S. Ragole

Zwolak, J. P., Kalantre, S. S., . Y. Wu, X., Ragole, S., & Taylor, J. M. (2018). QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. Plos One, 13. http://doi.org/10.1371/journal.pone.0205844 (Original work published October 2018)

Xu, H., Kemiktarak, U., Fan, J., Ragole, S., Lawall, J., & Taylor, J. M. (2017). Observation of optomechanical buckling transitions. Nature Communications, 8. http://doi.org/10.1038/ncomms14481 (Original work published March 2017)

Xu, H. T., Kemiktarak, U., . Y. Fan, J., Ragole, S., Lawall, J., & Taylor, J. M. (2016). Symmetry breaking in membrane optomechanics. In .

Ragole, S., & Taylor, J. M. (2016). Interacting Atomic Interferometry for Rotation Sensing Approaching the Heisenberg Limit. Physical Review Letters, 117. http://doi.org/10.1103/PhysRevLett.117.203002 (Original work published November 2016)

Ragole, S., Xu, H. T., Lawall, J., & Taylor, J. M. (2017). Thermodynamic limits for optomechanical systems with conservative potentials. Physical Review B, 96. http://doi.org/10.1103/PhysRevB.96.184106 (Original work published November 2017)

Perez-Rios, J., Ragole, S., Wang, J., & Greene, C. H. (2014). Comparison of classical and quantal calculations of helium three-body recombination. Journal of Chemical Physics, 140. http://doi.org/10.1063/1.4861851

Kalantre, S. S., Zwolak, J. P., Ragole, S., . Y. Wu, X., Zimmerman, N. M., Stewart, M. D., & Taylor, J. M. (2019). Machine learning techniques for state recognition and auto-tuning in quantum dots. Npj Quantum Information, 5. http://doi.org/10.1038/s41534-018-0118-7 (Original work published January 2019)