M. Fazel

Building Fluorescence Lifetime Maps Photon-by-photon by Leveraging Spatial Correlations

This paper introduces a doubly nonparametric framework for FLIM that learns fluorescent species and absolute lifetime maps while addressing spatial correlations and low photon counts.

Fluorescence Lifetime: Beating the IRF and interpulse window

This paper introduces BNP-LA, a Bayesian nonparametric method for analyzing fluorescence lifetime data with arbitrary lifetimes, and demonstrates its effectiveness using synthetic and experimental data.

Single Photon smFRET. II. Application to Continuous Illumination

The paper applies a Bayesian nonparametric framework to analyze smFRET data under continuous illumination, learning system states and kinetics from synthetic and experimental datasets.

Single Photon smFRET. III. Application to Pulsed Illumination

This paper applies a Bayesian nonparametric framework to analyze single photon, single molecule FRET data under pulsed illumination, addressing uncertainties and noise sources in Holliday junction experiments.

Single Photon smFRET. I. Theory and Conceptual Basis

This paper introduces a Bayesian nonparametric framework and software for analyzing single photon smFRET data, addressing unknown system states and noise sources.

Robust, Fiducial-Free Drift Correction for Super-resolution Imaging

This paper introduces a drift correction method for super-resolution imaging that uses brightfield images and post-processing to correct drift without fiducials or specialized hardware.

Comparing Lifeact and Phalloidin for Super-resolution Imaging of Actin in Fixed Cells

This study compares Lifeact and Phalloidin for super-resolution imaging of actin in fixed cells, finding Lifeact offers comparable resolution with additional benefits.

Sub-Nanometer Precision using Bayesian Grouping of Localizations

The paper introduces a Bayesian method to improve localization precision in single-molecule microscopy by grouping localizations from multiple blinking events.

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

The paper introduces a Bayesian method using Reversible Jump Markov Chain Monte Carlo to fit multiple emitters in dense super-resolution imaging data, providing posterior distributions and model coordinates.