I’m a Ph.D. candidate at University of Illinois Urbana-Champaign, pursuing a Ph.D. in Physics with a Computational Science and Engineering concentration.
My main research interests involve the structure-property relationship of single plasmonic nanoparticles by developing machine learning/deep learning models and finite-different time-domain (FDTD) simulations.
Before UIUC, I received a B.S. degree in Physics from Ritsumeikan University and an M.S. degree in Applied Physics from Rice University.
Machine Learning Applications in Single Plasmonic Particles
[1] Shiratori, K.; Bishop, L. D. C.; Ostovar, B.; Baiyasi, R.; Cai, Y.-Y.; Rossky, P. J.; Landes, C. F.; Link, S., Machine-Learned Decision Trees for Predicting Gold Nanorod Sizes from Spectra. Journal of Physical Chemistry C 2021,125 (35), 19353-19361.
[2] Shiratori, K.; West, C. A.; Jia, Z.; Lee, S. A.; Cook, E. A.; Murphy, C. J.; Landes, C. F.; Link, S., Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates. Submitted.
Mass Electromagnetic Simulations to Explain Experiments
Utilizing High-Performance Computing, I did mass simulations for the optical properties of single plasmonic nanoparticles with slightly different geometry and conditions to explain the experimental data.
Web Apps for Nanoscience
Taking advantage of an open-source Python library streamlit, I launched web applications for nanoscientists: AuNRs size prediction, Unit conversion, Mie calculation, Scale bar plot in electron microscopic images, and more.
