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

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

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

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

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