EDS Publications

  1. A. Levy, R. Raghu, J. R. Feathers, M. Grzadkowski, F. Poitevin, J. D. Johnston, F. Vallese, O.B. Clarke, G. Wetzstein and E. D. Zhong, “CryoDRGN-AI: Neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets”, Nature Methods (2025). doi: 10.1038/s41592-025-02720-4

  2. J. Shenoy, A. Levy, K. Ayyer, F. Poitevin and G. Wetzstein, "Scalable 3D reconstruction for X-ray single particle imaging with online machine learning", Nat. Commun. 16, (2025) doi: 10.1038/s41467-025-62226-7

  3. Z. Chen, C. Wang, M. Gao, C. H. Yoon, J. B. Thayer and J. J. Turner, "Augmenting X-ray single-particle imaging reconstruction with self-supervised machine learning", Newton 100110 (2025) doi: 10.1016/j.newton.2025.100110

  4. Y. Ni, Z. Chen, A. N. Petsch, E. Xu, C. Peng, A. I. Kolesnikov, S. Chowdhury, A. Bansil, J. B. Thayer and J. J. Turner, "Physics-Guided Dual Implicit Neural Representations for Source Separation", arXiv preprint,  (2025) arXiv: 2507.05249

  5. A. Levy, E. R. Chan, S. Fridovich-Keil, F. Poitevin, E. D. Zhong and G. Wetzstein, “Solving Inverse Problems in Protein Space Using Diffusion-Based Priors”, arXiv preprint arXiv:2406.04239, (2024) doi: 10.48550/arXiv2406.04239

  6. A. Peck, T. J. Lane and F. Poitevin, “Modeling diffuse scattering with simple, physically interpretable models”, In Methods in enzymology, 2023

  7. S. R. Chitturi, N. G. Burdet, Y. Nashed, D. Ratner, A. Mishra, T. J. Lane, M. Seaberg, V. Esposito, C. H. Yoon, M. Dunne and J. J. Turner, "A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis", Struct. Dyn. 9, 054302 (2022) doi: 10.1063/4.0000161

  8. K. M. Dalton, J. B. Greisman and D. R. Hekstra, "A unifying Bayesian framework for merging X-ray diffraction data", Nat Commun 13, 7764 (2022). https://doi.org/10.1038/s41467-022-35280-8

  9. A. Levy*, F. Poitevin*, J. Martel*, Y. Nashed, A. Peck, N. Miolane, D. Ratner, M. Dunne and G. Wetzstein, "CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images", European Conference on Computer Vision (ECCV) 2022

  10. Y.Nashed, A. Peck, J. Martel, A. Levy, B. Koo, G. Wetzstein, N. Miolane, D. Ratner and F. Poitevin, “Heterogeneous reconstruction of deformable atomic models in Cryo-EM”, In Machine Learning for Structural Biology workshop, (2022)

  11. A. Levy, G. Wetzstein, J. Martel, F. Poitevin and E. D. Zhong, “Amortized Inference for Heterogeneous Reconstruction in Cryo-EM”, Proc. NeurIPS, (2022)

  12. A. Ecoffet, G. Woollard, A. Kushner, F. Poitevin and K. D. Duc, "Application of transport-based metric for continuous interpolation between cryo-EM density maps[J]", AIMS Mathematics, 7(1): 986-999, (2022) doi: 10.3934/math.2022059 

  13. C. H. Yoon, "Psocake: GUI for Making Data Analysis a Piece of Cake", in Handbook on Big Data and Machine Learning in the Physical Sciences, World Scientific, World Scientific (2020)

  1. Z. Chen, A. N. Petsch, A. J. Israelski, R. Plumley, L. Shen, C. Wang, C. Peng, et al., "An Agentic Artificially Intelligent X-ray Scientist”, Research Square preprint, (2025)

  2. Z. Chen, A. N. Petsch, Z. Ji, S. R. Chitturi, C. Peng, C. Jia, A. I. Kolesnikov, J. B. Thayer and J. J. Turner, "Implicit neural representations for experimental steering of advanced experiments", Cell Reports Physical Science 6, no. 1, (2025)

  3. “Towards Closing the Autonomous Loop at Multiple Facilities: Developing Web-based User Interfaces and Data Infrastructure for Autonomous Experiments and Machine Learning Workflows”, Contributed talk at the XIX International Small Angle Scattering Conference (SAS 2024), Taipei, Taiwan

  4. C. Fernando, H. Marcello, J. Wlodek, J. Sinsheimer, D. Olds, S. I. Campbell and P. M. Maffettone, “Robotic Integration for End-Stations at Scientific User Facilities”, Digital Discovery 4, no. 4: 1083–91. (2025) doi: 10.1039/d5dd00036j

  5. Z. Chen, C. Peng, A. N. Petsch, S. R. Chitturi, A. Okullo, S. Chowdhury, C. H. Yoon and J. J. Turner, "Bayesian experimental design and parameter estimation for ultrafast spin dynamics", Machine Learning: Science and Technology 4, no. 4: 045056, (2023)

  1. J. P. Blaschke, R. Bolotovsky, A. S. Brewster, J. Donatelli, A. DuJardin, W. Feng, et al., "ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science", Front. High Perform. Comput. 2, (2024) doi: 10.3389/fhpcp.2024.1414569

  2. J. P. Blaschke, A. S. Brewster, D. W. Paley, D. Mendez, A. Bhowmick, N. K. Sauter, et al., “Real-time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis”, in Concurrency and Computation: Practice and Experience, (2024)

  3. J. P. Blaschke, F. Wittwer, B. Enders and D. Bard, "How a lightsource uses a supercomputer for live interactive analysis of large data sets", Synchrotron Radiat. News 36, 10–16, (2023) doi: 10.1080/08940886.2023.2245700

  4. H. Y. Chang, E. Slaughter, S. Mirchandaney, J. Donatelli and C. H. Yoon, "Scaling and acceleration of three-dimensional structure determination for single-particle imaging experiments with SpiniFEL", arXiv [Preprint], (2021) arXiv: 2109.05339

  5. J. Donatelli, J. A. Sethian and P. H. Zwart, "Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase", Proc. Nat. Acad. Sci. 114, 7222–7227, (2017) doi: 10.1073/pnas.1708217114

  1. C. Wang, V. Mariani, F. Poitevin, M. Avaylon, and J. B. Thayer, “End-to-end deep learning pipeline for real-time bragg peak segmentation: From training to large-scale deployment”, Front. High Perform. Comput., vol. 3, 2025, Art. no. 1536471, doi: 10.3389/fhpcp.2025.1536471

  2. J. Liu, J. Tian, S. Wu, S. Di, B. Zhang, R. Underwood, Y. Huang, J. Huang, K. Zhao, G. Li, D. Tao, Z. Chen, and F. Cappello, "CuSZ-i: High-Ratio Scientific Lossy Compression on GPUs with Optimized Multi-Level Interpolation", In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '24). IEEE Press, Article 13, 1–15, doi: 10.1109/SC41406.2024.00019

  3. J. Winnicki, F. Poitevin, H. Li, and E. Darve, “Matrix Sketching for Online Analysis of LCLS Imaging Datasets”, In SuperComputing (2024)

  4. C. Wang, E. Florin, H. Chang, J. B. Thayer, and C. H. Yoon, “SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples”, (2023) doi: 10.48550/arXiv.2302.06895

  5. R. Herbst, R. Coffee, N. Fronk, K. Kim, K. Kim, L. Ruckman, and J. J. Russell, “Implementation of a framework for deploying AI inference engines in FPGAs”, Smoky Mountains Computational Sciences and Engineering Conference 2022, Springer

  1. . Reid, G. L. Dakovski, M. H. Seaberg, F. O'Dowd, S. A. Montoya, H. Chen, A. Okullo, S. Mardanya, S. D. Kevan, P. Fischer, E. E. Fullerton, S. K. Sinha, W. Colocho, A. Lutman, F.-J. Decker, S. Roy, J. Fujioka, Y. Tokura, M. P. Minitti, J. A. Johnson, M. Hoffmann, M. E. Amoo, A. Feiguin, C. Yoon, J. Thayer, Y. Nashed, C. Jia, A. Bansil, S. Chowdhury, A. M. Lindenberg, M. Dunne, E. Blackburn and J. J. Turner, "On ultrafast x-ray scattering methods for magnetism", Adv. Phys. X 9, 1 (2024) doi: 10.1080/23746149.2024.2423935

  2. J. Thayer, Z. Chen, R. Claus, D. Damiani, C. Ford, M. Dubrovin, V. Elmir, W. Kroeger, X. Li, S. Marchesini, V. Mariani, R. Melcchiori, S. Nelson, A. Peck, A. Perazzo, F. Poitevin, C. P. O'Grady, J. Otero, O. Quijano, M. Shankar, M. Uervirojnangkoorn, R. Veraldi, M. Weaver, C. Weninger, S. Yamajala, C. Wang and C. H. Yoon, "Massive Scale Data Analytics at LCLS-II", Eur. Phys. J. Web Conf. 295, 13002 (2024) doi: 10.1051/epjconf/202429513002LCLS-II07/18/23

  3. RH. Chen, S. R. Chitturi, R. Plumley, L. Shen, N. C. Drucker, N. Burdet, C. Peng, S. Mardanya, D. Ratner, A. Mishra, C. H. Yoon, S. Song, M. Chollet, G. Fabbris, M. Dunne, S. Nelson, M. Li, A. Lindenberg, C. Jia, Y. Nashed, A. Bansil, S. Chowdhury, A. E. Feiguin, J. J. Turner and J. B. Thayer, "Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities", 2022 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP), Dallas, TX, USA, pp. 1-9, (2022) doi: 10.1109/XLOOP56614.2022.00006XCS11/18/19

  4. J. B. Thayer, D. Damiani, M. Dubrovin, C. Ford, W. Kroeger, C. O'Grady, A. Perazzo, M. Shankar, M. Weaver, C. Weninger, S. Yamajala and S. Zohar, "Data Processing at the Linac Coherent Light Source", 2019 IEEE/ACM 1st Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP), Denver, CO, (2019) doi: 10.1109/XLOOP49562.2019.0001109/22/18

  1. D. Rogers, V. Mariani, C. Wang, R. Coffee, W. Kroeger, M. Shankar, H. T. Schwander, T. Beck, F. Poitevin and J. B. Thayer, “The LCLStream Ecosystem for Multi-Institutional Dataset Exploration”, doi: 10.48550/arXiv.2510.04012

  2. C. Wang, V. Mariani, F. Poitevin, M. Avaylon and J. B. Thayer, “End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment”, Frontiers in High Performance Computing 3, (2025) doi: 10.3389/fhpcp.2025.1536471

  1. C. Wang, V. Mariani, F. Poitevin, M. Avaylon and J. B. Thayer, “End-to-end deep learning pipeline for real-time bragg peak segmentation: From training to large-scale deployment”, Front. High Perform. Comput., vol. 3, Art. no. 1536471, (2025) doi: 10.3389/fhpcp.2025.1536471

  2. A. Dave, C. Wang, J. Russell, R. Herbst and J. B. Thayer, "FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging", Front. High Perform. Comput. 3, (2025) doi: 10.3389/fhpcp.2025.1520151MFX05/16/25

  3. W. Zheng, J.-S. Park, P. Kenesei, A. Ali, Z. Liu, I. Foster, N. Schwarz, R. Kettimuthu, A. Miceli and H. Sharma, “Rapid detection of rare events from in situ X-ray diffraction data using machine learning”, J. Appl. Cryst., vol. 57, no. 4 , (2024) doi: 10.1107/S160057672400517X

  4. R. Herbst, R. Coffee, N. Fronk, K. Kim, K. Kim, L. Ruckman and J. J. Russell, “Implementation of a framework for deploying AI inference engines in FPGAs”, in Accelerating Science and Engineering Discoveries through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, K. Doug, G. Al, S. Pophale, H. Liu, S. Parete-Koon, Eds. Cham, Switzerland: Springer Nature, pp. 120–134, (2022) doi: 10.1007/978-3-031-23606-8_8

  5. “SLAC NeuralNet Library Source”, GitHub, 2023. [Online]. Available: https://github.com/slaclab/snl/releases/tag/v0.2.0

  6. N. Layad, Z. Liu and R. Coffee, “Open source implementation of the CookieNetAE model”, GitHub. [Online]. Available: https://github.com/AISDC/CookieNetAE

  7. C. Wang, P.-N. Li, J. Thayer and C. H. Yoon, “PeakNet: An Autonomous Bragg Peak Finder with Deep Neural Networks”, arXiv: 2303.15301 [physics.comp-ph], Mar. 2023. [Online]. Available: http://arxiv.org/abs/2303.15301

  8. C. Wang, E. Florin, H.-Y. Chang, J. Thayer and C. H. Yoon, “SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples”, IUCrJ, vol. 10, pp. 568–578, (2023) doi: 10.1107/S2052252523006115

  9. C. Benmore, T. Bicer, M. K. Y. Chan, Z. Di, D. Gürsoy, I. Hwang, N. Kuklev, D. Lin, Z. Liu, I. Lobach, Z. Qiao, L. Rebuffi, H. Sharma, X. Shi, C. Sun, Y. Yao, T. Zhou, A. Sandy, A. Miceli, Y. Sun, N. Schwarz and M. J. Cherukara, “Advancing AI/ML at the advanced photon source”, Synchrotron Radiat. News, vol. 35, pp. 28–35, (2022) doi: 10.1080/08940886.2022.2112500

  10. H. Sharma, J.-S. Park, P. Kenesei, J. Almer, Z. Liu and A. Miceli, “Speeding up diffraction analysis using machine learning”, in Acta Crystallogr. A: Found. Adv., vol. 78, pp. A141–A141, (2022) doi: 10.1107/S2053273322098588

  11. Z. Liu, H. Sharma, J.-S. Park, P. Kenesei, A. Miceli, J. Almer, R. Kettimuthu and I. Foster, “BraggNN: fast X-ray Bragg peak analysis using deep learning”, IUCrJ, vol. 9, no. 1, (2022) doi: 10.1107/S2052252521011258

  12. A. Ali, H. Sharma, R. Kettimuthu, P. Kenesei, D. Trujillo, A. Miceli, I. Foster, R. Coffee, J. B. Thayer and Z. Liu, “fairDMS: Rapid model training by data and model reuse”, in 2022 IEEE Int. Conf. Cluster Comput. (CLUSTER), Los Alamitos, CA, USA, pp. 394–405, (2022) doi: 10.1109/CLUSTER51413.2022.00050

  13. M. Levental, A. Khan, R. Chard, K. Yoshi, K. Chard and I. Foster, “OpenHLS: High-level synthesis for low-latency deep neural networks for experimental science”, arXiv:2302.06751 [cs.AR], [Online], (2023) Available: https://doi.org/10.48550/ARXIV.2302.06751

  14. Z. Liu, A. Ali, P. Kenesei, A. Miceli, H. Sharma, N. Schwarz, D. Trujillo, H. Yoo, R. Coffee, N. Layad, J. Thayer, R. Herbst, C. Yoon and I. Foster, “Bridging data center AI systems with edge computing for actionable information retrieval”, in 3rd Annu. Workshop Extreme-scale Experiment-in-the-Loop Comput. (XLOOP), pp. 15-23 (2021)

  1. S. Strempfer, T. Zhou, K. Yoshii, M. Hammer, A. Babu, D. Bycul, J. Weizeorick, M J. Cherukara and A. Miceli, "A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging", Journals of Instrumentation, 17 P10042, (2022) doi: 10.1088/1748-0221/17/10/P10042

  2. M. P. Hammer, A. Gupta, H. Shi, S. Strempfer, L. Rota, P. King, J. Weizeorick, K. Yoshii, T. Zhou, A. Pena-Perez, B. Markovic, D. Doering, J. Thayer, A. Dragone and A. Miceli, "Design of the digital readout of the prototype SparkPix-RT ASIC with variable data velocity", IEEE Nuclear Science Symposium, (NSS MIC RTSD), (2023) doi: 10.1109/NSSMICRTSD49126.2023.10338327

  3. A. Gupta, M. P. Hammer, H. Shi, S. Strempfer, L. Rota, P. King, J. Weizeorick, K. Yoshii, T. Zhou, A. Pena-Perez, B. Markovic, D. Doering, J. Thayer, A. Dragone and A. Miceli, "Design of the digital readout of the prototype SparkPix-RT ASIC with variable data velocity", IEEE Nuclear Science Symposium, NSS MIC RTSD, (2023)

  4. A. Gupta, D. Doering, M. P. Hammer, H. Shi, J. Weizeorick, S. Strempfer, S. M. Gnanasekaran, P. King, A. P. Perez, H. Kim, L. Rota, L. Ruckman, K. Yoshii, B. Reese, B. Markovic, T. Zhou, A. Dragone and A. Miceli, "SparkPix-RT2: The design of a 192× 168 pixel detector ASIC with on-chip data processing for ultra-fast photon science", 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), doi: 10.1109/NSS/MIC/RTSD57108.2024.10657530

  5. S. Strempfer, D. Doering, M. Hammer, H. Shi, J. Weizeorick, S. M. Gnanasekaran, A. Gupta, P. King, H. Kim, A. Pena-Perez, L.Rota, L. Ruckman, B. Reese, B. Markovic, A. Dragone and A. Miceli, "Characterization of SparkPix-RT1: A 48x48 charge-integrating X-ray pixel detector with on-chip compression", 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), doi: 10.1109/NSS/MIC/RTSD57108.2024.10656486

  1. D. Li, B. K. Alpert, D. T. Becker, D. A. Bennett, G. A. Carini, H.-M. Cho, W. B. Doriese, J. E. Dusatko, J. W. Fowler, J. C. Frisch, J. D. Gard, S. Guillet, G. C. Hilton, M. R. Holmes, K. D. Irwin, V. Kotsubo, S.-J. Lee, J. A. B. Mates, K. M. Morgan, K. Nakahara, C. G. Pappas, C. D. Reintsema, D. R. Schmidt, S. R. Smith, D. S. Swetz, J. B. Thayer, C. J. Titus, J. N. Ullom, L. R. Vale, D. D. V. Winkle, A. Wessels and L. Zhang, "TES X-ray Spectrometer at SLAC LCLS-II", J. Low Temp. Phys. (2018) doi: 10.1007/s10909-018-2053-6LCLS-II08/17/18

  2. G. Blaj, C. J. Kenney, A. Dragone, G. Carini, S. Herrmann, P. Hart, A. Tomada, J. Koglin, G. Haller, S. Boutet, M. Messerschmidt, G. Williams, M. Chollet, G. Dakovski, S. Nelson, J. Pines, S. Song and J. Thayer, "Optimal Pulse Processing, Pile-up Decomposition and Applications of Silicon Drift Detectors at LCLS", IEEE Transactions on Nuclear Science PP, (2017) doi: 10.1109/TNS.2017.2762281CXI

  3. J. Thayer, D. Damiani, C. Ford, M. Dubrovin, I. Gaponenko, C. P. OGrady, W. Kroeger, J. Pines, T. J. Lane, A. Salnikov, D. Schneider, T. Tookey, M. Weaver, C. H. Yoon and A. Perazzo, "Data systems for the Linac coherent light source", Adv Struct Chem Imag 3:3, (2017) doi: 10.1186/s40679-016-0037-7GENERAL LCLS07/27/16

  4. J. Thayer, D. Damiani, C. Ford, I. Gaponenko, W. Kroeger, C. O'Grady, J. Pines, T. Tookey, M. Weaver and A. Perazzo, "Data systems for the Linac Coherent Light Source", J. Appl. Crystallogr. 49, 1363 (2016) doi: 10.1107/S160057671601105507/01/16LCLS

  5. G. A. Carini, R. Alonso-Mori, G. Blaj, P. Caragiulo, M. Chollet, D. Damiani, D. Dragone, Y. Feng, G. Haller, P. Hart, J. Hasi, R. Herbst, S. Herrmann, C. Kenney, H. Lemke, L. Manger, B. Markovic, A. Mehta, S. Nelson, K. Nishimura, S. Osier, J. Pines, B. Reese, A. Robert, J. Segal, M. Sikorski, S. Song, J. Thayer, A. Tomada, M. Weaver and D. Zhu, "ePix100 Camera: Use and Applications at LCLS", AIP Conf. Proc. 1741, 040008 (2016) doi: 10.1063/1.4952880

  6. G. Blaj, C. J. Kenney, S. Boutet, G. Carini, M. Chollet, G. Dakovski, G. Haller, P. Hart, S. Herrmann, J. Koglin, M. Messerschmidt, S. Nelson, J. Pines, S. Song, J. Thayer, A. Tomada and G. Williams, "Performance of silicon drift detectors at LCLS", IEEE Symposium on Nuclear Science (NSS/MIC), doi: 10.1109/NSSMIC.2016.806982701/01/14LCLS

  7. Herrmann, P. Hart, A. Dragone, D. Freytag, R. Herbst, J. Pines, M. Weaver, G. A. Carini, J. B. Thayer, O. Shawn, C. J. Kenney and G. Haller, "CSPAD Upgrades and CSPAD V1.5 at LCLS", J. Phys. Conf. Ser. 493, 012013 (2014) doi: 10.1088/1742-6596/493/1/012013

  8. G. A. Carini, S. Boutet, M. Chollet, A. Dragone, G. Haller, P. A. Hart, S. C. Herrmann, C. J. Kenney, J. Koglin, M. Messerschmidt, S. Nelson, J. Pines, A. Robert, S. Song, J. B. Thayer, G. J. Williams and D. Zhu, "Experience with the CSPAD during Dedicated Detector Runs at LCLS", J. Phys. Conf. Ser. 493, 012011 (2014) doi: 10.1088/1742-6596/493/1/012011

  9. G. A. Carini, S. Boutet, M. Chollet, A. Dragone, G. Haller, P. A. Hart, S. C. Herrmann, C. J. Kenney, J. Koglin, H. T. Lemke, M. Messerschmidt, S. Nelson, J. Pines, A. Robert, S. Song, J. B. Thayer, G. J. Williams and D. Zhu, "Measurements at synchrotrons and FELs: Some differences observed with the CSPAD", IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), doi: 10.1109/NSSMIC.2013.6829694

Invited Talks

SpeakerEventLocationDateTopic

Dalton, Kevin

ACA Annual Meeting 2024

Denver, CO

July 2024

Scaling Scaling: Variational Inference for X-ray Diffraction Data

Diffraction Methods in Structural Biology

Berlin, Germany

July 2024

Scaling Scaling: Variational Inference for X-ray Diffraction Data

Pittsburgh Diffraction Conference

Ithaca, NY, USA

September 2024

Scaling Scaling: Variational Inference for X-ray Diffraction Data

Mariani, Valerio

Smoky Mountains Computational Sciences & Engineering Conference 2024 (ORNL-SMC2024)

Knoxville, TN

September 2024

 

SSRL/LCLS User Meeting 2025

Menlo Park, CA

September 2025

SSRL/LCLS User Meeting Overview 2025 - Valerio

Poitevin, Frederic

ACA 74th Annual Meeting

Denver, CO, USA

July 2024

Current and Future Strategies for End-to-End Data Analysis at LCLS

7th Ringberg Workshop on Structural Biology

Bavaria, Germany

February 2024

Current and Future Strategies for End-to-End Online Analysis at LCLS

SciX 2023

Sparks, NV, USA

October 2023

Publish and/or Flourish - a National Lab perspective

72nd ACA Annual Meeting 2022

 

July 2022

Amortized Inference for Ab Initio Reconstruction of 3D Molecular Volumes

Monterey Data Conference 2025

Monterey, CA

August 2025

LCLS Data Deluge – recent stories and future plans

Thayer, Jana

AIRA Workshop 2021

Virtual

July 2021

Artificial Intelligence & Robotics for Modern Accelerator-Based Light Sources (Plenary Talk)

CHEP 2023

Norfolk, VA, USA

May 2023

Massive Scale Data Analytics at LCLS-II

IUCr 2023

Melbourne, Australia

August 2023

Data Analytics at the Linac Coherent Light Source

DOE IFDEPS 2024

Port Jefferson, NY, USA

March 2024

Future Directions in Detector and Data Systems Integration through the lens of LCLS

SRI 2024

Hamburg, Germany

August 2024

How I Learned to Stop Worrying and Love the Data Deluge (Plenary Talk)

Supercomputing 2024 (SC24)

Atlanta, GA, USA

November 2024

How I Learned to Stop Worrying and Love the Data Deluge (Invited Talk)

Fast ML for Science 2023

San Francisco, CA, USA

November 2023

Real-time ML at the Linac Coherent Light Source

PhotonDiag 2025

Liverpool, UK

September 2025

From Data Deluge to Discovery Engine:  How AI-Augmented Instruments Revolutionize Science (Invited Talk)

Productive, Performant Software for Large Scale Scientific Data Analysis Workshop

Menlo Park, CA

October 2025

Turning Petabytes into Physics:  The Case for Scalable, User-Centric Software for Next-Generation Science at LCLS (Plenary Talk)

Yoon, Chuck

SRI 2021

Virtual

March 2022

Data Processing at the Linac Coherent Light Source