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Statistical Machine Learning
New experiments frequently mean entirely new statistical data reduction strategies are needed. We develop these to analyze our single-molecule and microscopy image data, frequently drawing on concepts from statistics and information theory.
Photon-by-Photon Analysis of Single-Molecule Fluorescence
On the single-molecule side, photon-by-photon single-molecule analysis is a practice developed by our group that, experimentally, records the arrival time of each detected photon and, theoretically, analyzes the photon time series using data-driven and model-free methods that are statistically robust. The change-point analysis is one such method developed by our lab. Further developments have extended this using Bayesian-inference based methods.
Links: https://doi.org/10.1021/jp0467548 https://doi.org/10.1021/jp062024j https://doi.org/10.1021/acs.jpca.7b04378 https://doi.org/10.1016/j.bpj.2017.11.008 https://doi.org/10.1021/acs.jpcb.8b10561
Super Line Localization
Much like super-resolution microscopy enables sub-diffraction limited resolution of single points, super-localization of line structures in noisy microscope images, which we have developed, enables the resolution of sub-diffraction limited line structures such as microtubules in cells.
Links: https://doi.org/10.1016/j.bpj.2020.04.009 https://doi.org/10.1117/12.2567752