I. Sgouralis

An accurate probabilistic step finder for time-series analysis

This paper introduces BNP-Step, a Bayesian nonparametric method for step-finding in noisy time-series data, which accurately identifies transitions and emission levels without assuming geometric holding times or using ad hoc penalties.

BNP-Track: A framework for superresolved tracking

BNP-Track extends superresolution to track multiple emitters dynamically without relying on photodynamics, achieving sub-50 nm localization accuracy in both in-cell and synthetic data.

BNP-Track: A framework for multi-particle superresolved tracking

BNP-Track is a framework for superresolved tracking of fluorescent molecules that uses Bayesian nonparametrics to infer emitter numbers and tracks, outperforming conventional methods in crowded environments.

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.

A computational proposal for tracking multiple molecules in a multi-focus confocal setup

This paper proposes a computational method to track multiple fluorescent molecules in a multi-focus confocal setup, enabling 3D high-speed tracking below the diffraction limit.

Direct Photon-by-photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics

This paper introduces a Bayesian nonparametric method to directly infer the number of chemical species and their lifetimes from photon arrival data without pre-specifying the number of species.

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

This paper introduces a Bayesian nonparametric method to count fluorophores and track their photophysical states from time traces, applicable to both fluorescent and photoactivated localization microscopy.

A Continuous Time Representation of smFRET for the Extraction of Rapid Kinetics

This paper develops a continuous-time hidden Markov jump process to learn kinetic rates from smFRET data beyond acquisition rates, validated on simulated and experimental data.

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

This paper generalizes hidden Markov models to continuous time for analyzing single molecule kinetics, introducing hidden Markov jump processes to handle fast transitions without discrete time assumptions.

Residence Time Analysis of RNA Polymerase Transcription Dynamics: A Bayesian Sticky HMM Approach

This paper introduces a Bayesian sticky HMM approach to analyze RNA polymerase residence times without distinguishing between long and short events, revealing force-dependent dynamics and limited effects of GreB.

Pitching single focus confocal data analysis one photon at a time with Bayesian nonparametrics

This paper introduces a Bayesian nonparametric method to analyze single-focus confocal data by directly processing photon arrival times, enabling faster and more efficient molecular dynamics analysis than traditional methods.

An Alternative Framework for Fluorescence Correlation Spectroscopy

This paper introduces a new analysis method for fluorescence correlation spectroscopy that enables faster and less phototoxic analysis of single molecule fluorescence confocal microscopy data.