Skip to content

Graduate student Jacqueline Alvarez defends PhD dissertation

December 11, 2025

Graduate student, Jacqueline (Jacky) Alvarez, successfully defended her PhD dissertation, titled "Data-Efficient Learning and Optimization Methods for Image Processing," on December 11, 2025.  Jacky is advised by Prof. Roummel Marcia.  Her committee members include Prof. Chrysoula Tsogka (Applied Mathematics), Prof. Arnold Kim (Applied Mathematics), and Dr. Omar DeGuchy (Lawrence Livermore National Laboratory).

Jacky received her B.S. in Mathematics from Cal Poly Pomona.  Jacky interned at the Autonomy Technology Research at Wright State University under the supversion of Dr. Sean O'Rourke and Dr. Breton Minnehan from the Air Force Research Laboratory and at Lawrence Livermore National Laboratory under the supervision of Brian Gallagher and Keith Henderson.  Jacky presented her research at the 2025 SIAM Conference on Computational Science and Engineering in Fort Worth, TX; the 2024 Research Symposium in Pittsburgh, PA; and the 2022 SPIE Optics + Photonics Conference in San Diego, CA.  Jacky received an "Outstanding Poster Award" at the 1st SIAM Northern and Central California Section Annual Meeting.  She served as the President of the UC Merced SIAM Student Chapter from 2021-23.  Jacky is scheduled to join the National Security Agency in March, 2026.

Congratulations, Dr. Alvarez!

Dr. Alvarez's selected publications:

  • J. Alvarez, K. Henderson, B. Gallagher M. Aufderheide, R. F. Marcia, and M. Jiang (2025). “Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing”. In: Research in Nondestructive Evaluation 36(4), pp. 159–176. doi: 10.1080/09349847.2025.2519070

  • J. Alvarez, K. Henderson, M. Aufderheide, B. Gallagher, R.F. Marcia, and M. Jiang (2024). “Overcoming Data Sparsity to Enable Deep Learning for Radiographic Non-Destructive Testing”. In: ASNT Research Symposium 2024. doi: 10.32548/RS.2024.003.

  • R.E. Garcia, J. Alvarez, and R.F. Marcia (2022). “Machine Learning for Classifying Images with Motion Blur”. In: 2022 21st IEEE International Conference on Machine Learning and Applications. IEEE, pp. 490–494. doi: 10.1109/ICMLA55696.2022.00079.

  • B. Ilan, A. Ranganath, J. Alvarez, S. Khatri, and R.F. Marcia (2022). “Interpretability of ReLU for Inversion”. In: 2022 21st IEEE International Conference on Machine Learning and Applications. IEEE, pp. 1190–1195. doi: 10.1109/ICMLA55696.2022.00192.

  • J. Alvarez, A.D. Kim, R.F. Marcia, and C. Tsogka (2022). “Synthetic aperture radar inverse scattering reconstruction using convolutional neural networks”. In: Applications of Machine Learning 2022. Vol. 12227. SPIE, p. 1222703. doi: 10.1117/12.2633577.

  • Ho, A., J. Alvarez, and R.F. Marcia (2021). “Convolution Padding in Recurrent Neural Networks for Image Denoising with Limited Data”. In: 2021 55th Asilomar Conference on Signals, Systems, and Computers, pp. 1699–1703. doi: 10.1109/IEEECONF53345.2021.9723313.

  • DeGuchy, O., J. Alvarez and A.D. Kim, R.F. Marcia, and C. Tsogka (2020). “Machine learning for forward and inverse scattering in synthetic aperture radar”. In: Applications of Machine Learning 2020. Vol. 11511. International Society for Optics and Photonics. SPIE, 115110S. doi: 10.1117/12.2568302

  • J. Alvarez, O. DeGuchy, and R.F. Marcia (2020). “Image Classification in Synthetic Aperture Radar Using Reconstruction from Learned Inverse Scattering”. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 2867–2870. doi: 10.1109/IGARSS39084.2020.9323529.