1. Sequence-guided protein structure determination using graph convolutional and recurrent networks
    Po-Nan Li, Saulo H. P. de Oliveira, Soichi Wakatsuki, and Henry van den Bedem, arXiv 2007.06847 (2020) 
  2. Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
    L. Domine and K. Terao (DeepLearnPhysics collaboration), Phys. Rev. D 102 012005 (2020)  
  3. Black box optimization with Local Generative Surrogates
    S. Shirobokov, V. Belavin, M. Kagan, A. Ustyuzhanin, A. G. Baydin (In submission),, Presented at ICML 2020 Workshop on Real World Experimental Design (2020)
  4. Deep Sets for Flavour Tagging on the ATLAS Experiment
    N. Hartman, R. Teixeira De Lima, M. Kagan, on behalf of the ATLAS Collaboration,
    Proceedings of Connecting the Dots 2020, ATL-PHYS-PUB-2020-014 (2020)
  5. A Roadmap for HEP Software and Computing R&D for the 2020's
    J. Albrecht, et. al., Comput. Softw. Big. Sci. (2019) 3, 7 (2020)
  6. Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
    D.H. Koh and others (DeepLearnPhysics collaboration), arXiv 2007.03083 (2020)
  7. Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data
    F. Drielsma and others (DeepLearnPhysics collaboration), arXiv 2007.01335 (2020)
  8. Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
    L. Domine and others (DeepLearnPhysics collaboration), arXiv 2006.14745 (2020)
  9. MadMiner: Machine Learning-Based Inference for Particle Physics
    Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer, Comp. and Software for Big Sci. 4, 3 (2020)
  10. Improving inference with matrix elements and machine learning
    Johann Brehmer, Kyle Cranmer, Felix Kling, Int. J. of Modern Physics A 35, 15n16, 2041008 (2020)
  11. PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
    C. Adams, K. Terao, and T. Wongjirad (DeepLearnPhysics collaboration), arXiv 2006.01993 (2020)
  12. Storage ring nonlinear dynamics optimization with multi-objective multi-generation Gaussian process optimizer
    M. Song, X. Huang, L. Spentzouris, and Z. Zhang, Nucl. Instr. Meths. A 976, 164273 (2020)
  13. Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
    Jiang, Z., Li, J., Yang, Y. et al. Nat Commun 11, 2310, (2020)
  14. Temporal power reconstruction for an x-ray free-electron laser using convolutional neural networks
    X. Ren, A. Edelen, A. Lutman, G. Marcus, T. Maxwell, and D. Ratner, Phys. Rev. Accel. Beams 23, 040701, (2020) 
  15. Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems
    A.L. Edelen, N. Neveu, M. Frey, Y. Huber, C. Mayes, and A. Adelmann, Phys. Rev. Accel. Beams 23, 044601, (2020) 
  16. Reinforcement Learning for Adaptive Illumination with X-Rays
    J.R. Betterton, D. Ratner, S. Webb, M. Kochenderfer, IEEE International Conference on Robotics and Automation (ICRA), (2020)
  17. What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging
    T.J. Lane and D. Ratner, Optics Express, 28, 5898 (2020) 
  18. Bayesian optimization of a free-electron laser
    J. Duris, et al., Phys. Rev. Lett., 124, 124801 (2020)
  19. Mapping photocathode quantum efficiency with ghost imaging
    K. Kabra, S. Li, F. Cropp, T.J. Lane, P. Musumeci, and D. Ratner, Phys. Rev. AB, 22, 022803 (2020)
  20. Continual Learning Via Neural Pruning
    S. Golkar, M. Kagan, K. Cho, Presented at NeurIPS 2019 Neuro-AI workshop (2019)
  21. Pump-probe ghost imaging with SASE FELs
  22. D. Ratner, J. Cryan, T.J. Lane, S. Li, and G. Stupakov, Phys. Rev. X, 9, 011045 (2019) 
  23. Machine Learning at the Edge for Ultra High Rate Detectors
    A. C. Therrien, R. T. Herbst, O. E. Quijano, A. Gatton, R. Coffee, IEEE NSS-MIC, Manchester, UK, (2019) 
  24. Online tuning and light source control using a physics-informed Gaussian process
    Adi Hanuka, J. Duris, J. Shtalenkova, D. Kennedy, A. Edelen, D. Ratner, X. Huang, Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), Vancouver, Canada, (2019) 
  25. Machine Learning Models for Optimization and Control of X-ray Free Electron Lasers
    A.L. Edelen, N. Neveu, C. Emma, D. Ratner, C. Mayes, Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), Vancouver, Canada, (2019) 
  26. Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
    C. Adams et al., Phys. Rev. D, 99, 092001 (2019)
  27. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
    Rama K Vasudevan, Kamal Choudhary, Apurva Mehta, Ryan Smith, Gilad Kusne, Francesca Tavazza, Lukas Vlcek, Maxim Ziatdinov, Sergei V Kalinin, Jason Hattrick-Simpers, MRS Comm., (3), 821-838, (2019)
  28. Electron ghost imaging
    S. Li, F. Cropp, K. Kabra, T. J. Lane, G. Wetzstein, P. Musumeci, and D. Ratner, Phys. Rev. Lett., 121, 114801 (2018) 
  29. Machine learning at the energy and intensity frontiers of particle physics
    A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, and T. Wongjirad, Nature 560 41–48 (2018)
  30. Machine learning-based longitudinal phase space prediction of particle accelerators
    C. Emma, A. Edelen, M. J. Hogan, B. O’Shea, G. White, and V. Yakimenko, Phys. Rev. Accel. Beams 21, 112802, (2018) 
  31. Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution
    A. Scheinker, A. Edelen, D. Bohler, C. Emma, and A. Lutman, Phys. Rev. Lett. 121, 044801, (2018) 
  32. Accelerated Discovery of Metallic Glasses through Iteration of Machine Learning and High-throughput Experiments 
    F. Ren, L. Ward, T. Williams, K. J. Laws, C. Wolverton, J. Hattrick-Simpers, A. Mehta, Science Advances, Vol. 4, no. 4, eaaq1566 (2018)
  33. Learning to Pivot with Adversarial Networks 
    G. Louppe, M. Kagan, K. Cranmer Proceedings of NeurIPS 2017
  34. Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment
    ATLAS Collaboration (2017)
  35. Finding a Needle in the Haystack: Identification of Functionally Important Minority Phases in Operating Battery 
    K. Zhang, F. Ren, X. Wang, E. Hu, Y. Xu, X.-Q. Yang, H. Li, L. Chen, P. Pianetta, A. Mehta, X. Yu and Y. Liu, Nano Lett. 17, 7882 (2017) 
  36. Jet Images - Deep Learning Edition
    L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, A. Schwartzman, JHEP 07 069 (2016)
  37. Weighted KL divergence for adaptive GP set selection
    M. McIntire, D. Ratner, S. Ermon, UAI16 (2016) 
  38. Bayesian optimization of FEL performance at LCLS
    M. McIntire, T. Cope, S. Ermon, D. Ratner, IPAC16, Busan, Korea, 8-13 May, (2016) 
  39. Jet Images: Computer Vision Inspired Techniques for Jet Tagging
    J. Cogan, M. Kagan, E. Strauss, A. Schwartzman, JHEP 02 118 (2015)