Quantum Information Sciences (QIS)
• P. Jouzdani, C. W. Johnson, E. R. Mucciolo, I. Stetcu, Alternative approach to quantum imaginary time evolution, Physical Review A 106 (2022) 062435
  
  
• P. Jouzdani and S. Bringuier, Hybrid quantum-classical eigensolver without variation or parametric gates, Quantum Reports 3 (2021) 137-152
  
  
• P. Jouzdani, S. Bringuier, and M. Kostuk, A method of determining molecular excited-states using quantum computation, MRS Advances 6 (2021) 558–563
  
  
• S. Pelton, P. Jouzdani, Simultaneous indirect measurement of anticommuting operators with ancilla qubits, Cambridge Quantum Computing Workshop (2020)
  
  
• S. Bringuier, P. Jouzdani, Ab initio calculations in chemical and materials sciences using near-term quantum computation, MRS Spring conference (2021)

Machine Learning and Artificial Intelligence (ML/AI)
• Olofsson, K., Akçay, C., & Sammuli, B. (2022). Database-wide hazard modelling of the onset of DIII-D tearing modes with field features. Journal of Plasma Physics, 88(5), 895880503. doi:10.1017/S0022377822001003
  
  
• D. Orozco, B. Sammuli, J. Barr, W. Wehner and D. Humphreys, "Neural Network-Based Confinement Mode Prediction for Real-Time Disruption Avoidance," in IEEE Transactions on Plasma Science, 2022, doi: 10.1109/TPS.2022.3198596.
  
  
• Anand, H. & Sammuli, B. & Olofsson, K Erik J & Humphreys, D.A.. (2022). Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Networks on the DIII-D Tokamak. IEEE Transactions on Plasma Science. 1-5. 10.1109/TPS.2022.3181548.
  
  
• Sammuli, B.S. & Barr, Jayson & Humphreys, D.A.. (2021). Avoidance of vertical displacement events in DIII-D using a neural network growth rate estimator. Fusion Engineering and Design. 169. 112492. 10.1016/j.fusengdes.2021.112492.

Leadership High Performance Computing (HPC)
• S. Smith, E. Belli, O. Meneghini, R. Budiardja, D. Schissel, J. Candy, T. Neiser and A. Eubanks, A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management, SMC 2022, Communications in Computer and Information Science (Springer) 1690 (2023)
  
  
• E. Belli, J. Candy and I. Sfiligoi, Spectral transition of multiscale turbulence in the tokamak pedestal, Plasma Phys. Control. Fusion 65 (2023) 024001
  
  
• E.A. Belli and J. Candy, Asymmetry between deuterium and tritium turbulent particle flows, Phys. Plasmas 28 (2021) 062301
  
  
• E.A. Belli, J. Candy, I. Sfiligoi, and F. Würthwein, Comparing Single-Node and Multi-Node Performance of an Important Fusion HPC Code Benchmark, PEARC '22, Association for Computing Machinery (2022)
  
  
• W. Guttenfelder, R.J. Groebner, J.M. Canik, B.A. Grierson, E.A. Belli, and J. Candy, Testing predictions of electron scale turbulent pedestal transport in two DIII-D ELMy H-modes, Nucl. Fusion 61 (2021) 056005
  
  
• F.D. Halpern, Anti-symmetric representation of the extended magnetohydrodynamic equations, Phys. Plasmas 27 (2020) 042303
  
  
• F.D. Halpern, I. Sfiligoi, M. Kostuk, R. Stefan and R.E. Waltz. Simulations of plasmas and fluids using anti-symmetric models, J. Comput. Phys. 445 (2021) 110631
  
  
• N.T. Howard, P. Rodriguez-Fernandez, C. Holland, J.E. Rice, M. Greenwald, J. Candy and F. Sciortino, Gyrokinetic simulation of turbulence and transport in the SPARC tokamak, Physics of Plasmas 28 (2021) 072502
  
  
• N.T. Howard, C. Holland, T.L. Rhodes, J. Candy, P. Rodriguez-Fernandez, et al., The role of ion and electron-scale turbulence in setting heat and particle transport in the DIII-D ITER baseline scenario, Nuclear Fusion 61 (2021) 106002
  
  
• X. Jian, C. Holland, J. Candy, S. Ding, E. Belli, et al., Role of microtearing mode in DIII-D and future high-βp core plasmas, Physics of Plasmas 28 (2021) 042501
  
  
• P. Rodriguez-Fernandez, N. Howard and J. Candy, Nonlinear gyrokinetic predictions of SPARC burning plasma profiles enabled by surrogate modeling, Nucl. Fusion 62 (2022) 076036
  
  
• I. Sfiligoi, J. Candy and D. Subramanian, Fusion research using Azure A100 HPC instances, arXiv (2021)