh Rajput, Malachi Schram, Karthik Somayaji
Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccura…
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput +7
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulat…
Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex
Malachi Schram, Kishansingh Rajput, Karthik Somayaji Peng Li +2
Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks and Quantile Regression Models provide estimates to prediction uncertainties for data-driven deep…
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
Willem Blokland, Pradeep Ramuhalli, Charles Peters +5
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the da…