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Journal Article
C. A. Hass, Genz, F., Kustusch, M. Bridget, Ouime, P. - P. A., Pomian, K., Sayre, E. C., and Zwolak, J. P., Studying community development: a network analytical approach, 2018.
J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., Ray-based classification framework for high-dimensional data, 2020.
J. P. Zwolak, Kalantre, S. S., Wu, X., Ragole, S., and Taylor, J. M., QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments, PLOS ONE, vol. 13, no. 10, p. e0205844, 2018.
R. Dou and Zwolak, J. P., Practitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting, 2018.
S. Guo, Fritsch, A. R., Greenberg, C., Spielman, I. B., and Zwolak, J. P., Machine-learning enhanced dark soliton detection in Bose-Einstein condensates, 2021.
S. S. Kalantre, Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., and Taylor, J. M., Machine Learning techniques for state recognition and auto-tuning in quantum dots, 2017.
J. P. Zwolak, McJunkin, T., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., Lagally, M. G., Coppersmith, S. N., Eriksson, M. A., and Taylor, J. M., Auto-tuning of double dot devices in situ with machine learning, Phys. Rev. Applied , vol. 13, no. 034075 , 2020.