I am a Postdoctoral Research Fellow in the W. W. Hansen Experimental Physics Laboratory at Stanford University, and a Mentor at the NASA Frontier Development Lab.

Prior to coming to Stanford, I completed my PhD at the University of Glasgow under the supervision of Dr Iain G. Hannah. My PhD research concentrated on one of the unsolved problems in Heliophysics—the coronal heating problem. During my PhD I gained expertise in numerous time-series analysis techniques and methods for recovering the differential emission measure (an ill-posed inverse problem) from a wide range of spectroscopic and narrowband data. I am a member of the NuSTAR heliophysics working group and I led the analysis of the first solar flare observed by the NuSTAR hard X-ray astrophysics imaging spectrometer. I have also developed a stellar flare detection algorithm based on the observations obtained by the Kepler space telescope to determine the superflare rate of the Sun.

Professional Education

  • Doctor of Philosophy, University of Glasgow, Physics (2019)
  • Master of Physics, University of Southampton, Physics with Astrophysics with a Year Abroad (2014)

Stanford Advisors

All Publications

  • A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES Galvez, R., Fouhey, D. F., Jin, M., Szenicer, A., Munoz-Jaramillo, A., Cheung, M. M., Wright, P. J., Bobra, M. G., Liu, Y., Mason, J., Thomas, R. 2019; 242 (1)
  • A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance. Science advances Szenicer, A., Fouhey, D. F., Munoz-Jaramillo, A., Wright, P. J., Thomas, R., Galvez, R., Jin, M., Cheung, M. C. 2019; 5 (10): eaaw6548


    Measurements of the extreme ultraviolet (EUV) solar spectral irradiance (SSI) are essential for understanding drivers of space weather effects, such as radio blackouts, and aerodynamic drag on satellites during periods of enhanced solar activity. In this paper, we show how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA's Solar Dynamics Observatory obtained between 2010 to 2014. We describe a protocol and baselines for measuring the performance of models. Our best performing machine learning (ML) model based on convolutional neural networks (CNNs) outperforms other ML models, and a differential emission measure (DEM) based approach, yielding average relative errors of under 4.6% (maximum error over emission lines) and more typically 1.6% (median). We also provide evidence that the proposed method is solving this mapping in a way that makes physical sense and by paying attention to magnetic structures known to drive EUV SSI variability.

    View details for DOI 10.1126/sciadv.aaw6548

    View details for PubMedID 31616783

    View details for PubMedCentralID PMC6774717

  • Microflare Heating of a Solar Active Region Observed with NuSTAR, Hinode/XRT, and SDO/AIA ASTROPHYSICAL JOURNAL Wright, P. J., Hannah, I. G., Grefenstette, B. W., Glesener, L., Krucker, S., Hudson, H. S., Smith, D. M., Marsh, A. J., White, S. M., Kuhar, M. 2017; 844 (2)
  • First &ITNuSTAR&IT Limits on Quiet Sun Hard X-Ray Transient Events ASTROPHYSICAL JOURNAL Marsh, A. J., Smith, D. M., Glesener, L., Hannah, I. G., Grefenstette, B. W., Caspi, A., Knicker, S., Hudson, H. S., Madsen, K. K., White, S. M., Kuhar, M., Wright, P. J., Boggs, S. E., Christensen, F. E., Craig, W. W., Hailey, C. J., Harrison, F. A., Stern, D., Zhang, W. W. 2017; 849 (2)
  • Observations of Reconnection Flows in a Flare on the Solar Disk ASTROPHYSICAL JOURNAL LETTERS Wang, J., Simoes, P. A., Jeffrey, N. S., Fletcher, L., Wright, P. J., Hannah, I. G. 2017; 847 (1)
  • EVIDENCE OF SIGNIFICANT ENERGY INPUT IN THE LATE PHASE OF A SOLAR FLARE FROM NuSTAR X-RAY OBSERVATIONS ASTROPHYSICAL JOURNAL Kuhar, M., Krucker, S., Hannah, I. G., Glesener, L., Saint-Hilaire, P., Grefenstette, B. W., Hudson, H. S., White, S. M., Smith, D. M., Marsh, A. J., Wright, P. J., Boggs, S. E., Christensen, F. E., Craig, W. W., Hailey, C. J., Harrison, F. A., Stern, D., Zhang, W. W. 2017; 835 (1)