General Lab Information

Data Science and Systems Integration

Publications

  1. Fernando, C. & Maffettone, P. (2025). Robotic integration for end-stations at scientific user facilities. Digital Discovery https://dx.doi.org/10.1039/d5dd00036j
  2. Morris, T. W., Rakitin, M., Du, Y., Fedurin, M., Giles, A. C., Leshchev, D., Li, W. H., Romasky, B., Stavitski, E., Walter, A. L., Moeller, P., Nash, B., & Islegen-Wojdyla, A. (2024). A general Bayesian algorithm for the autonomous alignment of beamlines. Journal of Synchrotron Radiation, 31(6), 1446-1456 https://dx.doi.org/10.1107/s1600577524008993
  3. Campbell, S. (2024). Facile Integration of Robots into Experimental Orchestration at Scientific User Facilities. 2024 IEEE International Conference on Robotics and Automation (ICRA) https://dx.doi.org/10.1109/ICRA57147.2024.10611706
  4. Cook, N. & Rakitin, M. (2023). Integrating Online Analysis With Experiments To Improve X-Ray Light Source Operations. https://dx.doi.org/10.18429/JACoW-ICALEPCS2023-TUSDSC02
  5. Rakitin, M., Campbell, S., Allan, D., Caswell, T., Gavrilov, D., Hanwell, M., & Wilkins, S. (2022). Next generation experimental data access at NSLS-II. Journal of Physics: Conference Series, 2380(1), 012100 https://dx.doi.org/10.1088/1742-6596/2380/1/012100
  6. Goel, H., Chubar, O., Wiegart, L., Fluerasu, A., Li, R., He, A., Rakitin, M., Moeller, P., & Nagler, R. (2022). Developments in SRW Code and Sirepo Framework Supporting Simulation of Time-Dependent Coherent X-ray Scattering Experiments. Journal of Physics: Conference Series, 2380(1), 012126 https://dx.doi.org/10.1088/1742-6596/2380/1/012126
  7. Nash, B., Abell, D., Nagler, R., Moeller, P., Keilman, M., Pogorelov, I., Goldring, N., Rakitin, M., Lynch, J., Giles, A., Walter, A., Maldonado, J., Morris, T., Bak, S., & Du, Y. (2022). Combining diagnostics, modeling, and control systems for automated alignment of the TES beamline. Journal of Physics: Conference Series, 2380(1), 012103 https://dx.doi.org/10.1088/1742-6596/2380/1/012103
  8. Maffettone, P. M., Campbell, S., Hanwell, M. D., Wilkins, S., & Olds, D. (2022). Delivering real-time multi-modal materials analysis with enterprise beamlines. Cell Reports Physical Science, 3(11), 101112 https://dx.doi.org/10.1016/j.xcrp.2022.101112
  9. Zhao, C., Chung, C., Jiang, S., Noack, M. M., Chen, J., Manandhar, K., Lynch, J., Zhong, H., Zhu, W., Maffettone, P., Olds, D., Fukuto, M., Takeuchi, I., Ghose, S., Caswell, T., Yager, K. G., & Chen-Wiegart, Y. K. (2022). Machine-learning for designing nanoarchitectured materials by dealloying. Communications Materials, 3(1), Article 86 https://dx.doi.org/10.1038/s43246-022-00303-w
  10. Morris, T., Rakitin, M., Giles, A., Lynch, J., Walter, A. L., Nash, B., Abell, D., Moeller, P., Pogorelov, I., & Goldring, N. (2022). On-the-fly optimization of synchrotron beamlines using machine learning. Proceedings Volume 12222, Optical System Alignment, Tolerancing, and Verification XIV https://dx.doi.org/10.1117/12.2644996
  11. Konstantinova, T., Maffettone, P. M., Ravel, B., Campbell, S. I., Barbour, A. M., & Olds, D. (2022). Machine learning enabling high-throughput and remote operations at large-scale user facilities. Digital Discovery, 1(4), 413-426 https://dx.doi.org/10.1039/d2dd00014h
  12. Olds, D., Allan, D. B., Caswell, T. A., Lynch, J., Maffettone, P. M., & Campbell, S. I. (2021). Optimizing High- Throughput Capabilities by Leveraging Reinforcement Learning Methods with the Bluesky Suite. 2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP) https://dx.doi.org/10.1109/xloop54565.2021.00011
  13. Konstantinova, T., Wiegart, L., Rakitin, M., DeGennaro, A. M., & Barbour, A. M. (2021). Noise reduction in X‑ray photon correlation spectroscopy with convolutional neural networks encoder-decoder models. Scientific Reports, 11, Article 14756 https://dx.doi.org/10.1038/s41598-021-93747-y
  14. Sinsheimer, J., Bouet, N., Ghose, S., Dooryhee, E., & Conley, R. (2021). Gaming the beamlines-employing reinforcement learning to maximize scientific outcomes at large-scale user facilities. Machine Learning: Science and Technology, 2(2), 025025 https://dx.doi.org/10.1088/2632-2153/abc9fc
  15. Campbell, S. I., Allan, D. B., Barbour, A. M., Olds, D., Rakitin, M. S., Smith, R., & Wilkins, S. B. (2020). Outlook for Artificial Intelligence and Machine Learning at the NSLS-II. Machine Learning: Science and Technology https://dx.doi.org/10.1088/2632-2153/abbd4e
  16. Rakitin, M. S., Giles, A., Swartz, K., Lynch, J., Moeller, P., Nagler, R., Allan, D., Caswell, T., Wiegart, L., Chubar, O., & Du, Y. (2020). Introduction of the Sirepo-Bluesky interface and its application to the optimization problems. Advances in Computational Methods for X-Ray Optics V, 11493 https://dx.doi.org/10.1117/12.2569000