Publications

Rajput, K., Lin, S., Schram, M., Sammuli, B. (2025). Uncertainty based online ensemble on non-stationary data for fusion science. NeurIPS 2025 Machine Learning for Physical Science Workshop.

Rajput, K., Schram, M., Blokland, W., Zhukov, A., Lin, S. (2025). Continual learning for particle accelerators. NeurIPS 2025 Machine Learning for Physical Science Workshop.

Britton, T., Jeske, T., Lawrence, D., Matsiuk, N., Rasool, R. (2025). Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring. EPJ Web of Conferences, Vol. 337, 01227.

Mohammed, A. H., Jones, M., McSpadden, D., Schram, M., Hess, B., Rajput, K. (2025). Decode the workload: Training deep learning models for efficient compute cluster representation. EPJ Web of Conferences, Volume 337, Article 01120.

Rajput, K., and others (2025). Towards continual machine learning for particle accelerators. IBIC 2025.

Mohammed, A. H., Rajput, K., Taylor, S., Furletov, D., Furletov, S., Schram, M. (2025). Geometric GNNs for Charged Particle Tracking at GlueX. In MLST.

Braga, K., Diefenthaler, M., Goldenberg, S., Lersch, D., Li, Y., Qiu, J.-W., Rajput, K., Ringer, F., Sato, N., Schram, M. (2025). Toward an event-level analysis of hadron structure using differential programming. Preprint.

Rajput, K., Lin, S., Edelen, A., Blokland, W., Schram, M. (2025). An Outlook Towards Deployable Continual Learning for Particle Accelerators. MLST.

Lersch, D., Schram, M., Dai, Z., Rajput, K., Sato, N., Wu, X., Childers, J. T., Goldenberg, S. (2025). SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver. In MLST.

Rajput, K., Schram, M., Edelen, A., Colen, J., Kasparian, A., Roussel, R., Carpenter, A., Zhang, H., Benesch, J. (2025). Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators. MLST.

Colen, J., Schram, M., Rajput, K., Kasparian, A. (2025). Explainable physics-based constraints on reinforcement learning for accelerator controls. Preprint.

Rajput, K., Schram, M., Sammuli, B., Lin, S. (2025). Uncertainty guided online ensemble for non-stationary data streams in fusion science. Under review at Engineering Applications of Artificial Intelligence.

Britton, T., Jeske, T., Lawrence, D., Rajput, K. (2024). Hydra: computer vision for data quality monitoring. JINST.

Goldenberg, S., Schram, M., Rajput, K., Britton, T., Pappas, C., Lu, D., Walden, J., Radaideh, M. I., Cousineau, S., Harave, S. (2024). Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. Machine Learning: Science and Technology, Volume 5, Number 4, Article 045009.

Fanelli, C., Giroux, J., McSpadden, D., Rajput, K., Suresh, K., Cisbani, E., Deconinck, W., Walter, E., Bressan, A., Diefenthaler, M., and others (2024). AI4EIC Hackathon: PID with the ePIC dRICH. EPJ Web of Conferences, Volume 295, Article 08004.

Rajput, K., Schram, M., Blokland, W., Alanazi, Y., Ramuhalli, P., Zhukov, A., Peters, C., Vilalta, R. (2024). Robust errant beam prognostics with conditional modeling for particle accelerators. MLST.

Allaire, C., Fanelli, C., Rajput, K. (2024). Artificial Intelligence for the Electron Ion Collider (AI4EIC). CSBS.

Rajput, K., Schram, M., Somayaji, K. (2023). Uncertainty aware deep learning for particle accelerators. arXiv Preprint, arXiv:2309.14502.

Alanazi, Y., Schram, M., Rajput, K., Goldenberg, S., Vidyaratne, L., Pappas, C., Radaideh, M. I., Lu, D., Ramuhalli, P., Cousineau, S. (2023). Multi-module-based CVAE to predict HVCM faults in the SNS accelerator. Machine Learning with Applications, Volume 13, Article 100484.

Schram, M., Rajput, K., K. S., NS, Li, P., John, J. S., Sharma, H. (2023). Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex. Physical Review Accelerators and Beams, Volume 26, Article 044602.

Rajput, K., Chen, G. (2022). Probabilistic Envelope based Visualization for Monitoring Drilling Well Data Logging. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022), Volume 3: IVAPP.