Research Interests

• Uncertainty quantification
• Scientific machine and deep learning
• Inertial confinement fusion
• Global climate modeling
• Climate extremes
• Western US climate

Ph.D. Physics, University of Sussex, 2014

MAS. Mathematics, University of Cambridge, 2009

MPhys. Physics, Lancaster University, 2008

See Google Scholar or Publons for most up-to-date publication list.

Gemma J. Anderson, Stephen C. Myers, and Nathan A. Simmons, "Emulation of seismic-phase travel times with machine learning", Accepted for publication in Geophysical Journal International (2023).

K. L. Baker, S. MacLaren, O. Jones, B. K. Spears, P. K. Patel, R. Nora, L. Divol, O. L. Landen, G. J. Anderson, et al., "Alpha heating of indirect-drive layered implosions on the National Ignition Facility", Phys. Rev. E 107, 015202 (2023).

K. L. Baker, O. Jones, C. Weber, D. Clark, P. K. Patel, C. A. Thomas, O. L. Landen, R. Nora, G. J. Anderson, et al., “Hydroscaling Indirect-Drive Implosions on the National Ignition Facility”, Physics of Plasmas 29, 062705 (2022).

Bogdan Kustowski, Jim A Gaffney, Brian K Spears, Gemma J Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J Thiagarajan, Michael K G Kruse and Ryan C Nora,"Suppressing simulation bias in multi-modal data using transfer learning", Mach. Learn.: Sci. Technol. 3 015035 (2022).

Baoxiang Pan, Gemma J. Anderson, Andre Goncalves, Donald D. Lucas, Celine J.W. Bonfils, and Jiwoo Lee, “Improving seasonal forecast using probabilistic deep learning,” Journal of Advances in Modeling Earth Systems, 14 (2022).

Baoxiang Pan, Gemma J. Anderson, Andre Goncalves, Donald D. Lucas, Céline J.W. Bonfils, Jiwoo Lee, Yang Tian, and Hsi-Yen Ma, “Learning to correct climate projection biases,” Journal of Advances in Modeling Earth Systems 13, 10 (2021).

Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson et al. “The data-driven future of high-energy-density physics,” Nature 593, 351–361 (2021).

Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, and Brian Spears, “Designing accurate emulators for scientific processes using calibration-driven deep models,” Nat Commun 11, 5622 (2020).

Bogdan Kustowski, Jim A Gaffney, Brian K Spears, Gemma J Anderson, Jayaraman J Thiagarajan, and Rushil Anirudh, “Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion,” IEEE Transactions on Plasma Science, 48(1), 46–53 (2020).

Gemma J. Anderson and Donald D Lucas, “Machine Learning Predictions of a Multiresolution Climate Model Ensemble,” Geophysical Research Letters 45.9 (2018).

Gemma J. Anderson, Donald D. Lucas, Céline Bonfils, “Uncertainty analysis of simulations of the turn-of-the-century drought in the western United States,” Journal of Geophysical Research: Atmospheres, 123, 1–19 (2018).

Céline Bonfils, Gemma Anderson, Benjamin D Santer, Thomas J Phillips, Karl E Taylor, Matthias Cuntz, Mark D Zelinka, Kate Marvel, Benjamin I Cook, Ivana Cvijanovic, and Paul J Durack, “Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity,” Journal of Climate 30.17 (2017).

Donough Regan, Gemma J. Anderson, Matthew Hull, and David Seery, “Constraining Galileon Inflation,” Journal of Cosmology and Astroparticle Physics 02.015 (2015).

Gemma J. Anderson, Donough Regan, and David Seery, “Optimal bispectrum constraints on single-field models of inflation,” Journal of Cosmology and Astroparticle Physics 07.017 (2014).

Gemma J. Anderson, David J Mulryne, and David Seery, “Transport equations for the inflationary trispectrum,” Journal of Cosmology and Astroparticle Physics 10.019 (2012).